Publications

Kravtchenko, Ekaterina; Demberg, Vera

Informationally redundant utterances elicit pragmatic inferences Inproceedings Forthcoming

Cognition. 2022 May 14, 2022.

Most theories of pragmatics and language processing predict that speakers avoid excessive informational redundancy. Informationally redundant utterances are, however, quite common in natural dialogue. From a comprehension standpoint, it remains unclear how comprehenders interpret these utterances, and whether they make attempts to reconcile the ‚dips‘ in informational utility with expectations of ‚appropriate‘ or ‚rational‘ speaker informativity. We show that informationally redundant (overinformative) utterances can trigger pragmatic inferences that increase utterance utility in line with comprehender expectations. In a series of three studies, we look at utterances which refer to stereotyped event sequences describing common activities (scripts). When comprehenders encounter utterances describing events that can be easily inferred from prior context, they interpret them as signifying that the event conveys new, unstated information (i.e. an event otherwise assumed to be habitual, such as paying the cashier when shopping, is reinterpreted as non-habitual). We call these inferences atypicality inferences. Further, we show that the degree to which these atypicality inferences are triggered depends on the framing of the utterance. In the absence of an exclamation mark or a discourse marker indicating the speaker’s specific intent to communicate the given information, such inferences are far less likely to arise. Overall, the results demonstrate that excessive conceptual redundancy leads to comprehenders revising the conversational common ground, in an effort to accommodate unexpected dips in informational utility.

@inproceedings{Kravtchenko_redundant_2022,
title = {Informationally redundant utterances elicit pragmatic inferences},
author = {Ekaterina Kravtchenko and Vera Demberg},
url = {https://pubmed.ncbi.nlm.nih.gov/35580451/},
doi = {https://doi.org/ 10.1016/j.cognition.2022.105159},
year = {2022},
date = {2022},
booktitle = {Cognition. 2022 May 14},
abstract = {Most theories of pragmatics and language processing predict that speakers avoid excessive informational redundancy. Informationally redundant utterances are, however, quite common in natural dialogue. From a comprehension standpoint, it remains unclear how comprehenders interpret these utterances, and whether they make attempts to reconcile the 'dips' in informational utility with expectations of 'appropriate' or 'rational' speaker informativity. We show that informationally redundant (overinformative) utterances can trigger pragmatic inferences that increase utterance utility in line with comprehender expectations. In a series of three studies, we look at utterances which refer to stereotyped event sequences describing common activities (scripts). When comprehenders encounter utterances describing events that can be easily inferred from prior context, they interpret them as signifying that the event conveys new, unstated information (i.e. an event otherwise assumed to be habitual, such as paying the cashier when shopping, is reinterpreted as non-habitual). We call these inferences atypicality inferences. Further, we show that the degree to which these atypicality inferences are triggered depends on the framing of the utterance. In the absence of an exclamation mark or a discourse marker indicating the speaker's specific intent to communicate the given information, such inferences are far less likely to arise. Overall, the results demonstrate that excessive conceptual redundancy leads to comprehenders revising the conversational common ground, in an effort to accommodate unexpected dips in informational utility.},
keywords = {Accommodation; Context-dependent implicatures; Experimental pragmatics; Psycholinguistics; Redundancy},
pubstate = {forthcoming},
type = {inproceedings}
}

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Project:   A3

Mayn, Alexandra; Demberg, Vera

Pragmatics of Metaphor Revisited: Modeling the Role of Degree and Salience in Metaphor Understanding Inproceedings Forthcoming

LREC 2022, Toronto, Canada, 2022.

@inproceedings{Mayn_2022_of,
title = {Pragmatics of Metaphor Revisited: Modeling the Role of Degree and Salience in Metaphor Understanding},
author = {Alexandra Mayn and Vera Demberg},
url = {https://cognitivesciencesociety.org/cogsci-2022/},
year = {2022},
date = {2022},
publisher = {LREC 2022},
address = {Toronto, Canada},
pubstate = {forthcoming},
type = {inproceedings}
}

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Project:   A3

Kravtchenko, Ekaterina; Demberg, Vera

Modeling atypicality inferences in pragmatic reasoning Inproceedings Forthcoming

CogSci 2022, Toronto, Canada, 2022.

