Publications

This study reveals the relation between surprisal, phonetic distance, and latency based on a multilingual, short-term priming framework. Four Slavic languages (Bulgarian, Czech, Polish, and Russian) are investigated across two priming conditions: associative and phonetic priming, involving true cognates and near-homophones, respectively. This research is grounded in the methodology of information theory and proposes new methods for quantifying differences between meaningful lexical primes and targets for closely related languages. It also outlines the influence of phonetic distance between cognate and noncognate pairs of primes and targets on response times in a cross-lingual lexical decision task. The experimental results show that phonetic distance moderates response times only in Polish and Czech, whereas the surprisal-based correspondence effect is an accurate predictor of latency for all tested languages. The information-theoretic approach of quantifying feature-based alternations between Slavic cognates and near-homophones appears to be a valid method for latency moderation in the auditory modality. The outcomes of this study suggest that the surprisal-based (un)expectedness of spoken stimuli is an accurate predictor of human performance in multilingual lexical decision tasks.

• https://www.isca-speech.org/archive/interspeech_2021/kudera21_interspeech.html
• https://doi.org/10.21437/Interspeech.2021-1003

@inproceedings{kudera21_interspeech,
title = {Phonetic Distance and Surprisal in Multilingual Priming: Evidence from Slavic},
author = {Jacek Kudera and Philip Georgis and Bernd M{\"o}bius and Tania Avgustinova and Dietrich Klakow},
url = {https://www.isca-speech.org/archive/interspeech_2021/kudera21_interspeech.html},
doi = {https://doi.org/10.21437/Interspeech.2021-1003},
year = {2021},
date = {2021},
booktitle = {Proc. Interspeech},
pages = {3944-3948},
abstract = {This study reveals the relation between surprisal, phonetic distance, and latency based on a multilingual, short-term priming framework. Four Slavic languages (Bulgarian, Czech, Polish, and Russian) are investigated across two priming conditions: associative and phonetic priming, involving true cognates and near-homophones, respectively. This research is grounded in the methodology of information theory and proposes new methods for quantifying differences between meaningful lexical primes and targets for closely related languages. It also outlines the influence of phonetic distance between cognate and noncognate pairs of primes and targets on response times in a cross-lingual lexical decision task. The experimental results show that phonetic distance moderates response times only in Polish and Czech, whereas the surprisal-based correspondence effect is an accurate predictor of latency for all tested languages. The information-theoretic approach of quantifying feature-based alternations between Slavic cognates and near-homophones appears to be a valid method for latency moderation in the auditory modality. The outcomes of this study suggest that the surprisal-based (un)expectedness of spoken stimuli is an accurate predictor of human performance in multilingual lexical decision tasks.},
pubstate = {published},
type = {inproceedings}
}

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How do neural networks “perceive” speech sounds from unknown languages? Does the typological similarity between the model’s training language (L1) and an unknown language (L2) have an impact on the model representations of L2 speech signals? To answer these questions, we present a novel experimental design based on representational similarity analysis (RSA) to analyze acoustic word embeddings (AWEs)—vector representations of variable-duration spoken-word segments. First, we train monolingual AWE models on seven Indo-European languages with various degrees of typological similarity. We then employ RSA to quantify the cross-lingual similarity by simulating native and non-native spoken-word processing using AWEs. Our experiments show that typological similarity indeed affects the representational similarity of the models in our study. We further discuss the implications of our work on modeling speech processing and language similarity with neural networks.

• https://aclanthology.org/2021.blackboxnlp-1.32/
• https://doi.org/10.18653/v1/2021.blackboxnlp-1.32

@inproceedings{abdullah-etal-2021-familiar,
title = {How Familiar Does That Sound? Cross-Lingual Representational Similarity Analysis of Acoustic Word Embeddings},
author = {Badr M. Abdullah and Iuliia Zaitova and Tania Avgustinova and Bernd M{\"o}bius and Dietrich Klakow},
url = {https://aclanthology.org/2021.blackboxnlp-1.32/},
doi = {https://doi.org/10.18653/v1/2021.blackboxnlp-1.32},
year = {2021},
date = {2021},
booktitle = {Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP},
pages = {407-419},
publisher = {Association for Computational Linguistics},
abstract = {How do neural networks “perceive” speech sounds from unknown languages? Does the typological similarity between the model’s training language (L1) and an unknown language (L2) have an impact on the model representations of L2 speech signals? To answer these questions, we present a novel experimental design based on representational similarity analysis (RSA) to analyze acoustic word embeddings (AWEs)—vector representations of variable-duration spoken-word segments. First, we train monolingual AWE models on seven Indo-European languages with various degrees of typological similarity. We then employ RSA to quantify the cross-lingual similarity by simulating native and non-native spoken-word processing using AWEs. Our experiments show that typological similarity indeed affects the representational similarity of the models in our study. We further discuss the implications of our work on modeling speech processing and language similarity with neural networks.},
pubstate = {published},
type = {inproceedings}
}

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Transformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on. We evaluate three models (BERT, RoBERTa, and ALBERT), testing their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks. We focus on relative clauses (in American English) as a complex phenomenon needing contextual information and antecedent identification to be resolved. Based on a naturalistic dataset, probing shows that all three models indeed capture linguistic knowledge about grammaticality, achieving high performance. Evaluation on diagnostic cases and masked prediction tasks considering fine-grained linguistic knowledge, however, shows pronounced model-specific weaknesses especially on semantic knowledge, strongly impacting models’ performance. Our results highlight the importance of (a) model comparison in evaluation task and (b) building up claims of model performance and the linguistic knowledge they capture beyond purely probing-based evaluations.

