Publications

Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.

• https://aclanthology.org/Q17-1003
• https://doi.org/10.1162/tacl_a_00044

@article{ashutoshTacl2016,
title = {Modeling Semantic Expectations: Using Script Knowledge for Referent Prediction},
author = {Ashutosh Modi and Ivan Titov and Vera Demberg and Asad Sayeed and Manfred Pinkal},
url = {https://aclanthology.org/Q17-1003},
doi = {https://doi.org/10.1162/tacl_a_00044},
year = {2016},
date = {2016},
journal = {Transactions of the Association for Computational Linguistics},
pages = {31-44},
publisher = {MIT Press},
address = {Cambridge, MA},
abstract = {Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.},
pubstate = {published},
type = {article}
}

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Semantic scripts is a conceptual representation which defines how events are organized into higher level activities. Practically all the previous approaches to inducing script knowledge from text relied on count-based techniques (e.g., generative models) and have not attempted to compositionally model events. In this work, we introduce a neural network model which relies on distributed compositional representations of events. The model captures statistical dependencies between events in a scenario, overcomes some of the shortcomings of previous approaches (e.g., by more effectively dealing with data sparsity) and outperforms count-based counterparts on the narrative cloze task.

• https://www.researchgate.net/publication/306093411_Event_Embeddings_for_Semantic_Script_Modeling

@inproceedings{modi:CONLL2016,
title = {Event Embeddings for Semantic Script Modeling},
author = {Ashutosh Modi},
url = {https://www.researchgate.net/publication/306093411_Event_Embeddings_for_Semantic_Script_Modeling},
year = {2016},
date = {2016-10-17},
booktitle = {Proceedings of the Conference on Computational Natural Language Learning (CoNLL)},
address = {Berlin, Germany},
abstract = {Semantic scripts is a conceptual representation which defines how events are organized into higher level activities. Practically all the previous approaches to inducing script knowledge from text relied on count-based techniques (e.g., generative models) and have not attempted to compositionally model events. In this work, we introduce a neural network model which relies on distributed compositional representations of events. The model captures statistical dependencies between events in a scenario, overcomes some of the shortcomings of previous approaches (e.g., by more effectively dealing with data sparsity) and outperforms count-based counterparts on the narrative cloze task.},
pubstate = {published},
type = {inproceedings}
}

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This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing.

• 352_Paper     (0.62MB)
• https://aclanthology.org/L16-1555

@inproceedings{MODI16.352,
title = {InScript: Narrative texts annotated with script information},
author = {Ashutosh Modi and Tatjana Anikina and Simon Ostermann and Manfred Pinkal},
editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
url = {https://aclanthology.org/L16-1555},
year = {2016},
date = {2016-10-17},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
isbn = {978-2-9517408-9-1},
pages = {3485-3493},
publisher = {European Language Resources Association (ELRA)},
address = {Portoro{\v{z}, Slovenia},
abstract = {This paper presents the InScript corpus (Narrative Texts Instantiating Script structure). InScript is a corpus of 1,000 stories centered around 10 different scenarios. Verbs and noun phrases are annotated with event and participant types, respectively. Additionally, the text is annotated with coreference information. The corpus shows rich lexical variation and will serve as a unique resource for the study of the role of script knowledge in natural language processing.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.researchgate.net/publication/321621784_When_the_food_arrives_before_the_menu_Modeling_event-driven_surprisal_in_language_comprehension

@inproceedings{Venhuizen2016,
title = {When the food arrives before the menu: Modeling event-driven surprisal in language comprehension},
author = {Noortje Venhuizen and Harm Brouwer and Matthew W. Crocker},
url = {https://www.researchgate.net/publication/321621784_When_the_food_arrives_before_the_menu_Modeling_event-driven_surprisal_in_language_comprehension},
year = {2016},
date = {2016},
booktitle = {29th CUNY conference on Human Sentence Processing},
publisher = {Events in Language and Cognition workshops},
address = {University of Florida},
abstract = {

