Publications

In this paper, we discuss the human thematic fit judgement correlation task in the context of real-valued vector space word representations. Thematic fit is the extent to which an argument fulfils the selectional preference of a verb given a role: for example, how well “cake” fulfils the patient role of “cut”. In recent work, systems have been evaluated on this task by finding the correlations of their output judgements with human-collected judgement data. This task is a representationindependent way of evaluating models that can be applied whenever a system score can be generated, and it is applicable wherever predicate-argument relations are significant to performance in end-user tasks. Significant progress has been made on this cognitive modeling task, leaving considerable space for future, more comprehensive types of evaluation.

• https://www.researchgate.net/publication/306094219_Thematic_fit_evaluation_an_aspect_of_selectional_preferences

@article{Sayeed2016,
title = {Thematic fit evaluation: an aspect of selectional preferences},
author = {Asad Sayeed and Clayton Greenberg and Vera Demberg},
url = {https://www.researchgate.net/publication/306094219_Thematic_fit_evaluation_an_aspect_of_selectional_preferences},
year = {2016},
date = {2016},
journal = {Proceedings of the 1st Workshop on Evaluating Vector Space Representations for NLP},
pages = {99-105},
abstract = {In this paper, we discuss the human thematic fit judgement correlation task in the context of real-valued vector space word representations. Thematic fit is the extent to which an argument fulfils the selectional preference of a verb given a role: for example, how well “cake” fulfils the patient role of “cut”. In recent work, systems have been evaluated on this task by finding the correlations of their output judgements with human-collected judgement data. This task is a representationindependent way of evaluating models that can be applied whenever a system score can be generated, and it is applicable wherever predicate-argument relations are significant to performance in end-user tasks. Significant progress has been made on this cognitive modeling task, leaving considerable space for future, more comprehensive types of evaluation.},
pubstate = {published},
type = {article}
}

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This paper investigates whether Information Theory (IT) in the tradition of Shannon (1948) and in particular the “Uniform Information Density Hypothesis” (UID, see Jager 2010) might contribute to our understanding of a phenomenon called “article omission” (AO) in the literature. To this effect, we trained language models on a corpus of 17 different text types (from prototypically written text types like legal texts to prototypically spoken text types like dialogue) with about 2.000 sentences each and compared the density profiles of minimal pairs. Our results suggest, firstly, that an overtly realized article significantly reduces the surprisal on the following head noun (as was to be expected). It also shows, however, that omitting the article results in a non-uniform distribution (thus contradicting the UID). Since empirically AO seems not to depend on specific lexical items, we also trained our language models on a more abstract level (part of speech). With respect to this level of analysis we were able to show that, again, an overtly realized article significantly reduces the surprisal on the following head noun, but at the same time AO results in a more uniform distribution of information. In the case of AO the UID thus seems to operate on the level of POS rather than on the lexical level.

• 16_konvensproc     (0.34MB)
• https://www.linguistics.rub.de/konvens16/pub/16_konvensproc.pdf

@inproceedings{HorchReich2016,
title = {On “Article Omission” in German and the “Uniform Information Density Hypothesis”},
author = {Ingo Reich and Eva Horch},
editor = {Stefanie Dipper and Friedrich Neubarth and Heike Zinsmeister},
url = {https://www.linguistics.rub.de/konvens16/pub/16_konvensproc.pdf},
year = {2016},
date = {2016},
booktitle = {Proceedings of the 13th Conference on Natural Language Processing (KONVENS 2016)},
pages = {125-127},
address = {Bochum},
abstract = {This paper investigates whether Information Theory (IT) in the tradition of Shannon (1948) and in particular the “Uniform Information Density Hypothesis” (UID, see Jager 2010) might contribute to our understanding of a phenomenon called “article omission” (AO) in the literature. To this effect, we trained language models on a corpus of 17 different text types (from prototypically written text types like legal texts to prototypically spoken text types like dialogue) with about 2.000 sentences each and compared the density profiles of minimal pairs. Our results suggest, firstly, that an overtly realized article significantly reduces the surprisal on the following head noun (as was to be expected). It also shows, however, that omitting the article results in a non-uniform distribution (thus contradicting the UID). Since empirically AO seems not to depend on specific lexical items, we also trained our language models on a more abstract level (part of speech). With respect to this level of analysis we were able to show that, again, an overtly realized article significantly reduces the surprisal on the following head noun, but at the same time AO results in a more uniform distribution of information. In the case of AO the UID thus seems to operate on the level of POS rather than on the lexical level.},
pubstate = {published},
type = {inproceedings}
}