@inproceedings{Kravtchenko_2022_atypicality,
title = {Modeling atypicality inferences in pragmatic reasoning},
author = {Ekaterina Kravtchenko and Vera Demberg},
url = {https://cognitivesciencesociety.org/cogsci-2022/},
year = {2022},
date = {2022},
publisher = {CogSci 2022},
address = {Toronto, Canada},
pubstate = {forthcoming},
type = {inproceedings}
}

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Project:   A3

Donatelli, Lucia; Schmidt, Theresa; Biswas, Debanjali; Köhn, Arne; Zhai, Fangzhou; Koller, Alexander

Aligning Actions Across Recipe Graphs Inproceedings

Proceedings of EMNLP, pp. 6930–6942, 2021.

Recipe texts are an idiosyncratic form of instructional language that pose unique challenges for automatic understanding. One challenge is that a cooking step in one recipe can be explained in another recipe in different words, at a different level of abstraction, or not at all. Previous work has annotated correspondences between recipe instructions at the sentence level, often glossing over important correspondences between cooking steps across recipes. We present a novel and fully-parsed English recipe corpus, ARA (Aligned Recipe Actions), which annotates correspondences between individual actions across similar recipes with the goal of capturing information implicit for accurate recipe understanding. We represent this information in the form of recipe graphs, and we train a neural model for predicting correspondences on ARA. We find that substantial gains in accuracy can be obtained by taking fine-grained structural information about the recipes into account.

@inproceedings{donatelli21:align,
title = {Aligning Actions Across Recipe Graphs},
author = {Lucia Donatelli and Theresa Schmidt and Debanjali Biswas and Arne K{\"o}hn and Fangzhou Zhai and Alexander Koller},
url = {https://aclanthology.org/2021.emnlp-main.554},
year = {2021},
date = {2021},
booktitle = {Proceedings of EMNLP},
pages = {6930–6942},
abstract = {Recipe texts are an idiosyncratic form of instructional language that pose unique challenges for automatic understanding. One challenge is that a cooking step in one recipe can be explained in another recipe in different words, at a different level of abstraction, or not at all. Previous work has annotated correspondences between recipe instructions at the sentence level, often glossing over important correspondences between cooking steps across recipes. We present a novel and fully-parsed English recipe corpus, ARA (Aligned Recipe Actions), which annotates correspondences between individual actions across similar recipes with the goal of capturing information implicit for accurate recipe understanding. We represent this information in the form of recipe graphs, and we train a neural model for predicting correspondences on ARA. We find that substantial gains in accuracy can be obtained by taking fine-grained structural information about the recipes into account.},
pubstate = {published},
type = {inproceedings}
}

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Projects:   A3 A7

Zhai, Fangzhou; Skrjanec, Iza; Koller, Alexander

Script Parsing with Hierarchical Sequence Modelling Inproceedings

Proceedings of *SEM 2021: 10th Joint Conf. on Lexical and Computational Semantics, pp. 195-201, 2021.

Scripts capture commonsense knowledge about everyday activities and their participants. Script knowledge proved useful in a number of NLP tasks, such as referent prediction, discourse classification, and story generation. A crucial step for the exploitation of script knowledge is script parsing, the task of tagging a text with the events and participants from a certain activity. This task is challenging: it requires information both about the ways events and participants are usually uttered in surface language as well as the order in which they occur in the world. We show how to do accurate script parsing with a hierarchical sequence model and transfer learning. Our model improves the state of the art of event parsing by over 16 points F-score and, for the first time, accurately tags script participants.