• https://aclanthology.org/2020.coling-main.67/

@inproceedings{Mosbach2020,
title = {A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English},
author = {Marius Mosbach and Stefania Degaetano-Ortlieb and Marie-Pauline Krielke and Badr M. Abdullah and Dietrich Klakow},
url = {https://aclanthology.org/2020.coling-main.67/},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
pages = {771-787},
abstract = {Transformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on. We evaluate three models (BERT, RoBERTa, and ALBERT), testing their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks. We focus on relative clauses (in American English) as a complex phenomenon needing contextual information and antecedent identification to be resolved. Based on a naturalistic dataset, probing shows that all three models indeed capture linguistic knowledge about grammaticality, achieving high performance. Evaluation on diagnostic cases and masked prediction tasks considering fine-grained linguistic knowledge, however, shows pronounced model-specific weaknesses especially on semantic knowledge, strongly impacting models’ performance. Our results highlight the importance of (a) model comparison in evaluation task and (b) building up claims of model performance and the linguistic knowledge they capture beyond purely probing-based evaluations.},
pubstate = {published},
type = {inproceedings}
}

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In a web-based experiment, Bulgarian audio stimuli in the form of recorded isolated words are presented to Russian native speakers who are required to write a suitable Russian translation. The degree of intelligibility, as revealed by the cognate guessing task, is relatively high for this pair of languages. We correlate the obtained intercomprehension scores with established linguistic factors in order to determine their influence on the cross-linguistic spoken word recognition. A detailed error analysis focuses on sound correspondences that cause translation problems in such an intercomprehension scenario.

• PROCEEDINGS_IBLCONF2020_vol2     (5.35MB)

@inproceedings{Stenger2020b,
title = {How intelligible is spoken Bulgarian for Russian native speakers in an intercomprehension scenario?},
author = {Irina Stenger and Tania Avgustinova},
editor = {Vanya Micheva et al.},
year = {2020},
date = {2020},
booktitle = {Proceedings of the International Annual Conference of the Institute for Bulgarian Language},
pages = {142-151},
address = {Sofia, Bulgaria},
abstract = {In a web-based experiment, Bulgarian audio stimuli in the form of recorded isolated words are presented to Russian native speakers who are required to write a suitable Russian translation. The degree of intelligibility, as revealed by the cognate guessing task, is relatively high for this pair of languages. We correlate the obtained intercomprehension scores with established linguistic factors in order to determine their influence on the cross-linguistic spoken word recognition. A detailed error analysis focuses on sound correspondences that cause translation problems in such an intercomprehension scenario.},
pubstate = {published},
type = {inproceedings}
}

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Computational modelling of the observed mutual intelligibility of Slavic languages unavoid-ably requires systematic integration of classical Slavistics knowledge from comparative his-torical grammar and traditional contrastive description of language pairs. The phenomenon of intercomprehension is quite intuitive: speakers of a given language L1 understand another closely related language (variety) L2 without being able to use the latter productively, i.e. for speaking or writing.

This specific mode of using the human linguistic competence manifests itself as receptive multilingualism. The degree of mutual understanding of genetically close-ly related languages, such as Bulgarian and Russian, corresponds to objectively measurable distances at different linguistic levels. The common Slavic basis and the comparative-syn-chronous perspective allow us to reveal Bulgarian-Russian linguistic affinity with regard to spelling, vocabulary and grammar.

• https://e-knjige.ff.uni-lj.si/znanstvena-zalozba/catalog/view/226/326/5284-1

@inbook{Avgustinova2020,
title = {Russian-Bulgarian mutual intelligibility in light of linguistic and statistical models of Slavic receptive multilingualism [Russko-bolgarskaja vzaimoponjatnost’ v svete lingvisti{\v{c}eskich i statisti{\v{c}eskich modelej slavjanskoj receptivnoj mnogojazy{\v{c}nocsti]},
author = {Tania Avgustinova and Irina Stenger},
editor = {Roland Marti and Patrice Pognan and Mojca Schlamberger Brezar},
url = {https://e-knjige.ff.uni-lj.si/znanstvena-zalozba/catalog/view/226/326/5284-1},
year = {2020},
date = {2020},
pages = {85-99},
publisher = {University Press, Faculty of Arts},
address = {Ljubljana, Slovenia},
abstract = {Computational modelling of the observed mutual intelligibility of Slavic languages unavoid-ably requires systematic integration of classical Slavistics knowledge from comparative his-torical grammar and traditional contrastive description of language pairs. The phenomenon of intercomprehension is quite intuitive: speakers of a given language L1 understand another closely related language (variety) L2 without being able to use the latter productively, i.e. for speaking or writing. This specific mode of using the human linguistic competence manifests itself as receptive multilingualism. The degree of mutual understanding of genetically close-ly related languages, such as Bulgarian and Russian, corresponds to objectively measurable distances at different linguistic levels. The common Slavic basis and the comparative-syn-chronous perspective allow us to reveal Bulgarian-Russian linguistic affinity with regard to spelling, vocabulary and grammar.},
pubstate = {published},
type = {inbook}
}

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This study contributes to a better understanding of receptive multilingualism by determining similarities and differences in successful processing of written and spoken cognate words in an unknown but (closely) related language. We investigate two Slavic languages with regard to their mutual intelligibility. The current focus is on the recognition of isolated Bulgarian words by Russian native speakers in a cognate guessing task, considering both written and audio stimuli.