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Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Surface features achieve good performance, but they are not readily applicable to other languages without semantic lexicons. Previous neural models require parses, surface features, or a small label set to work well. Here, we propose neural network models that are based on feedforward and long-short term memory architecture without any surface features. To our surprise, our best configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning. Under various fine-grained label sets and a cross-linguistic setting, our feedforward models perform consistently better or at least just as well as systems that require hand-crafted surface features. Our models present the first neural Chinese discourse parser in the style of Chinese Discourse Treebank, showing that our results hold cross-linguistically.

• http://arxiv.org/abs/1606.01990

@article{DBLP:journals/corr/RutherfordDX16,
title = {Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features},
author = {Attapol Rutherford and Vera Demberg and Nianwen Xue},
url = {http://arxiv.org/abs/1606.01990},
year = {2016},
date = {2016},
journal = {CoRR},
abstract = {Inferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Surface features achieve good performance, but they are not readily applicable to other languages without semantic lexicons. Previous neural models require parses, surface features, or a small label set to work well. Here, we propose neural network models that are based on feedforward and long-short term memory architecture without any surface features. To our surprise, our best configured feedforward architecture outperforms LSTM-based model in most cases despite thorough tuning. Under various fine-grained label sets and a cross-linguistic setting, our feedforward models perform consistently better or at least just as well as systems that require hand-crafted surface features. Our models present the first neural Chinese discourse parser in the style of Chinese Discourse Treebank, showing that our results hold cross-linguistically.},
pubstate = {published},
type = {article}
}

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Previous experimental studies on concessive connectives have only looked at their local facilitating or predictive effect on discourse relation comprehension and have often viewed them as a class of discourse markers with similar effects. We look into the effect of two connectives, but and although, for inferring contrastive vs. concessive discourse relations to complement previous experimental work on causal inferences. An offline survey on AMTurk and an online eye-tracking-while-reading experiment are conducted to show that even between these two connectives, which mark the same set of relations, interpretations are biased. The bias is consistent with the distribution of the connective across discourse relations. This suggests that an account of discourse connective meaning based on probability distributions can better account for comprehension data than a classic categorical approach, or an approach where closely related connectives only have a core meaning and the rest of the interpretation comes from the discourse arguments.

• CogSci2016_AsrDemberg     (0.23MB)
• https://www.semanticscholar.org/paper/But-vs.-Although-under-the-microscope-Asr-Demberg/68be3f7ec0d7642f4371d991fc15471416141dfd

@inproceedings{Asr2016b,
title = {But vs. Although under the microscope},
author = {Fatemeh Torabi Asr and Vera Demberg},
url = {https://www.semanticscholar.org/paper/But-vs.-Although-under-the-microscope-Asr-Demberg/68be3f7ec0d7642f4371d991fc15471416141dfd},
year = {2016},
date = {2016},
booktitle = {Proceedings of the 38th Meeting of the Cognitive Science Society},
pages = {366-371},
address = {Philadelphia, Pennsylvania, USA},
abstract = {Previous experimental studies on concessive connectives have only looked at their local facilitating or predictive effect on discourse relation comprehension and have often viewed them as a class of discourse markers with similar effects. We look into the effect of two connectives, but and although, for inferring contrastive vs. concessive discourse relations to complement previous experimental work on causal inferences. An offline survey on AMTurk and an online eye-tracking-while-reading experiment are conducted to show that even between these two connectives, which mark the same set of relations, interpretations are biased. The bias is consistent with the distribution of the connective across discourse relations. This suggests that an account of discourse connective meaning based on probability distributions can better account for comprehension data than a classic categorical approach, or an approach where closely related connectives only have a core meaning and the rest of the interpretation comes from the discourse arguments.},
pubstate = {published},
type = {inproceedings}
}