@inproceedings{zhaiSkrjanecKoller2021,
title = {Script Parsing with Hierarchical Sequence Modelling},
author = {Fangzhou Zhai and Iza Skrjanec and Alexander Koller},
url = {https://aclanthology.org/2021.starsem-1.18},
doi = {https://doi.org/10.18653/v1/2021.starsem-1.18},
year = {2021},
date = {2021},
booktitle = {Proceedings of *SEM 2021: 10th Joint Conf. on Lexical and Computational Semantics},
pages = {195-201},
abstract = {Scripts capture commonsense knowledge about everyday activities and their participants. Script knowledge proved useful in a number of NLP tasks, such as referent prediction, discourse classification, and story generation. A crucial step for the exploitation of script knowledge is script parsing, the task of tagging a text with the events and participants from a certain activity. This task is challenging: it requires information both about the ways events and participants are usually uttered in surface language as well as the order in which they occur in the world. We show how to do accurate script parsing with a hierarchical sequence model and transfer learning. Our model improves the state of the art of event parsing by over 16 points F-score and, for the first time, accurately tags script participants.},
pubstate = {published},
type = {inproceedings}
}

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Project:   A3

Zarcone, Alessandra; Demberg, Vera

Interaction of script knowledge and temporal discourse cues in a visual world study Journal Article

Discourse Processes, Routledge, pp. 1-16, 2021.

There is now a well-established literature showing that people anticipate upcoming concepts and words during language processing. Commonsense knowledge about typical event sequences and verbal selectional preferences can contribute to anticipating what will be mentioned next. We here investigate how temporal discourse connectives (before, after), which signal event ordering along a temporal dimension, modulate predictions for upcoming discourse referents. Our study analyses anticipatory gaze in the visual world and supports the idea that script knowledge, temporal connectives (before eating → menu, appetizer), and the verb’s selectional preferences (order → appetizer) jointly contribute to shaping rapid prediction of event participants.

@article{zarcone2021script,
title = {Interaction of script knowledge and temporal discourse cues in a visual world study},
author = {Alessandra Zarcone and Vera Demberg},
url = {https://doi.org/10.1080/0163853X.2021.1930807},
doi = {https://doi.org/10.1080/0163853X.2021.1930807},
year = {2021},
date = {2021-07-26},
journal = {Discourse Processes},
pages = {1-16},
publisher = {Routledge},
abstract = {There is now a well-established literature showing that people anticipate upcoming concepts and words during language processing. Commonsense knowledge about typical event sequences and verbal selectional preferences can contribute to anticipating what will be mentioned next. We here investigate how temporal discourse connectives (before, after), which signal event ordering along a temporal dimension, modulate predictions for upcoming discourse referents. Our study analyses anticipatory gaze in the visual world and supports the idea that script knowledge, temporal connectives (before eating → menu, appetizer), and the verb’s selectional preferences (order → appetizer) jointly contribute to shaping rapid prediction of event participants.},
pubstate = {published},
type = {article}
}

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Project:   A3

Zarcone, Alessandra; Demberg, Vera

A bathtub by any other name: the reduction of german compounds in predictive contexts Inproceedings Forthcoming

Proceedings of the 43rd Annual Meeting of the Cognitive Science Society (CogSci), 2021.

@inproceedings{Zarcone2021,
title = {A bathtub by any other name: the reduction of german compounds in predictive contexts},
author = {Alessandra Zarcone and Vera Demberg},
year = {2021},
date = {2021},
booktitle = {Proceedings of the 43rd Annual Meeting of the Cognitive Science Society (CogSci)},
pubstate = {forthcoming},
type = {inproceedings}
}

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Project:   A3

Ryzhova, Margarita; Demberg, Vera

Processing particularized pragmatic inferences under load Inproceedings

Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020), 2020.

A long-standing question in language understanding is whether pragmatic inferences are effortful or whether they happen seamlessly without measurable cognitive effort. We here measure the strength of particularized pragmatic inferences in a setting with high vs. low cognitive load. Cognitive load is induced by a secondary dot tracking task.