The experimentally obtained intercomprehension scores show a generally high degree of intelligibility of Bulgarian cognates to Russian subjects, as well as processing difficulties in case of visual vs. auditory perception. In search of an explanation, we examine the linguistic factors that can contribute to various degrees of written and spoken word intelligibility. The intercomprehension scores obtained in the online word translation experiments are correlated with (i) the identical and mismatched correspondences on the orthographic and phonetic level, (ii) the word length of the stimuli, and (iii) the frequency of Russian cognates. Additionally we validate two measuring methods: the Levenshtein distance and the word adaptation surprisal as potential predictors of the word intelligibility in reading and oral intercomprehension.

• http://www.dialog-21.ru/media/4962/stengeriplusavgustinovat-045.pdf

@inproceedings{Stenger2020b,
title = {Visual vs. auditory perception of Bulgarian stimuli by Russian native speakers},
author = {Irina Stenger and Tania Avgustinova},
editor = {Vladimir P. Selegej et al.},
url = {http://www.dialog-21.ru/media/4962/stengeriplusavgustinovat-045.pdf},
year = {2020},
date = {2020},
booktitle = {Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference ‘Dialogue’},
pages = {684 - 695},
abstract = {This study contributes to a better understanding of receptive multilingualism by determining similarities and differences in successful processing of written and spoken cognate words in an unknown but (closely) related language. We investigate two Slavic languages with regard to their mutual intelligibility. The current focus is on the recognition of isolated Bulgarian words by Russian native speakers in a cognate guessing task, considering both written and audio stimuli. The experimentally obtained intercomprehension scores show a generally high degree of intelligibility of Bulgarian cognates to Russian subjects, as well as processing difficulties in case of visual vs. auditory perception. In search of an explanation, we examine the linguistic factors that can contribute to various degrees of written and spoken word intelligibility. The intercomprehension scores obtained in the online word translation experiments are correlated with (i) the identical and mismatched correspondences on the orthographic and phonetic level, (ii) the word length of the stimuli, and (iii) the frequency of Russian cognates. Additionally we validate two measuring methods: the Levenshtein distance and the word adaptation surprisal as potential predictors of the word intelligibility in reading and oral intercomprehension.},
pubstate = {published},
type = {inproceedings}
}

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We report on a web-based resource for conducting intercomprehension experiments with native speakers of Slavic languages and present our methods for measuring linguistic distances and asymmetries in receptive multilingualism. Through a website which serves as a platform for online testing, a large number of participants with different linguistic backgrounds can be targeted. A statistical language model is used to measure information density and to gauge how language users master various degrees of (un)intelligibilty. The key idea is that intercomprehension should be better when the model adapted for understanding the unknown language exhibits relatively low average distance and surprisal. All obtained intelligibility scores together with distance and asymmetry measures for the different language pairs and processing directions are made available as an integrated online resource in the form of a Slavic intercomprehension matrix (SlavMatrix).

• 2020.cllrd-1.6     (0.43MB)
• https://aclanthology.org/2020.cllrd-1.6/
• https://doi.org/10.3726/978-3-653-07147-4

@inproceedings{Stenger2020b,
title = {The INCOMSLAV Platform: Experimental Website with Integrated Methods for Measuring Linguistic Distances and Asymmetries in Receptive Multilingualism},
author = {Tania Avgustinova and Kl{\'a}ra J{\'a}grov{\'a} and Irina Stenger},
editor = {James Fiumara and Christopher Cieri and Mark Liberman and Chris Callison-Burch},
url = {https://aclanthology.org/2020.cllrd-1.6/},
doi = {https://doi.org/10.3726/978-3-653-07147-4},
year = {2020},
date = {2020},
booktitle = {LREC 2020 Workshop Language Resources and Evaluation Conference 11-16 May 2020, Citizen Linguistics in Language Resource Development (CLLRD 2020)},
pages = {483-500},
publisher = {Peter Lang},
abstract = {We report on a web-based resource for conducting intercomprehension experiments with native speakers of Slavic languages and present our methods for measuring linguistic distances and asymmetries in receptive multilingualism. Through a website which serves as a platform for online testing, a large number of participants with different linguistic backgrounds can be targeted. A statistical language model is used to measure information density and to gauge how language users master various degrees of (un)intelligibilty. The key idea is that intercomprehension should be better when the model adapted for understanding the unknown language exhibits relatively low average distance and surprisal. All obtained intelligibility scores together with distance and asymmetry measures for the different language pairs and processing directions are made available as an integrated online resource in the form of a Slavic intercomprehension matrix (SlavMatrix).},
pubstate = {published},
type = {inproceedings}
}

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• https://www.peterlang.com/view/title/19540
• https://doi.org/10.3726/978-3-653-07147-4

@incollection{Stenger2020,
title = {“Reading Polish with Czech Eyes” or “How Russian Can a Bulgarian Text Be?”: Orthographic Differences as an Experimental Variable in Slavic Intercomprehension},
author = {Irina Stenger and Kl{\'a}ra J{\'a}grov{\'a} and Andrea Fischer and Tania Avgustinova},
editor = {Teodora Radeva-Bork and Peter Kosta},
url = {https://www.peterlang.com/view/title/19540},
doi = {https://doi.org/10.3726/978-3-653-07147-4},
year = {2020},
date = {2020},
booktitle = {Current Developments in Slavic Linguistics. Twenty Years After (based on selected papers from FDSL 11)},
pages = {483-500},
publisher = {Peter Lang},
pubstate = {published},
type = {incollection}
}