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In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data. This raises a number of questions regarding interoperability of discourse relation annotation schemes, as well as regarding differences in discourse annotation for written vs. spoken domains. In this paper, we describe ouron annotating two spoken domains from the SPICE Ireland corpus (telephone conversations and broadcast interviews) according todifferent discourse annotation schemes, PDTB 3.0 and CCR. We show that annotations in the two schemes can largely be mappedone another, and discuss differences in operationalisations of discourse relation schemes which present a challenge to automatic mapping. We also observe systematic differences in the prevalence of implicit discourse relations in spoken data compared to written texts,find that there are also differences in the types of causal relations between the domains. Finally, we find that PDTB 3.0 addresses many shortcomings of PDTB 2.0 wrt. the annotation of spoken discourse, and suggest further extensions. The new corpus has roughly theof the CoNLL 2015 Shared Task test set, and we hence hope that it will be a valuable resource for the evaluation of automatic discourse relation labellers.

• 457_Paper     (0.19MB)
• https://aclanthology.org/L16-1165

@inproceedings{REHBEIN16.457,
title = {Annotating Discourse Relations in Spoken Language: A Comparison of the PDTB and CCR Frameworks},
author = {Ines Rehbein and Merel Scholman and Vera Demberg},
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/L16-1165},
year = {2016},
date = {2016},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
isbn = {978-2-9517408-9-1},
pages = {1039-1046},
publisher = {European Language Resources Association (ELRA)},
address = {Portoro{\v{z}, Slovenia},
abstract = {In discourse relation annotation, there is currently a variety of different frameworks being used, and most of them have been developed and employed mostly on written data. This raises a number of questions regarding interoperability of discourse relation annotation schemes, as well as regarding differences in discourse annotation for written vs. spoken domains. In this paper, we describe ouron annotating two spoken domains from the SPICE Ireland corpus (telephone conversations and broadcast interviews) according todifferent discourse annotation schemes, PDTB 3.0 and CCR. We show that annotations in the two schemes can largely be mappedone another, and discuss differences in operationalisations of discourse relation schemes which present a challenge to automatic mapping. We also observe systematic differences in the prevalence of implicit discourse relations in spoken data compared to written texts,find that there are also differences in the types of causal relations between the domains. Finally, we find that PDTB 3.0 addresses many shortcomings of PDTB 2.0 wrt. the annotation of spoken discourse, and suggest further extensions. The new corpus has roughly theof the CoNLL 2015 Shared Task test set, and we hence hope that it will be a valuable resource for the evaluation of automatic discourse relation labellers.},
pubstate = {published},
type = {inproceedings}
}

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The resource contains all texts from the Broadcast interview and Telephone conversation genres from the SPICE-Ireland corpus, annotated with discourse relations according to the PDTB 3.0 and CCR frameworks. Contact person: Merel Scholman

@inproceedings{merel2016,
title = {Disco-SPICE (Spoken conversations from the SPICE-Ireland corpus annotated with discourse relations)},
author = {Ines Rehbein and Merel Scholman and Vera Demberg},
year = {2016},
date = {2016},
booktitle = {Annotating discourse relations in spoken language: A comparison of the PDTB and CCR frameworks. Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 16)},
address = {Portoro{\v{z}, Slovenia},
abstract = {The resource contains all texts from the Broadcast interview and Telephone conversation genres from the SPICE-Ireland corpus, annotated with discourse relations according to the PDTB 3.0 and CCR frameworks. Contact person: Merel Scholman},
pubstate = {published},
type = {inproceedings}
}