If this type of pragmatic inference comes at no cognitive processing cost, inferences should be similarly strong in both the high and the low load condition. If they are effortful, we expect a smaller effect size in the dual tasking condition. Our results show that participants who have difficulty in dual tasking (as evidenced by incorrect answers to comprehension questions) exhibit a smaller pragmatic effect when they were distracted with a secondary task in comparison to the single task condition. This finding supports the idea that pragmatic inferences are effortful.

@inproceedings{Ryzhova2020,
title = {Processing particularized pragmatic inferences under load},
author = {Margarita Ryzhova and Vera Demberg},
url = {https://cognitivesciencesociety.org/cogsci-2020/},
year = {2020},
date = {2020-10-17},
booktitle = {Proceedings of the 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020)},
abstract = {A long-standing question in language understanding is whether pragmatic inferences are effortful or whether they happen seamlessly without measurable cognitive effort. We here measure the strength of particularized pragmatic inferences in a setting with high vs. low cognitive load. Cognitive load is induced by a secondary dot tracking task. If this type of pragmatic inference comes at no cognitive processing cost, inferences should be similarly strong in both the high and the low load condition. If they are effortful, we expect a smaller effect size in the dual tasking condition. Our results show that participants who have difficulty in dual tasking (as evidenced by incorrect answers to comprehension questions) exhibit a smaller pragmatic effect when they were distracted with a secondary task in comparison to the single task condition. This finding supports the idea that pragmatic inferences are effortful.},
pubstate = {published},
type = {inproceedings}
}

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Project:   A3

Zhai, Fangzhou; Demberg, Vera; Koller, Alexander

Story Generation with Rich Details Inproceedings

Proceedings of the 28th International Conference on Computational Linguistics (CoLing 2020), International Committee on Computational Linguistics, pp. 2346-2351, Barcelona, Spain (Online), 2020.

Automatically generated stories need to be not only coherent, but also interesting. Apart from realizing a story line, the text also needs to include rich details to engage the readers. We propose a model that features two different generation components: an outliner, which proceeds the main story line to realize global coherence; a detailer, which supplies relevant details to the story in a locally coherent manner. Human evaluations show our model substantially improves the informativeness of generated text while retaining its coherence, outperforming various baselines.

@inproceedings{zhai-etal-2020-story,
title = {Story Generation with Rich Details},
author = {Fangzhou Zhai and Vera Demberg and Alexander Koller},
url = {https://www.aclweb.org/anthology/2020.coling-main.212},
doi = {https://doi.org/10.18653/v1/2020.coling-main.212},
year = {2020},
date = {2020},
booktitle = {Proceedings of the 28th International Conference on Computational Linguistics (CoLing 2020)},
pages = {2346-2351},
publisher = {International Committee on Computational Linguistics},
address = {Barcelona, Spain (Online)},
abstract = {Automatically generated stories need to be not only coherent, but also interesting. Apart from realizing a story line, the text also needs to include rich details to engage the readers. We propose a model that features two different generation components: an outliner, which proceeds the main story line to realize global coherence; a detailer, which supplies relevant details to the story in a locally coherent manner. Human evaluations show our model substantially improves the informativeness of generated text while retaining its coherence, outperforming various baselines.},
pubstate = {published},
type = {inproceedings}
}

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Project:   A3

Ostermann, Simon

Script knowledge for natural language understanding PhD Thesis

Saarland University, Saarbruecken, Germany, 2019.

While people process text, they make frequent use of information that is assumed to be common ground and left implicit in the text. One important type of such commonsense knowledge is script knowledge, which is the knowledge about the events and participants in everyday activities such as visiting a restaurant.