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• https://www.researchgate.net/publication/320988696_Reading_Between_the_Lines_The_Influence_of_Script_Knowledge_on_On-Line_Comprehension

@inproceedings{Rabs2016,
title = {Reading between the lines: The influence of script knowledge on on-line comprehension},
author = {Elisabeth Rabs and Heiner Drenhaus and Francesca Delogu and Matthew W. Crocker},
url = {https://www.researchgate.net/publication/320988696_Reading_Between_the_Lines_The_Influence_of_Script_Knowledge_on_On-Line_Comprehension},
year = {2016},
date = {2016},
booktitle = {29th CUNY conference on Human Sentence Processing},
publisher = {Events in Language and Cognition workshops},
address = {University of Florida},
abstract = {

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The MaryTTS system is a modular architecture text-to-speech (TTS) system whose development started around 15 years ago. This paper presents the MaryTTS entry for the Blizzard Challenge 2016. For this entry, we used the default configuration of MaryTTS based on the unit selection paradigm.

However, the architecture is currently undergoing a massive refactoring process in order to provide a more fully modular system. This will allow researchers to focus only on some part of the synthesis process. The current participation objective includes assessing the current baseline quality in order to evaluate any future improvements. These can be achieved more easily thanks to a more flexible and robust architecture. The results obtained in this challenge prove that our system is not obsolete, but improvements need to be made to maintain it in the state of the art in the future.

• MARYTTS_Blizzard2016     (0.23MB)
• https://www.semanticscholar.org/paper/The-MaryTTS-entry-for-the-Blizzard-Challenge-2016-Maguer-Steiner/62e04ad78ba1a531e419bea25cb9eb8799aaf07e

@inproceedings{LeMaguer2016BC,
title = {The MaryTTS entry for the Blizzard Challenge 2016},
author = {S{\'e}bastien Le Maguer and Ingmar Steiner},
url = {https://www.semanticscholar.org/paper/The-MaryTTS-entry-for-the-Blizzard-Challenge-2016-Maguer-Steiner/62e04ad78ba1a531e419bea25cb9eb8799aaf07e},
year = {2016},
date = {2016-09-16},
booktitle = {Blizzard Challenge},
address = {Cupertino, CA, USA},
abstract = {The MaryTTS system is a modular architecture text-to-speech (TTS) system whose development started around 15 years ago. This paper presents the MaryTTS entry for the Blizzard Challenge 2016. For this entry, we used the default configuration of MaryTTS based on the unit selection paradigm. However, the architecture is currently undergoing a massive refactoring process in order to provide a more fully modular system. This will allow researchers to focus only on some part of the synthesis process. The current participation objective includes assessing the current baseline quality in order to evaluate any future improvements. These can be achieved more easily thanks to a more flexible and robust architecture. The results obtained in this challenge prove that our system is not obsolete, but improvements need to be made to maintain it in the state of the art in the future.},
pubstate = {published},
type = {inproceedings}
}

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The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora. This task typically involves the learning of short range dependencies, which generally model the syntactic properties of a language and/or long range dependencies, which are semantic in nature. We propose in this paper a new multi-span architecture, which separately models the short and long context information while it dynamically merges them to perform the language modeling task. This is done through a novel recurrent Long-Short Range Context (LSRC) network, which explicitly models the local (short) and global (long) context using two separate hidden states that evolve in time. This new architecture is an adaptation of the Long-Short Term Memory network (LSTM) to take into account the linguistic properties. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art language modeling techniques.