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A large-scale interdisciplinary research collaboration at Saarland University (Crocker et al. 2016) investigates the hypothesis that language use may be driven by the optimal utilization of the communication channel. The information-theoretic concepts of entropy (Shannon, 1949) and surprisal (Hale 2001; Levy 2008) have gained in popularity due to their potential to predict human linguistic behavior. The underlying assumption is that there is a certain total amount of information contained in a message, which is distributed over the individual units constituting it. Capturing this distribution of information is the goal of surprisal-based modeling with the intention of predicting the processing effort experienced by humans upon encountering these units. The ease of processing linguistic material is thus correlated with its contextually determined predictability, which may be appropriately indexed by Shannon’s notion of information. Multilingualism pervasiveness suggests that human language competence is used quite robustly, taking on various types of information and employing multi-source compensatory and guessing strategies. While it is not realistic to require from every single person to master several languages, it is certainly beneficial to strive and promote a significantly higher degree of receptive skills facilitating the access to other languages. Taking advantage of linguistic similarity – genetic, typological or areal – is the key to acquiring such abilities as efficiently as possible. Awareness that linguistic structures known of a specific language apply to other varieties in which similar phenomena are detectable is indeed essential

• Avgustinova2020.Festschrift     (0.33MB)
• https://www.coli.uni-saarland.de/~tania/ta-pub/Avgustinova2020.Festschrift.pdf

@inbook{TAfestGA,
title = {Surprisal in Intercomprehension},
author = {Tania Avgustinova},
editor = {Milena Slavcheva and Kiril Simov and Petya Osenova and Svetla Boytcheva},
url = {https://www.coli.uni-saarland.de/~tania/ta-pub/Avgustinova2020.Festschrift.pdf},
year = {2020},
date = {2020},
booktitle = {Knowledge, Language, Models},
isbn = {978-954-452-062-5},
pages = {6-19},
publisher = {INCOMA Ltd.},
address = {Shoumen, Bulgaria},
abstract = {A large-scale interdisciplinary research collaboration at Saarland University (Crocker et al. 2016) investigates the hypothesis that language use may be driven by the optimal utilization of the communication channel. The information-theoretic concepts of entropy (Shannon, 1949) and surprisal (Hale 2001; Levy 2008) have gained in popularity due to their potential to predict human linguistic behavior. The underlying assumption is that there is a certain total amount of information contained in a message, which is distributed over the individual units constituting it. Capturing this distribution of information is the goal of surprisal-based modeling with the intention of predicting the processing effort experienced by humans upon encountering these units. The ease of processing linguistic material is thus correlated with its contextually determined predictability, which may be appropriately indexed by Shannon’s notion of information. Multilingualism pervasiveness suggests that human language competence is used quite robustly, taking on various types of information and employing multi-source compensatory and guessing strategies. While it is not realistic to require from every single person to master several languages, it is certainly beneficial to strive and promote a significantly higher degree of receptive skills facilitating the access to other languages. Taking advantage of linguistic similarity – genetic, typological or areal – is the key to acquiring such abilities as efficiently as possible. Awareness that linguistic structures known of a specific language apply to other varieties in which similar phenomena are detectable is indeed essential},
pubstate = {published},
type = {inbook}
}

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State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between distant languages but also between closely-related languages or even different spoken varieties of the same language. However, it is still unclear to what extent neural LID models generalize to speech samples with different acoustic conditions due to domain shift. In this paper, we present a set of experiments to investigate the impact of domain mismatch on the performance of neural LID systems for a subset of six Slavic languages across two domains (read speech and radio broadcast) and examine two low-level signal descriptors (spectral and cepstral features) for this task. Our experiments show that (1) out-of-domain speech samples severely hinder the performance of neural LID models, and (2) while both spectral and cepstral features show comparable performance within-domain, spectral features show more robustness under domain mismatch. Moreover, we apply unsupervised domain adaptation to minimize the discrepancy between the two domains in our study. We achieve relative accuracy improvements that range from 9% to 77% depending on the diversity of acoustic conditions in the source domain.

• https://arxiv.org/abs/2008.00545
• https://doi.org/10.21437/Interspeech.2020-2930

@inproceedings{abdullah_etal_is2020,
title = {Cross-Domain Adaptation of Spoken Language Identification for Related Languages: The Curious Case of Slavic Languages},
author = {Badr M. Abdullah and Tania Avgustinova and Bernd M{\"o}bius and Dietrich Klakow},
url = {https://arxiv.org/abs/2008.00545},
doi = {https://doi.org/10.21437/Interspeech.2020-2930},
year = {2020},
date = {2020},
booktitle = {Proceedings of Interspeech 2020},
pages = {477-481},
abstract = {State-of-the-art spoken language identification (LID) systems, which are based on end-to-end deep neural networks, have shown remarkable success not only in discriminating between distant languages but also between closely-related languages or even different spoken varieties of the same language. However, it is still unclear to what extent neural LID models generalize to speech samples with different acoustic conditions due to domain shift. In this paper, we present a set of experiments to investigate the impact of domain mismatch on the performance of neural LID systems for a subset of six Slavic languages across two domains (read speech and radio broadcast) and examine two low-level signal descriptors (spectral and cepstral features) for this task. Our experiments show that (1) out-of-domain speech samples severely hinder the performance of neural LID models, and (2) while both spectral and cepstral features show comparable performance within-domain, spectral features show more robustness under domain mismatch. Moreover, we apply unsupervised domain adaptation to minimize the discrepancy between the two domains in our study. We achieve relative accuracy improvements that range from 9% to 77% depending on the diversity of acoustic conditions in the source domain.},
pubstate = {published},
type = {inproceedings}
}

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Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification (LID). In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness or non-linguists’ perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability to be the best predictor of the language representation similarity.