Due to its implicitness, it is hard for machines to exploit such script knowledge for natural language processing (NLP). This dissertation addresses the role of script knowledge in a central field of NLP, natural language understanding (NLU). In the first part of this thesis, we address script parsing. The idea of script parsing is to align event and participant mentions in a text with an underlying script representation. This makes it possible for a system to leverage script knowledge for downstream tasks. We develop the first script parsing model for events that can be trained on a large scale on crowdsourced script data. The model is implemented as a linear-chain conditional random field and trained on sequences of short event descriptions, implicitly exploiting the inherent event ordering information.

We show that this ordering information plays a crucial role for script parsing. Our model provides an important first step towards facilitating the use of script knowledge for NLU. In the second part of the thesis, we move our focus to an actual application in the area of NLU, i.e. machine comprehension. For the first time, we provide data sets for the systematic evaluation of the contribution of script knowledge for machine comprehension. We create MCScript, a corpus of narrations about everyday activities and questions on the texts. By collecting questions based on a scenario rather than a text, we aimed at creating challenging questions which require script knowledge for finding the correct answer. Based on the findings of a shared task carried out with the data set, which indicated that script knowledge is not relevant for good performance on our corpus, we revised the data collection process and created a second version of the data set.

@phdthesis{Ostermann2019,
title = {Script knowledge for natural language understanding},
author = {Simon Ostermann},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:291--ds-313016},
doi = {https://doi.org/10.22028/D291-31301},
year = {2019},
date = {2019},
school = {Saarland University},
address = {Saarbruecken, Germany},
abstract = {While people process text, they make frequent use of information that is assumed to be common ground and left implicit in the text. One important type of such commonsense knowledge is script knowledge, which is the knowledge about the events and participants in everyday activities such as visiting a restaurant. Due to its implicitness, it is hard for machines to exploit such script knowledge for natural language processing (NLP). This dissertation addresses the role of script knowledge in a central field of NLP, natural language understanding (NLU). In the first part of this thesis, we address script parsing. The idea of script parsing is to align event and participant mentions in a text with an underlying script representation. This makes it possible for a system to leverage script knowledge for downstream tasks. We develop the first script parsing model for events that can be trained on a large scale on crowdsourced script data. The model is implemented as a linear-chain conditional random field and trained on sequences of short event descriptions, implicitly exploiting the inherent event ordering information. We show that this ordering information plays a crucial role for script parsing. Our model provides an important first step towards facilitating the use of script knowledge for NLU. In the second part of the thesis, we move our focus to an actual application in the area of NLU, i.e. machine comprehension. For the first time, we provide data sets for the systematic evaluation of the contribution of script knowledge for machine comprehension. We create MCScript, a corpus of narrations about everyday activities and questions on the texts. By collecting questions based on a scenario rather than a text, we aimed at creating challenging questions which require script knowledge for finding the correct answer. Based on the findings of a shared task carried out with the data set, which indicated that script knowledge is not relevant for good performance on our corpus, we revised the data collection process and created a second version of the data set.},
pubstate = {published},
type = {phdthesis}
}

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Project:   A3

Wanzare, Lilian Diana Awuor

Script acquisition: a crowdsourcing and text mining approach PhD Thesis

Saarland University, Saarbruecken, Germany, 2019.

According to Grice’s (1975) theory of pragmatics, people tend to omit basic information when participating in a conversation (or writing a narrative) under the assumption that left out details are already known or can be inferred from commonsense knowledge by the hearer (or reader). Writing and understanding of texts makes particular use of a specific kind of common-sense knowledge, referred to as script knowledge.

Schank and Abelson (1977) proposed Scripts as a model of human knowledge represented in memory that stores the frequent habitual activities, called scenarios, (e.g. eating in a fast food restaurant, etc.), and the different courses of action in those routines.
This thesis addresses measures to provide a sound empirical basis for high-quality script models. We work on three key areas related to script modeling: script knowledge acquisition, script induction and script identification in text. We extend the existing repository of script knowledge bases in two different ways. First, we crowdsource a corpus of 40 scenarios with 100 event sequence descriptions (ESDs) each, thus going beyond the size of previous script collections.