• https://aclanthology.org/D16-1154/

@inproceedings{Oualil2016,
title = {Long-short range context neural networks for language models},
author = {Youssef Oualil and Mittul Singh and Clayton Greenberg and Dietrich Klakow},
url = {https://aclanthology.org/D16-1154/},
year = {2016},
date = {2016},
publisher = {EMLP 2016},
abstract = {The goal of language modeling techniques is to capture the statistical and structural properties of natural languages from training corpora. This task typically involves the learning of short range dependencies, which generally model the syntactic properties of a language and/or long range dependencies, which are semantic in nature. We propose in this paper a new multi-span architecture, which separately models the short and long context information while it dynamically merges them to perform the language modeling task. This is done through a novel recurrent Long-Short Range Context (LSRC) network, which explicitly models the local (short) and global (long) context using two separate hidden states that evolve in time. This new architecture is an adaptation of the Long-Short Term Memory network (LSTM) to take into account the linguistic properties. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art language modeling techniques.},
pubstate = {published},
type = {inproceedings}
}

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Secure user identification is important for the increasing number of eyewear computers but limited input capabilities pose significant usability challenges for established knowledge-based schemes, such as passwords or PINs. We present SkullConduct, a biometric system that uses bone conduction of sound through the user’s skull as well as a microphone readily integrated into many of these devices, such as Google Glass. At the core of SkullConduct is a method to analyze the characteristic frequency response created by the user’s skull using a combination of Mel Frequency Cepstral Coefficient (MFCC) features as well as a computationally light-weight 1NN classifier. We report on a controlled experiment with 10 participants that shows that this frequency response is person-specific and stable — even when taking off and putting on the device multiple times — and thus serves as a robust biometric. We show that our method can identify users with 97.0% accuracy and authenticate them with an equal error rate of 6.9%, thereby bringing biometric user identification to eyewear computers equipped with bone conduction technology.

• http://doi.acm.org/10.1145/2858036.2858152
• https://doi.org/10.1145/2858036.2858152

@inproceedings{Schneegass:2016:SBU:2858036.2858152,
title = {SkullConduct: Biometric User Identification on Eyewear Computers Using Bone Conduction Through the Skull},
author = {Stefan Schneegass and Youssef Oualil and Andreas Bulling},
url = {http://doi.acm.org/10.1145/2858036.2858152},
doi = {https://doi.org/10.1145/2858036.2858152},
year = {2016},
date = {2016},
booktitle = {Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems},
isbn = {978-1-4503-3362-7},
pages = {1379-1384},
publisher = {ACM},
address = {New York, NY, USA},
abstract = {Secure user identification is important for the increasing number of eyewear computers but limited input capabilities pose significant usability challenges for established knowledge-based schemes, such as passwords or PINs. We present SkullConduct, a biometric system that uses bone conduction of sound through the user's skull as well as a microphone readily integrated into many of these devices, such as Google Glass. At the core of SkullConduct is a method to analyze the characteristic frequency response created by the user's skull using a combination of Mel Frequency Cepstral Coefficient (MFCC) features as well as a computationally light-weight 1NN classifier. We report on a controlled experiment with 10 participants that shows that this frequency response is person-specific and stable -- even when taking off and putting on the device multiple times -- and thus serves as a robust biometric. We show that our method can identify users with 97.0% accuracy and authenticate them with an equal error rate of 6.9%, thereby bringing biometric user identification to eyewear computers equipped with bone conduction technology.},
pubstate = {published},
type = {inproceedings}
}

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This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.

• http://www.aclweb.org/anthology/W/W16/W16-2301

@inproceedings{bojar-EtAl:2016:WMT1,
title = {Findings of the 2016 Conference on Machine Translation},
author = {Ondvrej Bojar and Rajen Chatterjee and Christian Federmann and Yvette Graham and Barry Haddow and Matthias Huck and Antonio Jimeno Yepes and Philipp Koehn and Varvara Logacheva and Christof Monz and Matteo Negri and Aurelie Neveol and Mariana Neves and Martin Popel and Matt Post and Raphael Rubino and Carolina Scarton and Lucia Specia and Marco Turchi and Karin Verspoor and Marcos Zampieri},
url = {http://www.aclweb.org/anthology/W/W16/W16-2301},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of the First Conference on Machine Translation},
pages = {131-198},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
abstract = {This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments). The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.},
pubstate = {published},
type = {inproceedings}
}

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This work explores the use of unsupervised morph segmentation along with statistical language models for the task of vocabulary expansion. Unsupervised vocabulary expansion has large potential for improving vocabulary coverage and performance in different natural language processing tasks, especially in lessresourced settings on morphologically rich languages. We propose a combination of unsupervised morph segmentation and statistical language models and evaluate on languages from the Babel corpus. The method is shown to perform well for all the evaluated languages when compared to the previous work on the task.