• https://www.aclweb.org/anthology/2020.vardial-1.12

@inproceedings{abdullah_etal_vardial2020,
title = {Rediscovering the Slavic Continuum in Representations Emerging from Neural Models of Spoken Language Identification},
author = {Badr M. Abdullah and Jacek Kudera and Tania Avgustinova and Bernd M{\"o}bius and Dietrich Klakow},
url = {https://www.aclweb.org/anthology/2020.vardial-1.12},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2020)},
pages = {128-139},
publisher = {International Committee on Computational Linguistics (ICCL)},
address = {Barcelona, Spain (Online)},
abstract = {Deep neural networks have been employed for various spoken language recognition tasks, including tasks that are multilingual by definition such as spoken language identification (LID). In this paper, we present a neural model for Slavic language identification in speech signals and analyze its emergent representations to investigate whether they reflect objective measures of language relatedness or non-linguists’ perception of language similarity. While our analysis shows that the language representation space indeed captures language relatedness to a great extent, we find perceptual confusability to be the best predictor of the language representation similarity.},
pubstate = {published},
type = {inproceedings}
}

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Within the first shared task on machine translation between similar languages, we present our first attempts on Czech to Polish machine translation from an intercomprehension perspective. We propose methods based on the mutual intelligibility of the two languages, taking advantage of their orthographic and phonological similarity, in the hope to improve over our baselines. The translation results are evaluated using BLEU. On this metric, none of our proposals could outperform the baselines on the final test set. The current setups are rather preliminary, and there are several potential improvements we can try in the future.

• https://aclanthology.org/W19-5425
• https://doi.org/10.18653/v1/W19-5425

@inproceedings{csplMT,
title = {Machine Translation from an Intercomprehension Perspective},
author = {Yu Chen and Tania Avgustinova},
url = {https://aclanthology.org/W19-5425},
doi = {https://doi.org/10.18653/v1/W19-5425},
year = {2019},
date = {2019},
booktitle = {Proc. Fourth Conference on Machine Translation (WMT), Volume 3: Shared Task Papers},
pages = {192-196},
address = {Florence, Italy},
abstract = {Within the first shared task on machine translation between similar languages, we present our first attempts on Czech to Polish machine translation from an intercomprehension perspective. We propose methods based on the mutual intelligibility of the two languages, taking advantage of their orthographic and phonological similarity, in the hope to improve over our baselines. The translation results are evaluated using BLEU. On this metric, none of our proposals could outperform the baselines on the final test set. The current setups are rather preliminary, and there are several potential improvements we can try in the future.},
pubstate = {published},
type = {inproceedings}
}

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In CHAPTER I, I first introduce the thesis in the context of the project workflow in section 1. I then summarise the methods and findings from the project publications about the languages in focus. There I also introduce the relevant concepts and terminology viewed in the literature as possible predictors of intercomprehension and processing difficulty. CHAPTER II presents a quantitative (section 4) and a qualitative (section 5) analysis of the results of the cooperative translation experiments. The focus of this thesis – the language pair PL-CS – is explained and the hypotheses are introduced in section 6. The experiment website is introduced in section 7 with an overview over participants, the different experiments conducted and in which section they are discussed. In CHAPTER IV, free translation experiments are discussed in which two different sets of individual word stimuli were presented to Czech readers: (i) Cognates that are transformable with regular PL-CS correspondences (section 12) and (ii) the 100 most frequent PL nouns (section 13). CHAPTER V presents the findings of experiments in which PL NPs in two different linearisation conditions were presented to Czech readers (section 14.1-14.6). A short digression is made when I turn to experiments with PL internationalisms which were presented to German readers (14.7). CHAPTER VI discusses the methods and results of cloze translation experiments with highly predictable target words in sentential context (section 15) and random context with sentences from the cooperative translation experiments (section 16). A final synthesis of the findings, together with an outlook, is provided in CHAPTER VII.

---

In KAPITEL I stelle ich zunächst die These im Kontext des Projektablaufs in Abschnitt 1 vor. Anschließend fasse ich die Methoden und Erkenntnisse aus den Projektpublikationen zu den untersuchten Sprachen zusammen. Dort stelle ich auch die relevanten Konzepte und die Terminologie vor, die in der Literatur als mögliche Prädiktoren für Interkomprehension und Verarbeitungsschwierigkeiten angesehen werden. KAPITEL II enthält eine quantitative (Abschnitt 4) und eine qualitative (Abschnitt 5) Analyse der Ergebnisse der kooperativen Übersetzungsexperimente. Der Fokus dieser Arbeit – das Sprachenpaar PL-CS – wird erläutert und die Hypothesen werden in Abschnitt 6 vorgestellt. Die Experiment-Website wird in Abschnitt 7 mit einer Übersicht über die Teilnehmer, die verschiedenen durchgeführten Experimente und die Abschnitte, in denen sie besprochen werden, vorgestellt. In KAPITEL IV werden Experimente zur freien Übersetzung besprochen, bei denen tschechischen Lesern zwei verschiedene Sätze einzelner Wortstimuli präsentiert wurden: (i) Kognaten, die mit regulären PL-CS-Korrespondenzen umgewandelt werden können (Abschnitt 12) und (ii) die 100 häufigsten PL-Substantive (Abschnitt 13). KAPITEL V stellt die Ergebnisse von Experimenten vor, in denen tschechischen Lesern PL-NP in zwei verschiedenen Linearisierungszuständen präsentiert wurden (Abschnitt 14.1-14.6). Einen kurzen Exkurs mache ich, wenn ich mich den Experimenten mit PL-Internationalismen zuwende, die deutschen Lesern präsentiert wurden (14.7). KAPITEL VI erörtert die Methoden und Ergebnisse von Lückentexten mit hochgradig vorhersehbaren Zielwörtern im Satzkontext (Abschnitt 15) und Zufallskontext mit Sätzen aus den kooperativen Übersetzungsexperimenten (Abschnitt 16). Eine abschließende Synthese der Ergebnisse und ein Ausblick finden sich in KAPITEL VII.