Second, the corpus is enriched with partial alignments of ESDs, done by human annotators. The crowdsourced partial alignments are used as prior knowledge to guide the semi-supervised script-induction algorithm proposed in this dissertation. We further present a semi-supervised clustering approach to induce script structure from crowdsourced descriptions of event sequences by grouping event descriptions into paraphrase sets and inducing their temporal order. The proposed semi-supervised clustering model better handles order variation in scripts and extends script representation formalism, Temporal Script graphs, by incorporating „arbitrary order“ equivalence classes in order to allow for the flexible event order inherent in scripts.

In the third part of this dissertation, we introduce the task of scenario detection, in which we identify references to scripts in narrative texts. We curate a benchmark dataset of annotated narrative texts, with segments labeled according to the scripts they instantiate. The dataset is the first of its kind. The analysis of the annotation shows that one can identify scenario references in text with reasonable reliability. Subsequently, we proposes a benchmark model that automatically segments and identifies text fragments referring to given scenarios. The proposed model achieved promising results, and therefore opens up research on script parsing and wide coverage script acquisition.

@phdthesis{Wanzare2019,
title = {Script acquisition: a crowdsourcing and text mining approach},
author = {Lilian Diana Awuor Wanzare},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:291--ds-301634},
doi = {https://doi.org/http://dx.doi.org/10.22028/D291-30163},
year = {2019},
date = {2019},
school = {Saarland University},
address = {Saarbruecken, Germany},
abstract = {According to Grice’s (1975) theory of pragmatics, people tend to omit basic information when participating in a conversation (or writing a narrative) under the assumption that left out details are already known or can be inferred from commonsense knowledge by the hearer (or reader). Writing and understanding of texts makes particular use of a specific kind of common-sense knowledge, referred to as script knowledge. Schank and Abelson (1977) proposed Scripts as a model of human knowledge represented in memory that stores the frequent habitual activities, called scenarios, (e.g. eating in a fast food restaurant, etc.), and the different courses of action in those routines. This thesis addresses measures to provide a sound empirical basis for high-quality script models. We work on three key areas related to script modeling: script knowledge acquisition, script induction and script identification in text. We extend the existing repository of script knowledge bases in two different ways. First, we crowdsource a corpus of 40 scenarios with 100 event sequence descriptions (ESDs) each, thus going beyond the size of previous script collections. Second, the corpus is enriched with partial alignments of ESDs, done by human annotators. The crowdsourced partial alignments are used as prior knowledge to guide the semi-supervised script-induction algorithm proposed in this dissertation. We further present a semi-supervised clustering approach to induce script structure from crowdsourced descriptions of event sequences by grouping event descriptions into paraphrase sets and inducing their temporal order. The proposed semi-supervised clustering model better handles order variation in scripts and extends script representation formalism, Temporal Script graphs, by incorporating "arbitrary order" equivalence classes in order to allow for the flexible event order inherent in scripts. In the third part of this dissertation, we introduce the task of scenario detection, in which we identify references to scripts in narrative texts. We curate a benchmark dataset of annotated narrative texts, with segments labeled according to the scripts they instantiate. The dataset is the first of its kind. The analysis of the annotation shows that one can identify scenario references in text with reasonable reliability. Subsequently, we proposes a benchmark model that automatically segments and identifies text fragments referring to given scenarios. The proposed model achieved promising results, and therefore opens up research on script parsing and wide coverage script acquisition.},
pubstate = {published},
type = {phdthesis}
}

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Project:   A3

Ostermann, Simon; Roth, Michael; Pinkal, Manfred

MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants Inproceedings

Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (* SEM 2019), pp. 103-117, 2019.