• http://anthology.aclweb.org/P16-2029

@inproceedings{varjokallio-klakow:2016:P16-2,
title = {Unsupervised morph segmentation and statistical language models for vocabulary expansion},
author = {Matti Varjokallio and Dietrich Klakow},
url = {http://anthology.aclweb.org/P16-2029},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages = {175-180},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
abstract = {This work explores the use of unsupervised morph segmentation along with statistical language models for the task of vocabulary expansion. Unsupervised vocabulary expansion has large potential for improving vocabulary coverage and performance in different natural language processing tasks, especially in lessresourced settings on morphologically rich languages. We propose a combination of unsupervised morph segmentation and statistical language models and evaluate on languages from the Babel corpus. The method is shown to perform well for all the evaluated languages when compared to the previous work on the task.},
pubstate = {published},
type = {inproceedings}
}

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In this paper, we present Roleo, a web tool for visualizing the vector spaces generated by the evaluation of distributional memory (DM) models over thematic fit judgements. A thematic fit judgement is a rating of the selectional preference of a verb for an argument that fills a given thematic role. The DM approach to thematic fit judgements involves the construction of a sub-space in which a prototypical role-filler can be built for comparison to the noun being judged. We describe a publicly-accessible web tool that allows for querying and exploring these spaces as well as a technique for visualizing thematic fit sub-spaces efficiently for web use.

• P16-4024     (0.35MB)
• https://www.researchgate.net/publication/306093691_Roleo_Visualising_Thematic_Fit_Spaces_on_the_Web

@inproceedings{sayeed-hong-demberg:2016:P16-4,
title = {Roleo: Visualising Thematic Fit Spaces on the Web},
author = {Asad Sayeed and Xudong Hong and Vera Demberg},
url = {https://www.researchgate.net/publication/306093691_Roleo_Visualising_Thematic_Fit_Spaces_on_the_Web},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of ACL-2016 System Demonstrations},
pages = {139-144},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
abstract = {In this paper, we present Roleo, a web tool for visualizing the vector spaces generated by the evaluation of distributional memory (DM) models over thematic fit judgements. A thematic fit judgement is a rating of the selectional preference of a verb for an argument that fills a given thematic role. The DM approach to thematic fit judgements involves the construction of a sub-space in which a prototypical role-filler can be built for comparison to the noun being judged. We describe a publicly-accessible web tool that allows for querying and exploring these spaces as well as a technique for visualizing thematic fit sub-spaces efficiently for web use.},
pubstate = {published},
type = {inproceedings}
}

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Automatically learning script knowledge has proved difficult, with previous work not or just barely beating a most-frequent baseline. Script knowledge is a type of world knowledge which can however be useful for various task in NLP and psycholinguistic modelling. We here propose a model that includes participant information (i.e., knowledge about which participants are relevant for a script) and show, on the Dinners from Hell corpus as well as the InScript corpus, that this knowledge helps us to significantly improve prediction performance on the narrative cloze task.

• http://www.aclweb.org/anthology/N16-1067

@inproceedings{ahrendt-demberg:2016:N16-1,
title = {Improving event prediction by representing script participants},
author = {Simon Ahrendt and Vera Demberg},
url = {http://www.aclweb.org/anthology/N16-1067},
year = {2016},
date = {2016-06-01},
booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
pages = {546-551},
publisher = {Association for Computational Linguistics},
address = {San Diego, California},
abstract = {Automatically learning script knowledge has proved difficult, with previous work not or just barely beating a most-frequent baseline. Script knowledge is a type of world knowledge which can however be useful for various task in NLP and psycholinguistic modelling. We here propose a model that includes participant information (i.e., knowledge about which participants are relevant for a script) and show, on the Dinners from Hell corpus as well as the InScript corpus, that this knowledge helps us to significantly improve prediction performance on the narrative cloze task.},
pubstate = {published},
type = {inproceedings}
}

