Representing the Effort in Resolving Ambiguous Scope Inproceedings
Sinn und Bedeutung 20, Tübingen, Germany, 2015.
@inproceedings{SuB2015,
title = {Representing the Effort in Resolving Ambiguous Scope},
author = {Asad Sayeed},
url = {https://ojs.ub.uni-konstanz.de/sub/index.php/sub/article/view/284},
year = {2015},
date = {2015},
booktitle = {Sinn und Bedeutung 20},
address = {T{\"u}bingen, Germany},
abstract = {
Project: B2
A Distributional Account of Discourse Connectives and its Effect on Fine-grained Inferences Inproceedings
Text-link Conference, Louvain, Belgium, 2015.
@inproceedings{asr2015distributionalApproach,
title = {A Distributional Account of Discourse Connectives and its Effect on Fine-grained Inferences},
author = {Fatemeh Torabi Asr and Vera Demberg},
year = {2015},
date = {2015},
booktitle = {Text-link Conference},
address = {Louvain, Belgium},
pubstate = {published},
type = {inproceedings}
}
Project: B2
Inducing Clause-Combining Operations for Natural Language Generation Inproceedings
Proc. of the 1st International Workshop on Data-to-Text Generation, Edinburgh, Scotland, UK, 2015.
@inproceedings{howcroft:white:d2t-2015,
title = {Inducing Clause-Combining Operations for Natural Language Generation},
author = {David M. Howcroft and Michael White},
url = {http://www.macs.hw.ac.uk/InteractionLab/d2t/papers/d2t_HowcroftWhite},
year = {2015},
date = {2015},
booktitle = {Proc. of the 1st International Workshop on Data-to-Text Generation},
address = {Edinburgh, Scotland, UK},
pubstate = {published},
type = {inproceedings}
}
Project: A4
Cognitive Load and Individual Differences in Multitasking Abilities Conference
Workshop on Individual differences in language processing across the adult life span, 2015.
@conference{vogelsjorrig2015cognitive,
title = {Cognitive Load and Individual Differences in Multitasking Abilities},
author = {Jorrig Vogels and Vera Demberg and Jutta Kray},
url = {https://www.bibsonomy.org/bibtex/222f7284011f7023bd8095b6b554278d3/sfb1102},
year = {2015},
date = {2015},
booktitle = {Workshop on Individual differences in language processing across the adult life span},
pubstate = {published},
type = {conference}
}
Project: A4
Underinformative Event Mentions Trigger Context-Dependent Implicatures Inproceedings
Talk presented at Formal and Experimental Pragmatics: Methodological Issues of a Nascent Liaison (MXPRAG), Zentrum für Allgemeine Sprachwissenschaft (ZAS), Berlin, June 2015, 2015.
@inproceedings{Kravtchenko2015b,
title = {Underinformative Event Mentions Trigger Context-Dependent Implicatures},
author = {Ekaterina Kravtchenko and Vera Demberg},
year = {2015},
date = {2015-10-17},
booktitle = {Talk presented at Formal and Experimental Pragmatics: Methodological Issues of a Nascent Liaison (MXPRAG)},
publisher = {Zentrum f{\"u}r Allgemeine Sprachwissenschaft (ZAS), Berlin, June 2015},
pubstate = {published},
type = {inproceedings}
}
Project: A4
Same Same but Different: Type and Typicality in a Distributional Model of Complement Coercion Inproceedings
Word Structure and Word Usage. Proceedings of the NetWordS Final Conference. Pisa, March 30-April 1, 2015, pp. 91-94, Pisa, Italy, 2015.
@inproceedings{zarcone2015same,
title = {Same Same but Different: Type and Typicality in a Distributional Model of Complement Coercion},
author = {Alessandra Zarcone and Sebastian Padó and Alessandro Lenci},
url = {https://www.researchgate.net/publication/282740292_Same_same_but_different_Type_and_typicality_in_a_distributional_model_of_complement_coercion},
year = {2015},
date = {2015},
booktitle = {Word Structure and Word Usage. Proceedings of the NetWordS Final Conference. Pisa, March 30-April 1, 2015},
pages = {91-94},
address = {Pisa, Italy},
abstract = {
Search Challenges in Natural Language Generation with Complex Optimization Objectives Journal Article
KI - Künstliche Intelligenz, Special Issue on Companion Technologies, Springer Berlin Heidelberg, 2015, ISSN 1610-1987.Automatic natural language generation (NLG) is a difficult problem already when merely trying to come up with natural-sounding utterances. Ubiquituous applications, in particular companion technologies, pose the additional challenge of flexible adaptation to a user or a situation. This requires optimizing complex objectives such as information density, in combinatorial search spaces described using declarative input languages. We believe that AI search and planning is a natural match for these problems, and could substantially contribute to solving them effectively. We illustrate this using a concrete example NLG framework, give a summary of the relevant optimization objectives, and provide an initial list of research challenges.
@article{demberg:hoffmann:ki-2015,
title = {Search Challenges in Natural Language Generation with Complex Optimization Objectives},
author = {Vera Demberg and J{\"o}rg Hoffmann and David M. Howcroft and Dietrich Klakow and {\'A}lvaro Torralba},
url = {https://link.springer.com/article/10.1007/s13218-015-0409-5},
year = {2015},
date = {2015},
journal = {KI - K{\"u}nstliche Intelligenz, Special Issue on Companion Technologies},
publisher = {Springer Berlin Heidelberg},
abstract = {
Automatic natural language generation (NLG) is a difficult problem already when merely trying to come up with natural-sounding utterances. Ubiquituous applications, in particular companion technologies, pose the additional challenge of flexible adaptation to a user or a situation. This requires optimizing complex objectives such as information density, in combinatorial search spaces described using declarative input languages. We believe that AI search and planning is a natural match for these problems, and could substantially contribute to solving them effectively. We illustrate this using a concrete example NLG framework, give a summary of the relevant optimization objectives, and provide an initial list of research challenges.