@inproceedings{ostermann2019mcscript2,
title = {MCScript2.0: A Machine Comprehension Corpus Focused on Script Events and Participants},
author = {Simon Ostermann and Michael Roth and Manfred Pinkal},
url = {https://www.aclweb.org/anthology/S19-1012},
year = {2019},
date = {2019-10-17},
booktitle = {Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (* SEM 2019)},
pages = {103-117},
pubstate = {published},
type = {inproceedings}
}

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Project:   A3

Zhai, Fangzhou; Demberg, Vera; Shkadzko, Pavel; Shi, Wei; Sayeed, Asad

A Hybrid Model for Globally Coherent Story Generation Inproceedings

Proceedings of the Second Workshop on Storytelling, Association for Computational Linguistics, pp. 34-45, Florence, IT, 2019.

@inproceedings{Fangzhou2019,
title = {A Hybrid Model for Globally Coherent Story Generation},
author = {Fangzhou Zhai and Vera Demberg and Pavel Shkadzko and Wei Shi and Asad Sayeed},
year = {2019},
date = {2019},
booktitle = {Proceedings of the Second Workshop on Storytelling},
pages = {34-45},
publisher = {Association for Computational Linguistics},
address = {Florence, IT},
pubstate = {published},
type = {inproceedings}
}

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Projects:   A3 B2

Hong, Xudong; Sayeed, Asad; Demberg, Vera

Learning Distributed Event Representations with a Multi-Task Approach Inproceedings

7th Joint Conference on Lexical and Computational Semantics (SEM 2018), New Orleans, USA, 2018.

@inproceedings{Hong2018,
title = {Learning Distributed Event Representations with a Multi-Task Approach},
author = {Xudong Hong and Asad Sayeed and Vera Demberg},
url = {https://www.aclweb.org/anthology/S18-2002/},
doi = {https://doi.org/10.18653/v1/S18-2002},
year = {2018},
date = {2018},
booktitle = {7th Joint Conference on Lexical and Computational Semantics (SEM 2018)},
address = {New Orleans, USA},
pubstate = {published},
type = {inproceedings}
}

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Project:   A3

Ostermann, Simon; Seitz, Hannah; Thater, Stefan; Pinkal, Manfred

Mapping Text to Scripts: An Entailment Study Inproceedings

Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.

@inproceedings{MCScriptb,
title = {Mapping Text to Scripts: An Entailment Study},
author = {Simon Ostermann and Hannah Seitz and Stefan Thater and Manfred Pinkal},
year = {2018},
date = {2018},
booktitle = {Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018)},
address = {Miyazaki, Japan},
pubstate = {published},
type = {inproceedings}
}

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Roth, Michael; Thater, Stefan; Ostermann, Simon; Modi, Ashutosh; Pinkal, Manfred

MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge Inproceedings

Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan, 2018.

@inproceedings{MCScript,
title = {MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge},
author = {Michael Roth and Stefan Thater andSimon Ostermann and Ashutosh Modi and Manfred Pinkal},
year = {2018},
date = {2018-10-17},
booktitle = {Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018)},
address = {Miyazaki, Japan},
pubstate = {published},
type = {inproceedings}
}

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Project:   A3

Roth, Michael; Thater, Stefan; Ostermann, Simon; Modi, Ashutosh; Pinkal, Manfred

SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge Inproceedings

Proceedings of International Workshop on Semantic Evaluation, New Orleans, LA, USA, 2018.

@inproceedings{SemEval2018Task11,
title = {SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge},
author = {Michael Roth and Stefan Thater andSimon Ostermann and Ashutosh Modi and Manfred Pinkal},
year = {2018},
date = {2018-10-17},
booktitle = {Proceedings of International Workshop on Semantic Evaluation},
address = {New Orleans, LA, USA},
pubstate = {published},
type = {inproceedings}
}

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Roth, Michael; Thater, Stefan; Ostermann, Simon; Pinkal, Manfred

Aligning Script Events with Narrative Texts Inproceedings

Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), Association for Computational Linguistics, Vancouver, Canada, 2017.

Script knowledge plays a central role in text understanding and is relevant for a variety of downstream tasks. In this paper, we consider two recent datasets which provide a rich and general representation of script events in terms of paraphrase sets.