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LingoTurk is an open-source, freely available crowdsourcing client/server system aimed primarily at psycholinguistic experimentation where custom and specialized user interfaces are required but not supported by popular crowdsourcing task management platforms. LingoTurk enables user-friendly local hosting of experiments as well as condition management and participant exclusion. It is compatible with Amazon Mechanical Turk and Prolific Academic. New experiments can easily be set up via the Play Framework and the LingoTurk API, while multiple experiments can be managed from a single system.

• http://www.aclweb.org/anthology/N16-3012

@inproceedings{pusse-sayeed-demberg:2016:N16-3,
title = {LingoTurk: managing crowdsourced tasks for psycholinguistics},
author = {Florian Pusse and Asad Sayeed and Vera Demberg},
url = {http://www.aclweb.org/anthology/N16-3012},
year = {2016},
date = {2016-06-01},
booktitle = {Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations},
pages = {57-61},
publisher = {Association for Computational Linguistics},
address = {San Diego, California},
abstract = {LingoTurk is an open-source, freely available crowdsourcing client/server system aimed primarily at psycholinguistic experimentation where custom and specialized user interfaces are required but not supported by popular crowdsourcing task management platforms. LingoTurk enables user-friendly local hosting of experiments as well as condition management and participant exclusion. It is compatible with Amazon Mechanical Turk and Prolific Academic. New experiments can easily be set up via the Play Framework and the LingoTurk API, while multiple experiments can be managed from a single system.},
pubstate = {published},
type = {inproceedings}
}

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Scripts are standardized event sequences describing typical everyday activities, which play an important role in the computational modeling of cognitive abilities (in particular for natural language processing). We present a large-scale crowdsourced collection of explicit linguistic descriptions of script-specific event sequences (40 scenarios with 100 sequences each). The corpus is enriched with crowdsourced alignment annotation on a subset of the event descriptions, to be used in future work as seed data for automatic alignment of event descriptions (for example via clustering). The event descriptions to be aligned were chosen among those expected to have the strongest corrective effect on the clustering algorithm. The alignment annotation was evaluated against a gold standard of expert annotators. The resulting database of partially-aligned script-event descriptions provides a sound empirical basis for inducing high-quality script knowledge, as well as for any task involving alignment and paraphrase detection of events.

• https://aclanthology.org/L16-1556/

@inproceedings{WANZARE16.913,
title = {A Crowdsourced Database of Event Sequence Descriptions for the Acquisition of High-quality Script Knowledge},
author = {Lilian Diana Awuor Wanzare and Alessandra Zarcone and Stefan Thater and Manfred Pinkal},
editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Marko Grobelnik and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
url = {https://aclanthology.org/L16-1556/},
year = {2016},
date = {2016},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
isbn = {978-2-9517408-9-1},
publisher = {European Language Resources Association (ELRA)},
address = {Portoro{\v{z}, Slovenia},
abstract = {Scripts are standardized event sequences describing typical everyday activities, which play an important role in the computational modeling of cognitive abilities (in particular for natural language processing). We present a large-scale crowdsourced collection of explicit linguistic descriptions of script-specific event sequences (40 scenarios with 100 sequences each). The corpus is enriched with crowdsourced alignment annotation on a subset of the event descriptions, to be used in future work as seed data for automatic alignment of event descriptions (for example via clustering). The event descriptions to be aligned were chosen among those expected to have the strongest corrective effect on the clustering algorithm. The alignment annotation was evaluated against a gold standard of expert annotators. The resulting database of partially-aligned script-event descriptions provides a sound empirical basis for inducing high-quality script knowledge, as well as for any task involving alignment and paraphrase detection of events.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.bibsonomy.org/bibtex/217fb65d2ef291a8a10df15db8a8cf5c7/sfb1102