• https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/32995
• https://doi.org/10.22028/D291-32708

@phdthesis{Jagrova_Diss_2019,
title = {Reading Polish with Czech Eyes: Distance and Surprisal in Quantitative, Qualitative, and Error Analyses of Intelligibility},
author = {Kl{\'a}ra J{\'a}grov{\'a}},
url = {https://publikationen.sulb.uni-saarland.de/handle/20.500.11880/32995},
doi = {https://doi.org/10.22028/D291-32708},
year = {2019},
date = {2019},
school = {Saarland University},
address = {Saarbruecken, Germany},
abstract = {In CHAPTER I, I first introduce the thesis in the context of the project workflow in section 1. I then summarise the methods and findings from the project publications about the languages in focus. There I also introduce the relevant concepts and terminology viewed in the literature as possible predictors of intercomprehension and processing difficulty. CHAPTER II presents a quantitative (section 4) and a qualitative (section 5) analysis of the results of the cooperative translation experiments. The focus of this thesis – the language pair PL-CS – is explained and the hypotheses are introduced in section 6. The experiment website is introduced in section 7 with an overview over participants, the different experiments conducted and in which section they are discussed. In CHAPTER IV, free translation experiments are discussed in which two different sets of individual word stimuli were presented to Czech readers: (i) Cognates that are transformable with regular PL-CS correspondences (section 12) and (ii) the 100 most frequent PL nouns (section 13). CHAPTER V presents the findings of experiments in which PL NPs in two different linearisation conditions were presented to Czech readers (section 14.1-14.6). A short digression is made when I turn to experiments with PL internationalisms which were presented to German readers (14.7). CHAPTER VI discusses the methods and results of cloze translation experiments with highly predictable target words in sentential context (section 15) and random context with sentences from the cooperative translation experiments (section 16). A final synthesis of the findings, together with an outlook, is provided in CHAPTER VII.

---

In KAPITEL I stelle ich zun{\"a}chst die These im Kontext des Projektablaufs in Abschnitt 1 vor. Anschlie{\ss}end fasse ich die Methoden und Erkenntnisse aus den Projektpublikationen zu den untersuchten Sprachen zusammen. Dort stelle ich auch die relevanten Konzepte und die Terminologie vor, die in der Literatur als m{\"o}gliche Pr{\"a}diktoren f{\"u}r Interkomprehension und Verarbeitungsschwierigkeiten angesehen werden. KAPITEL II enth{\"a}lt eine quantitative (Abschnitt 4) und eine qualitative (Abschnitt 5) Analyse der Ergebnisse der kooperativen {\"U}bersetzungsexperimente. Der Fokus dieser Arbeit - das Sprachenpaar PL-CS - wird erl{\"a}utert und die Hypothesen werden in Abschnitt 6 vorgestellt. Die Experiment-Website wird in Abschnitt 7 mit einer {\"U}bersicht {\"u}ber die Teilnehmer, die verschiedenen durchgef{\"u}hrten Experimente und die Abschnitte, in denen sie besprochen werden, vorgestellt. In KAPITEL IV werden Experimente zur freien {\"U}bersetzung besprochen, bei denen tschechischen Lesern zwei verschiedene S{\"a}tze einzelner Wortstimuli pr{\"a}sentiert wurden: (i) Kognaten, die mit regul{\"a}ren PL-CS-Korrespondenzen umgewandelt werden k{\"o}nnen (Abschnitt 12) und (ii) die 100 h{\"a}ufigsten PL-Substantive (Abschnitt 13). KAPITEL V stellt die Ergebnisse von Experimenten vor, in denen tschechischen Lesern PL-NP in zwei verschiedenen Linearisierungszust{\"a}nden pr{\"a}sentiert wurden (Abschnitt 14.1-14.6). Einen kurzen Exkurs mache ich, wenn ich mich den Experimenten mit PL-Internationalismen zuwende, die deutschen Lesern pr{\"a}sentiert wurden (14.7). KAPITEL VI er{\"o}rtert die Methoden und Ergebnisse von L{\"u}ckentexten mit hochgradig vorhersehbaren Zielw{\"o}rtern im Satzkontext (Abschnitt 15) und Zufallskontext mit S{\"a}tzen aus den kooperativen {\"U}bersetzungsexperimenten (Abschnitt 16). Eine abschlie{\ss}ende Synthese der Ergebnisse und ein Ausblick finden sich in KAPITEL VII.},
pubstate = {published},
type = {phdthesis}
}

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• popr_www_specjalna_2019.indd_     (1.46MB)

@article{Jágrová2019,
title = {Slavische Interkomprehension im 5-Sprachen-Kurs – Dokumentation eines Semesters},
author = {Kl{\'a}ra J{\'a}grov{\'a} and Irina Stenger and Magdalena Telus},
year = {2019},
date = {2019},
journal = {Polnisch in Deutschland. Zeitschrift der Bundesvereinigung der Polnischlehrkr{\"a}fte. Sondernummer: Emil Krebs und die Mehrsprachigkeit in Europa},
pages = {122–133},
pubstate = {published},
type = {article}
}