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• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4723154/
• https://doi.org/10.1371/journal.pone.0146194

@article{demberg:sayeed:2016:plosone,
title = {The Frequency of Rapid Pupil Dilations as a Measure of Linguistic Processing Difficulty},
author = {Vera Demberg and Asad Sayeed},
editor = {Emmanuel Andreas Stamatakis},
url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4723154/},
doi = {https://doi.org/10.1371/journal.pone.0146194},
year = {2016},
date = {2016},
journal = {PLOS ONE},
volume = {11},
number = {1},
abstract = {

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We present the Royal Society Corpus (RSC) built from the Philosophical Transactions and Proceedings of the Royal Society of London. At present, the corpus contains articles from the first two centuries of the journal (1665-1869) and amounts to around 35 million tokens. The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English. Specifically, we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication. When building corpora from uncharted material, typically not all relevant meta-data (e.g. author, time, genre) or linguistic data (e.g. sentence/word boundaries, words, parts of speech) is readily available. We present an approach to obtain good quality meta-data and base text data adopting the concept of Agile Software Development.

• https://aclanthology.org/L16-1305

@inproceedings{Kermes2016,
title = {The Royal Society Corpus: From Uncharted Data to Corpus},
author = {Hannah Kermes and Stefania Degaetano-Ortlieb and J{\"o}rg Knappen and Ashraf Khamis and Elke Teich},
url = {https://aclanthology.org/L16-1305},
year = {2016},
date = {2016},
booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)},
pages = {1928-1931},
publisher = {European Language Resources Association (ELRA)},
address = {Portoro{\v{z}, Slovenia},
abstract = {We present the Royal Society Corpus (RSC) built from the Philosophical Transactions and Proceedings of the Royal Society of London. At present, the corpus contains articles from the first two centuries of the journal (1665-1869) and amounts to around 35 million tokens. The motivation for building the RSC is to investigate the diachronic linguistic development of scientific English. Specifically, we assume that due to specialization, linguistic encodings become more compact over time (Halliday, 1988; Halliday and Martin, 1993), thus creating a specific discourse type characterized by high information density that is functional for expert communication. When building corpora from uncharted material, typically not all relevant meta-data (e.g. author, time, genre) or linguistic data (e.g. sentence/word boundaries, words, parts of speech) is readily available. We present an approach to obtain good quality meta-data and base text data adopting the concept of Agile Software Development.},
pubstate = {published},
type = {inproceedings}
}

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Science gradually developed into an established sociocultural domain starting from the mid-17^th century onwards. In this process it became increasingly specialized and diversified. Here, we investigate a particular aspect of specialization on the basis of probabilistic topic models. As a corpus we use the Royal Society Corpus (Khamis et al. 2015), which covers the period from 1665 to 1869 and contains 9015 documents.

• https://www.semanticscholar.org/paper/Topical-Diversification-Over-Time-In-The-Royal-Fankhauser-Knappen/7f7dce0d0b8209d0c841c8da031614fccb97a787

@inproceedings{Fankhauser2016,
title = {Topical Diversification over Time in the Royal Society Corpus},
author = {Peter Fankhauser and J{\"o}rg Knappen and Elke Teich},
url = {https://www.semanticscholar.org/paper/Topical-Diversification-Over-Time-In-The-Royal-Fankhauser-Knappen/7f7dce0d0b8209d0c841c8da031614fccb97a787},
year = {2016},
date = {2016},
booktitle = {Proceedings of Digital Humanities (DH'16)},
address = {Krakow, Poland},
abstract = {Science gradually developed into an established sociocultural domain starting from the mid-17^th century onwards. In this process it became increasingly specialized and diversified. Here, we investigate a particular aspect of specialization on the basis of probabilistic topic models. As a corpus we use the Royal Society Corpus (Khamis et al. 2015), which covers the period from 1665 to 1869 and contains 9015 documents.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.researchgate.net/publication/331648262_The_Royal_Society_Corpus_Towards_a_high-quality_corpus_for_studying_diachronic_variation_in_scientific_writing