@inproceedings{LeMaguer2015Variability,
title = {Toward a Speech Synthesis Guided by the Modeling of Unexpected Events},
author = {S{\'e}bastien Le Maguer and Ingmar Steiner and Bernd M{\"o}bius},
editor = {Antje Schweitzer and Grzegorz Dogil},
url = {https://www.bibsonomy.org/bibtex/217fb65d2ef291a8a10df15db8a8cf5c7/sfb1102},
year = {2015},
date = {2015},
booktitle = {Workshop on Modeling Variability in Speech},
address = {Stuttgart, Germany},
pubstate = {published},
type = {inproceedings}
}

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• sfb-b1-coll-C4-March2015     (0.51MB)

@inproceedings{andrea2015models,
title = {Models for Mutual Intelligibility},
author = {Andrea Fischer and Kl{\'a}ra J{\'a}grov{\'a} and Irina Stenger and Tania Avgustinova and Dietrich Klakow and Roland Marti},
year = {2015},
date = {2015},
booktitle = {Data Mining and its Use and Usability for Linguistic Analysis},
publisher = {Universit{\"a}t des Saarlandes},
address = {Saarbr{\"u}cken, Germany},
pubstate = {published},
type = {inproceedings}
}

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This article presents the methods and findings of a computational transformation of orthography within two Slavic language pairs (Czech­Polish and Bulgarian­Russian) on different word sets. The experiment aimed at investigating to what extent these closely related languages are mutually intelligible, concentrating on their orthographies as linguistic interfaces to the written text. Besides analyzing orthographic similarity, the aim was to gain insights into the applicability of rules based on traditional linguistic assumptions for the purposes of language modelling.

• https://www.bibsonomy.org/bibtex/231c7c8a9b94a872a7396d5b1a1ef7962/sfb1102

@inproceedings{klara2015orthography,
title = {An Orthography Transformation Experiment with Czech-Polish and Bulgarian-Russian Parallel Word Sets},
author = {Andrea Fischer and Kl{\'a}ra J{\'a}grov{\'a} and Irina Stenger and Tania Avgustinova and Dietrich Klakow and Roland Marti},
editor = {Bernadette Sharp and Wiesław Lubaszewski and Rodolfo Delmonte},
url = {https://www.bibsonomy.org/bibtex/231c7c8a9b94a872a7396d5b1a1ef7962/sfb1102},
year = {2015},
date = {2015},
booktitle = {Natural Language Processing and Cognitive Science},
pages = {115-126},
publisher = {Ca Foscarina Editrice, Venezia},
abstract = {This article presents the methods and findings of a computational transformation of orthography within two Slavic language pairs (Czech­Polish and Bulgarian­Russian) on different word sets. The experiment aimed at investigating to what extent these closely related languages are mutually intelligible, concentrating on their orthographies as linguistic interfaces to the written text. Besides analyzing orthographic similarity, the aim was to gain insights into the applicability of rules based on traditional linguistic assumptions for the purposes of language modelling.},
pubstate = {published},
type = {inproceedings}
}

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The possibility of intercomprehension between related languages is a generally accepted fact suggesting that mutual intelligibility is systematic. Of particular interest are the Slavic languages, which are “sufficiently similar and sufficiently different to provide an attractive research laboratory” (Corbett 1998). They exhibit practically all typologically attested means of encoding grammatical information, ranging from extremely dense to highly redundant constructions, and their development is the result of various language contact scenarios (Balkansprachbund, German influence on West Slavic languages, Finno-Ugric substratum in East Slavic languages etc.).