We introduce the task of mapping event mentions in narrative texts to such script event types, and present a model for this task that exploits rich linguistic representations as well as information on temporal ordering. The results of our experiments demonstrate that this complex task is indeed feasible.

@inproceedings{ostermann-EtAl:2017:starSEM,
title = {Aligning Script Events with Narrative Texts},
author = {Michael Roth and Stefan Thater andSimon Ostermann and Manfred Pinkal},
url = {http://www.aclweb.org/anthology/S17-1016},
year = {2017},
date = {2017-10-17},
booktitle = {Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)},
publisher = {Association for Computational Linguistics},
address = {Vancouver, Canada},
abstract = {Script knowledge plays a central role in text understanding and is relevant for a variety of downstream tasks. In this paper, we consider two recent datasets which provide a rich and general representation of script events in terms of paraphrase sets. We introduce the task of mapping event mentions in narrative texts to such script event types, and present a model for this task that exploits rich linguistic representations as well as information on temporal ordering. The results of our experiments demonstrate that this complex task is indeed feasible.},
pubstate = {published},
type = {inproceedings}
}

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Project:   A3

Nguyen, Dai Quoc; Nguyen, Dat Quoc; Modi, Ashutosh; Thater, Stefan; Pinkal, Manfred

A Mixture Model for Learning Multi-Sense Word Embeddings Inproceedings

Association for Computational Linguistics, pp. 121-127, Vancouver, Canada, 2017.

Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings.

Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.

@inproceedings{nguyen-EtAl:2017:starSEM,
title = {A Mixture Model for Learning Multi-Sense Word Embeddings},
author = {Dai Quoc Nguyen and Dat Quoc Nguyen and Ashutosh Modi and Stefan Thater and Manfred Pinkal},
url = {http://www.aclweb.org/anthology/S17-1015},
year = {2017},
date = {2017},
pages = {121-127},
publisher = {Association for Computational Linguistics},
address = {Vancouver, Canada},
abstract = {Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The experimental results show that our model outperforms previous models on standard evaluation tasks.},
pubstate = {published},
type = {inproceedings}
}

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Projects:   A2 A3

Nguyen, Dai Quoc; Nguyen, Dat Quoc; Chu, Cuong Xuan; Thater, Stefan; Pinkal, Manfred

Sequence to Sequence Learning for Event Prediction Inproceedings

Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Asian Federation of Natural Language Processing, pp. 37-42, Taipei, Taiwan, 2017.

This paper presents an approach to the task of predicting an event description from a preceding sentence in a text. Our approach explores sequence-to-sequence learning using a bidirectional multi-layer recurrent neural network. Our approach substantially outperforms previous work in terms of the BLEU score on two datasets derived from WikiHow and DeScript respectively.

Since the BLEU score is not easy to interpret as a measure of event prediction, we complement our study with a second evaluation that exploits the rich linguistic annotation of gold paraphrase sets of events.

@inproceedings{nguyen-EtAl:2017:I17-2,
title = {Sequence to Sequence Learning for Event Prediction},
author = {Dai Quoc Nguyen and Dat Quoc Nguyen and Cuong Xuan Chu and Stefan Thater and Manfred Pinkal},
url = {http://www.aclweb.org/anthology/I17-2007},
year = {2017},
date = {2017-10-17},
booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
pages = {37-42},
publisher = {Asian Federation of Natural Language Processing},
address = {Taipei, Taiwan},
abstract = {This paper presents an approach to the task of predicting an event description from a preceding sentence in a text. Our approach explores sequence-to-sequence learning using a bidirectional multi-layer recurrent neural network. Our approach substantially outperforms previous work in terms of the BLEU score on two datasets derived from WikiHow and DeScript respectively. Since the BLEU score is not easy to interpret as a measure of event prediction, we complement our study with a second evaluation that exploits the rich linguistic annotation of gold paraphrase sets of events.},
pubstate = {published},
type = {inproceedings}
}

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Projects:   A3 A2

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