@inproceedings{Kermes2016a,
title = {The Royal Society Corpus. Towards a high-quality resource for studying diachronic variation in scientific writing},
author = {Hannah Kermes and J{\"o}rg Knappen and Ashraf Khamis and Stefania Degaetano-Ortlieb and Elke Teich},
url = {https://www.researchgate.net/publication/331648262_The_Royal_Society_Corpus_Towards_a_high-quality_corpus_for_studying_diachronic_variation_in_scientific_writing},
year = {2016},
date = {2016},
booktitle = {Proceedings of Digital Humanities (DH'16)},
address = {Krakow, Poland},
abstract = {

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We present a new approach for modeling diachronic linguistic change in grammatical usage. We illustrate the approach on English scientific writing in Late Modern English, focusing on grammatical patterns that are potentially indicative of shifts in register, genre and/or style. Commonly, diachronic change is characterized by the relative frequency of typical linguistic features over time. However, to fully capture changing linguistic usage, feature productivity needs to be taken into account as well. We introduce a data-driven approach for systematically detecting typical features and assessing their productivity over time, using information-theoretic
measures of entropy and surprisal.

• https://aclanthology.org/W16-2121
• https://doi.org/10.18653/v1/W16-2121

@inproceedings{Degaetano-Ortlieb2016a,
title = {Information-based modeling of diachronic linguistic change: from typicality to productivity},
author = {Stefania Degaetano-Ortlieb and Elke Teich},
url = {https://aclanthology.org/W16-2121},
doi = {https://doi.org/10.18653/v1/W16-2121},
year = {2016},
date = {2016},
booktitle = {Proceedings of Language Technologies for the Socio-Economic Sciences and Humanities (LATECH'16), Association for Computational Linguistics (ACL)},
pages = {165-173},
publisher = {Association for Computational Linguistics},
address = {Berlin, Germany},
abstract = {We present a new approach for modeling diachronic linguistic change in grammatical usage. We illustrate the approach on English scientific writing in Late Modern English, focusing on grammatical patterns that are potentially indicative of shifts in register, genre and/or style. Commonly, diachronic change is characterized by the relative frequency of typical linguistic features over time. However, to fully capture changing linguistic usage, feature productivity needs to be taken into account as well. We introduce a data-driven approach for systematically detecting typical features and assessing their productivity over time, using information-theoretic measures of entropy and surprisal.},
pubstate = {published},
type = {inproceedings}
}

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• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302025/

@inproceedings{Ankener2016,
title = {The influence of visual context on predictions in sentence processing: Evidence from ICA},
author = {Maria Staudte},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6302025/},
year = {2016},
date = {2016},
booktitle = {Proceedings at the Language and Perception International Conference},
address = {Trondheim, Norwegen},
abstract = {

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• ESLP2016_book_of_abstracts_2     (10.01MB)

@inproceedings{sekicki2016b,
title = {Cost and Gains of Using Visual Context for Referent Prediction},
author = {Maria Staudte},
year = {2016},
date = {2016-10-18},
booktitle = {Proceedings of the 9th Embodied and Situated Language Processing Conference (ESLP)},
address = {Pucón},
pubstate = {published},
type = {inproceedings}
}