• REMU-slides-presentation_2015-05-28     (0.63MB)
• https://www.bibsonomy.org/bibtex/187b1c53b1bad76027e0a305d2a6e2cce/sfb1102

@inproceedings{tania2015empirical,
title = {The Empirical Basis of Slavic Intercomprehension},
author = {Tania Avgustinova and Andrea Fischer and Kl{\'a}ra J{\'a}grov{\'a} and Irina Stenger},
url = {https://www.bibsonomy.org/bibtex/187b1c53b1bad76027e0a305d2a6e2cce/sfb1102},
year = {2015},
date = {2015},
booktitle = {REMU},
address = {Joensuu, Finland},
abstract = {The possibility of intercomprehension between related languages is a generally accepted fact suggesting that mutual intelligibility is systematic. Of particular interest are the Slavic languages, which are “sufficiently similar and sufficiently different to provide an attractive research laboratory” (Corbett 1998). They exhibit practically all typologically attested means of encoding grammatical information, ranging from extremely dense to highly redundant constructions, and their development is the result of various language contact scenarios (Balkansprachbund, German influence on West Slavic languages, Finno-Ugric substratum in East Slavic languages etc.).},
pubstate = {published},
type = {inproceedings}
}

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As dialog systems are getting more and more ubiquitous, there is an increasing number of application domains for natural language generation, and generation objectives are getting more diverse (e.g., generating informationally dense vs. less complex utterances, as a function of target user and usage situation). Flexible generation is difficult and labourintensive with traditional template-based generation systems, while fully data-driven approaches may lead to less grammatical output, particularly if the measures used for generation objectives are correlated with measures of grammaticality. We here explore the combination of a data-driven approach with two very simple automatic grammar induction methods, basing its implementation on OpenCCG.

• https://www.aclweb.org/anthology/W15-4718/

@inproceedings{fischer:demberg:klakow,
title = {Towards Flexible, Small-Domain Surface Generation: Combining Data-Driven and Grammatical Approaches},
author = {Andrea Fischer and Vera Demberg and Dietrich Klakow},
url = {https://www.aclweb.org/anthology/W15-4718/},
year = {2015},
date = {2015},
booktitle = {Proceedings of the 15th European Workshop on Natural Language Generation (ENLG)},
pages = {105-108},
publisher = {Association for Computational Linguistics},
address = {Brighton, England, UK},
abstract = {As dialog systems are getting more and more ubiquitous, there is an increasing number of application domains for natural language generation, and generation objectives are getting more diverse (e.g., generating informationally dense vs. less complex utterances, as a function of target user and usage situation). Flexible generation is difficult and labourintensive with traditional template-based generation systems, while fully data-driven approaches may lead to less grammatical output, particularly if the measures used for generation objectives are correlated with measures of grammaticality. We here explore the combination of a data-driven approach with two very simple automatic grammar induction methods, basing its implementation on OpenCCG.},
pubstate = {published},
type = {inproceedings}
}

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Violations of the maxims of Quantity occur when utterances provide more (over-specified) or less (under-specified) information than strictly required for referent identification. While behavioural data suggest that under-specified expressions lead to comprehension difficulty and communicative failure, there is no consensus as to whether over-specified expressions are also detrimental to comprehension. In this study we shed light on this debate, providing neurophysiological evidence supporting the view that extra information facilitates comprehension. We further present novel evidence that referential failure due to underspecification is qualitatively different from explicit cases of referential failure, when no matching referential candidate is available in the context.

• https://www.researchgate.net/publication/312296322_ERP_indices_of_situated_reference_in_visual_contexts

@article{Tourtouri2015,
title = {ERP Indices of situated reference in visual contexts},
author = {Elli Tourtouri and Francesca Delogu and Matthew W. Crocker},
url = {https://www.researchgate.net/publication/312296322_ERP_indices_of_situated_reference_in_visual_contexts},
year = {2015},
date = {2015},
publisher = {37th Annual Conference of the Cognitive Science Society},
address = {Austin, Texas, USA},
abstract = {Violations of the maxims of Quantity occur when utterances provide more (over-specified) or less (under-specified) information than strictly required for referent identification. While behavioural data suggest that under-specified expressions lead to comprehension difficulty and communicative failure, there is no consensus as to whether over-specified expressions are also detrimental to comprehension. In this study we shed light on this debate, providing neurophysiological evidence supporting the view that extra information facilitates comprehension. We further present novel evidence that referential failure due to underspecification is qualitatively different from explicit cases of referential failure, when no matching referential candidate is available in the context.},
pubstate = {published},
type = {article}
}

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