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Die slavischen Sprachen stellen einen bedeutenden indogermanischen Sprachzweig dar. Es stellt sich die Frage, inwieweit sich Sprecher verschiedener slavischer Sprachen interkomprehensiv verständigen können. Unter Interkomprehension wird die Kommunikationsfähigkeit von Sprechern verwandter Sprachen verstanden, wobei sich jeder Sprecher seiner Sprache bedient. Die vorliegende Arbeit untersucht die orthographische Verständlichkeit slavischer Sprachen mit kyrillischer Schrift im interkomprehensiven Lesen. Sechs ost- und südslavische Sprachen – Bulgarisch, Makedonisch, Russisch, Serbisch, Ukrainisch und Weißrussisch – werden im Hinblick auf orthographische Ähnlichkeiten und Unterschiede miteinander verglichen und statistisch analysiert. Der Fokus der empirischen Untersuchung liegt auf der Erkennung einzelner Kognaten mit diachronisch motivierten orthographischen Korrespondenzen in ost- und südslavischen Sprachen, ausgehend vom Russischen. Die in dieser Arbeit vorgestellten Methoden und erzielten Ergebnisse stellen einen empirischen Beitrag zur slavischen Interkomprehensionsforschung und Interkomrepehensionsdidaktik dar.

• Zur_Rolle_der_Orthographie_in_der_slavischen_Interkomprehension     (5.71MB)

@phdthesis{Stenger_diss_2019,
title = {Zur Rolle der Orthographie in der slavischen Interkomprehension mit besonderem Fokus auf die kyrillische Schrift},
author = {Irina Stenger},
year = {2019},
date = {2019},
school = {Saarland University},
address = {Saarbr{\"u}cken, Germany},
abstract = {Die slavischen Sprachen stellen einen bedeutenden indogermanischen Sprachzweig dar. Es stellt sich die Frage, inwieweit sich Sprecher verschiedener slavischer Sprachen interkomprehensiv verst{\"a}ndigen k{\"o}nnen. Unter Interkomprehension wird die Kommunikationsf{\"a}higkeit von Sprechern verwandter Sprachen verstanden, wobei sich jeder Sprecher seiner Sprache bedient. Die vorliegende Arbeit untersucht die orthographische Verst{\"a}ndlichkeit slavischer Sprachen mit kyrillischer Schrift im interkomprehensiven Lesen. Sechs ost- und s{\"u}dslavische Sprachen - Bulgarisch, Makedonisch, Russisch, Serbisch, Ukrainisch und Wei{\ss}russisch - werden im Hinblick auf orthographische {\"A}hnlichkeiten und Unterschiede miteinander verglichen und statistisch analysiert. Der Fokus der empirischen Untersuchung liegt auf der Erkennung einzelner Kognaten mit diachronisch motivierten orthographischen Korrespondenzen in ost- und s{\"u}dslavischen Sprachen, ausgehend vom Russischen. Die in dieser Arbeit vorgestellten Methoden und erzielten Ergebnisse stellen einen empirischen Beitrag zur slavischen Interkomprehensionsforschung und Interkomrepehensionsdidaktik dar.},
pubstate = {published},
type = {phdthesis}
}

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• http://www.dialog-21.ru/digest/2019/online/

@inproceedings{Stenger2019,
title = {Interaction of linguistic and socio-cognitive factors in receptive multilingualism [Vzaimodejstvie lingvisti{\v{c}eskich i sociokognitivnych parametrov pri receptivnom mul’tilingvisme]},
author = {Irina Stenger and Tania Avgustinova and Konstantin I. Belousov and Dmitrij A. Baranov and Elena V. Erofeeva},
url = {http://www.dialog-21.ru/digest/2019/online/},
year = {2019},
date = {2019},
booktitle = {25th International Conference on Computational Linguistics and Intellectual Technologies (Dialogue 2019)},
address = {Moscow, Russia},
pubstate = {published},
type = {inproceedings}
}

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Languages may be differently distant from each other and their mutual intelligibility may be asymmetric. In this paper we introduce incom.py, a toolbox for calculating linguistic distances and asymmetries between related languages. incom.py allows linguist experts to quickly and easily perform statistical analyses and compare those with experimental results. We demonstrate the efficacy of incom.py in an incomprehension experiment on two Slavic languages: Bulgarian and Russian. Using incom.py we were able to validate three methods to measure linguistic distances and asymmetries: Levenshtein distance, word adaptation surprisal, and conditional entropy as predictors of success in a reading intercomprehension experiment.

• RANLP094     (0.63MB)
• https://aclanthology.org/R19-1094/
• https://doi.org/10.26615/978-954-452-056-4_094

@inproceedings{Mosbach2019,
title = {incom.py - A Toolbox for Calculating Linguistic Distances and Asymmetries between Related Languages},
author = {Marius Mosbach and Irina Stenger and Tania Avgustinova and Dietrich Klakow},
editor = {Galia Angelova and Ruslan Mitkov and Ivelina Nikolova and Irina Temnikova},
url = {https://aclanthology.org/R19-1094/},
doi = {https://doi.org/10.26615/978-954-452-056-4_094},
year = {2019},
date = {2019},
booktitle = {Proceedings of Recent Advances in Natural Language Processing, RANLP 2019, Varna, Bulgaria, 2-4 September 2019},
pages = {811-819},
address = {Varna, Bulgaria},
abstract = {Languages may be differently distant from each other and their mutual intelligibility may be asymmetric. In this paper we introduce incom.py, a toolbox for calculating linguistic distances and asymmetries between related languages. incom.py allows linguist experts to quickly and easily perform statistical analyses and compare those with experimental results. We demonstrate the efficacy of incom.py in an incomprehension experiment on two Slavic languages: Bulgarian and Russian. Using incom.py we were able to validate three methods to measure linguistic distances and asymmetries: Levenshtein distance, word adaptation surprisal, and conditional entropy as predictors of success in a reading intercomprehension experiment.},
pubstate = {published},
type = {inproceedings}
}