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We investigated the effect of visual context on cognitive load (CL) that is induced by prediction forming during sentence processing, using a novel measure of CL: the Index of Cognitive Activity. We conducted two experiments, one including only linguistic stimuli (LING) and one with the additional visual context of four potential target objects (VIS). Noun predictability was modulated by verb constraint (ironable vs. describable objects) and thematic fit; and further by visual competitors (two ironable vs. four describable objects).
“The woman (1) irons / (2) describes soon the (a) t-shirt / (b) sock.“
We found lower CL on the noun in (1a) compared to (1b) in both studies, suggesting that after “iron“, “t-shirt“ was more predictable, and hence easier to process, than “sock“. More importantly, VIS findings show higher CL on “iron“ compared to “describe“, suggesting that visual context allowed for active exclusion of two non-ironable targets. Conversely, CL on nouns was lower when following “iron“ than “describe“, due to only one ironable competitor compared to three describable competitors. These findings suggest that the presence of visual context alters the distribution of CL during sentence processing. Future work includes gaze cues as additional information, potentially further affecting CL distribution.

@inproceedings{sekicki2016,
title = {Language Processing: Cognitive Load with(out) Visual Context},
author = {Mirjana Sekicki and Christine Ankener and Maria Staudte},
year = {2016},
date = {2016-10-18},
booktitle = {Proceedings at the 22nd Annual Conference on Architectures and Mechanisms for Language Processing (AMLaP)},
address = {Bilbao, Spain},
abstract = {We investigated the effect of visual context on cognitive load (CL) that is induced by prediction forming during sentence processing, using a novel measure of CL: the Index of Cognitive Activity. We conducted two experiments, one including only linguistic stimuli (LING) and one with the additional visual context of four potential target objects (VIS). Noun predictability was modulated by verb constraint (ironable vs. describable objects) and thematic fit; and further by visual competitors (two ironable vs. four describable objects). ''The woman (1) irons / (2) describes soon the (a) t-shirt / (b) sock.'' We found lower CL on the noun in (1a) compared to (1b) in both studies, suggesting that after ''iron'', ''t-shirt'' was more predictable, and hence easier to process, than ''sock''. More importantly, VIS findings show higher CL on ''iron'' compared to ''describe'', suggesting that visual context allowed for active exclusion of two non-ironable targets. Conversely, CL on nouns was lower when following ''iron'' than ''describe'', due to only one ironable competitor compared to three describable competitors. These findings suggest that the presence of visual context alters the distribution of CL during sentence processing. Future work includes gaze cues as additional information, potentially further affecting CL distribution.},
pubstate = {published},
type = {inproceedings}
}

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Contexts that constrain upcoming words to some higher or lower extent can be composed differently but are typically all evaluated using cloze-probability (Rayner & Well, 1996). Less predicted words were found to correlate with more negative N400 (e.g., Frank et al., 2015; Kutas & Hillyard, 1984) and longer reading times (Rayner & Well, 1996; Smith & Levy, 2013). Recently, however, it has been suggested that predictability, as in cloze-probability, is only one influence on processing cost (e.g., DeLong et al., 2014). As DeLong et al. show, differences in plausibility of words with similar cloze-probability also affect processing of such words, reflected in different ERP components. This hints at a difference between frequency-based and deeper semantic processing. Moreover, a relatively novel measure, the Index of Cognitive Activity (ICA) capturing pupil jitter, has been linked to cognitive load and predictability (Demberg et al., 2013).

• CUNY2016     (0.06MB)
• https://www.coli.uni-saarland.de/~mirjana/papers/CUNY2016.pdf

@inproceedings{CUNY2016_A5,
title = {Low Predictability: An Empirical Comparison of Paradigms Used for Sentence Comprehension},
author = {Maria Staudte},
url = {https://www.coli.uni-saarland.de/~mirjana/papers/CUNY2016.pdf},
year = {2016},
date = {2016},
booktitle = {29th Annual Conference on Human Sentence Processing (CUNY)},
address = {Gainesville, FL},
abstract = {Contexts that constrain upcoming words to some higher or lower extent can be composed differently but are typically all evaluated using cloze-probability (Rayner & Well, 1996). Less predicted words were found to correlate with more negative N400 (e.g., Frank et al., 2015; Kutas & Hillyard, 1984) and longer reading times (Rayner & Well, 1996; Smith & Levy, 2013). Recently, however, it has been suggested that predictability, as in cloze-probability, is only one influence on processing cost (e.g., DeLong et al., 2014). As DeLong et al. show, differences in plausibility of words with similar cloze-probability also affect processing of such words, reflected in different ERP components. This hints at a difference between frequency-based and deeper semantic processing. Moreover, a relatively novel measure, the Index of Cognitive Activity (ICA) capturing pupil jitter, has been linked to cognitive load and predictability (Demberg et al., 2013).},
pubstate = {published},
type = {inproceedings}
}

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• http://www.frontiersin.org/language_sciences/10.3389/fpsyg.2016.00844/abstract
• https://doi.org/10.3389/fpsyg.2016.00844

@article{Zarcone2016,
title = {Salience and attention in surprisal-based accounts of language processing},
author = {Alessandra Zarcone and Marten van Schijndel and Jorrig Vogels and Vera Demberg},
url = {http://www.frontiersin.org/language_sciences/10.3389/fpsyg.2016.00844/abstract},
doi = {https://doi.org/10.3389/fpsyg.2016.00844},
year = {2016},
date = {2016},
journal = {Frontiers in Psychology},
volume = {7},
number = {844},
abstract = {

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A common problem in cognitive modelling is lack of access to accurate broad-coverage models of event-level surprisal. As shown in,
e.g., Bicknell et al. (2010), event-level knowledge does affect human expectations for verbal arguments. For example, the model should be able to predict that mechanics are likely to check tires, while journalists are more likely to check typos. Similarly, we would like to predict what locations are likely for playing football or playing flute in order to estimate the surprisal of actually-encountered locations. Furthermore, such a model can be used to provide a probability distribution over fillers for a thematic role which is not mentioned in the text at all.

To this end, we train two neural network models (an incremental one and a non-incremental one) on large amounts of automatically rolelabelled text. Our models are probabilistic and can handle several roles at once, which also enables them to learn interactions between different role fillers. Evaluation shows a drastic improvement over current state-of-the-art systems on modelling human thematic fit judgements, and we demonstrate via a sentence similarity task that the system learns highly useful embeddings.

• https://www.semanticscholar.org/paper/Event-participant-modelling-with-neural-networks-Tilk-Demberg/d08d663d7795c76bb008f539b1ac7caf8a9ef26c

@inproceedings{Tilk2016,
title = {Event participation modelling with neural networks},
author = {Ottokar Tilk and Vera Demberg and Asad Sayeed and Dietrich Klakow and Stefan Thater},
url = {https://www.semanticscholar.org/paper/Event-participant-modelling-with-neural-networks-Tilk-Demberg/d08d663d7795c76bb008f539b1ac7caf8a9ef26c},
year = {2016},
date = {2016},
publisher = {Conference on Empirical Methods in Natural Language Processing},
address = {Austin, Texas, USA},
abstract = {A common problem in cognitive modelling is lack of access to accurate broad-coverage models of event-level surprisal. As shown in, e.g., Bicknell et al. (2010), event-level knowledge does affect human expectations for verbal arguments. For example, the model should be able to predict that mechanics are likely to check tires, while journalists are more likely to check typos. Similarly, we would like to predict what locations are likely for playing football or playing flute in order to estimate the surprisal of actually-encountered locations. Furthermore, such a model can be used to provide a probability distribution over fillers for a thematic role which is not mentioned in the text at all. To this end, we train two neural network models (an incremental one and a non-incremental one) on large amounts of automatically rolelabelled text. Our models are probabilistic and can handle several roles at once, which also enables them to learn interactions between different role fillers. Evaluation shows a drastic improvement over current state-of-the-art systems on modelling human thematic fit judgements, and we demonstrate via a sentence similarity task that the system learns highly useful embeddings.},
pubstate = {published},
type = {inproceedings}
}

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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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