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This contribution outlines an international research effort for creating a typology of syntactic idioms on the borderline of the dictionary and the grammar. Recent studies focusing on the adequate description of such units, especially for modern Russian, have resulted in two types of linguistic resources: a microsyntactic dictionary of Russian, and a microsyntactically annotated corpus of Russian texts. Our goal now is to discover to what extent the findings can be generalized cross-linguistically in order to create analogous multilingual resources. The initial work consists in constructing a typology of relevant phenomena. The empirical base is provided by closely related languages which are mutually intelligible to various degrees. We start by creating an inventory for this typology for four representative Slavic languages: Russian (East Slavic), Bulgarian (South Slavic), Polish and Czech (West Slavic). Our preliminary results show that the aim is attainable and can be of relevance to theoretical, comparative and applied linguistics as well as in NLP tasks.

• https://link.springer.com/chapter/10.1007/978-3-030-30135-4_2

@inproceedings{Avgustinova2019,
title = {Towards a Typology of Microsyntactic Constructions},
author = {Tania Avgustinova and Leonid Iomdin},
editor = {Gloria Corpas-Pastor and Ruslan Mitkov},
url = {https://link.springer.com/chapter/10.1007/978-3-030-30135-4_2},
year = {2019},
date = {2019-09-18},
booktitle = {Computational and Corpus-Based Phraseology},
pages = {15-30},
publisher = {Springer, Cham},
abstract = {This contribution outlines an international research effort for creating a typology of syntactic idioms on the borderline of the dictionary and the grammar. Recent studies focusing on the adequate description of such units, especially for modern Russian, have resulted in two types of linguistic resources: a microsyntactic dictionary of Russian, and a microsyntactically annotated corpus of Russian texts. Our goal now is to discover to what extent the findings can be generalized cross-linguistically in order to create analogous multilingual resources. The initial work consists in constructing a typology of relevant phenomena. The empirical base is provided by closely related languages which are mutually intelligible to various degrees. We start by creating an inventory for this typology for four representative Slavic languages: Russian (East Slavic), Bulgarian (South Slavic), Polish and Czech (West Slavic). Our preliminary results show that the aim is attainable and can be of relevance to theoretical, comparative and applied linguistics as well as in NLP tasks.},
pubstate = {published},
type = {inproceedings}
}

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In monolingual human language processing, the predictability of a word given its surrounding sentential context is crucial. With regard to receptive multilingualism, it is unclear to what extent predictability in context interplays with other linguistic factors in understanding a related but unknown language – a process called intercomprehension. We distinguish two dimensions influencing processing effort during intercomprehension: surprisal in sentential context and linguistic distance.

Based on this hypothesis, we formulate expectations regarding the difficulty of designed experimental stimuli and compare them to the results from think-aloud protocols of experiments in which Czech native speakers decode Polish sentences by agreeing on an appropriate translation. On the one hand, orthographic and lexical distances are reliable predictors of linguistic similarity. On the other hand, we obtain the predictability of words in a sentence with the help of trigram language models.

We find that linguistic distance (encoding similarity) and in-context surprisal (predictability in context) appear to be complementary, with neither factor outweighing the other, and that our distinguishing of these two measurable dimensions is helpful in understanding certain unexpected effects in human behaviour.

• https://www.sciencedirect.com/science/article/pii/S0885230817300451

@article{Jágrová2018b,
title = {Language models, surprisal and fantasy in Slavic intercomprehension},
author = {Kl{\'a}ra J{\'a}grov{\'a} and Tania Avgustinova and Irina Stenger and Andrea Fischer},
url = {https://www.sciencedirect.com/science/article/pii/S0885230817300451},
year = {2018},
date = {2018},
journal = {Computer Speech & Language},
abstract = {In monolingual human language processing, the predictability of a word given its surrounding sentential context is crucial. With regard to receptive multilingualism, it is unclear to what extent predictability in context interplays with other linguistic factors in understanding a related but unknown language – a process called intercomprehension. We distinguish two dimensions influencing processing effort during intercomprehension: surprisal in sentential context and linguistic distance. Based on this hypothesis, we formulate expectations regarding the difficulty of designed experimental stimuli and compare them to the results from think-aloud protocols of experiments in which Czech native speakers decode Polish sentences by agreeing on an appropriate translation. On the one hand, orthographic and lexical distances are reliable predictors of linguistic similarity. On the other hand, we obtain the predictability of words in a sentence with the help of trigram language models. We find that linguistic distance (encoding similarity) and in-context surprisal (predictability in context) appear to be complementary, with neither factor outweighing the other, and that our distinguishing of these two measurable dimensions is helpful in understanding certain unexpected effects in human behaviour.},
pubstate = {published},
type = {article}
}

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• www_gazeta_2017     (1.72MB)

@article{Jágrová2018,
title = {Polski nadal nieskomplikowany? Interkomprehensionsexperimente mit Nominalphrasen},
author = {Kl{\'a}ra J{\'a}grov{\'a} and Irina Stenger and Tania Avgustinova},
year = {2018},
date = {2018},
journal = {Polnisch in Deutschland. Zeitschrift der Bundesvereinigung der Polnischlehrkr{\"a}fte},
pages = {20-37},
volume = {5/2017},
pubstate = {published},
type = {article}
}

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