Project: A3
Semantically Underinformative Utterances Trigger Pragmatic Inferences Proceeding
Annual Conference of the Cognitive Science Society (CogSci), Mind, Technology, and Society Pasadena Convention Center, 2015.
@proceeding{kravtchenko:demberg,
title = {Semantically Underinformative Utterances Trigger Pragmatic Inferences},
author = {Ekaterina Kravtchenko and Vera Demberg},
url = {https://www.semanticscholar.org/paper/Semantically-underinformative-utterances-trigger-Kravtchenko-Demberg/33256a5fca918eef5de8998db5a695d9bced5975},
year = {2015},
date = {2015-10-17},
booktitle = {Annual Conference of the Cognitive Science Society (CogSci)},
address = {Mind, Technology, and Society Pasadena Convention Center},
abstract = {
Project: A3
Identifying Actional Features Through Semantic Priming: Cross-Romance Comparison Incollection
Taming the TAME systems. Cahiers Chronos 27, Rodopi, pp. 161-187, Amsterdam/Philadelphia, 2015.
@incollection{batiukova2015identifying,
title = {Identifying Actional Features Through Semantic Priming: Cross-Romance Comparison},
author = {Olga Batiukova and Pier Marco Bertinetto and Alessandro Lenci and Alessandra Zarcone},
url = {https://brill.com/display/book/edcoll/9789004292772/B9789004292772-s010.xml},
year = {2015},
date = {2015},
booktitle = {Taming the TAME systems. Cahiers Chronos 27},
pages = {161-187},
publisher = {Rodopi},
address = {Amsterdam/Philadelphia},
abstract = {
Learning to Predict Script Events from Domain-Specific Text Journal Article
Lexical and Computational Semantics (* SEM 2015), pp. 205-210, 2015.The automatic induction of scripts (Schank and Abelson, 1977) has been the focus of many recent works. In this paper, we employ a variety of these methods to learn Schank and Abelson’s canonical restaurant script, using a novel dataset of restaurant narratives we have compiled from a website called “Dinners from Hell.” Our models learn narrative chains, script-like structures that we evaluate with the “narrative cloze” task (Chambers and Jurafsky, 2008).
@article{rudinger2015learning,
title = {Learning to Predict Script Events from Domain-Specific Text},
author = {Rachel Rudinger and Vera Demberg and Ashutosh Modi and Benjamin Van Durme and Manfred Pinkal},
url = {http://www.aclweb.org/anthology/S15-1024},
year = {2015},
date = {2015},
journal = {Lexical and Computational Semantics (* SEM 2015)},
pages = {205-210},
abstract = {The automatic induction of scripts (Schank and Abelson, 1977) has been the focus of many recent works. In this paper, we employ a variety of these methods to learn Schank and Abelson’s canonical restaurant script, using a novel dataset of restaurant narratives we have compiled from a website called “Dinners from Hell.” Our models learn narrative chains, script-like structures that we evaluate with the “narrative cloze” task (Chambers and Jurafsky, 2008).},
pubstate = {published},
type = {article}
}
Project: A3
Inducing Clause-Combining Rules: A Case Study with the SPaRKy Restaurant Corpus Inproceedings
Proc. of the 15th European Workshop on Natural Language Generation, Association for Computational Linguistics, Brighton, England, UK, 2015.We describe an algorithm for inducing clause-combining rules for use in a traditional natural language generation architecture. An experiment pairing lexicalized text plans from the SPaRKy Restaurant Corpus with logical forms obtained by parsing the corresponding sentences demonstrates that the approach is able to learn clause-combining operations which have essentially the same coverage as those used in the SPaRKy Restaurant Corpus. This paper fills a gap in the literature, showing that it is possible to learn microplanning rules for both aggregation and discourse connective insertion, an important step towards ameliorating the knowledge acquisition bottleneck for NLG systems that produce texts with rich discourse structures using traditional architectures.
@inproceedings{white:howcroft:enlg-2015,
title = {Inducing Clause-Combining Rules: A Case Study with the SPaRKy Restaurant Corpus},
author = {Michael White and David M. Howcroft},
url = {http://www.aclweb.org/anthology/W15-4704},
year = {2015},
date = {2015},
booktitle = {Proc. of the 15th European Workshop on Natural Language Generation},
publisher = {Association for Computational Linguistics},
address = {Brighton, England, UK},
abstract = {We describe an algorithm for inducing clause-combining rules for use in a traditional natural language generation architecture. An experiment pairing lexicalized text plans from the SPaRKy Restaurant Corpus with logical forms obtained by parsing the corresponding sentences demonstrates that the approach is able to learn clause-combining operations which have essentially the same coverage as those used in the SPaRKy Restaurant Corpus. This paper fills a gap in the literature, showing that it is possible to learn microplanning rules for both aggregation and discourse connective insertion, an important step towards ameliorating the knowledge acquisition bottleneck for NLG systems that produce texts with rich discourse structures using traditional architectures.},
pubstate = {published},
type = {inproceedings}
}
Project: A3
ERP indices of referential informativity in visual contexts Inproceedings
Paper presented at the 28th CUNY Conference on Human Sentence Processing, University of South California, Los Angeles, USA, 2015.
@inproceedings{Tourtourietal2015a,
title = {ERP indices of referential informativity in visual contexts},
author = {Elli Tourtouri and Francesca Delogu and Matthew W. Crocker},
url = {https://www.researchgate.net/publication/322570166_ERP_indices_of_referential_informativity_in_visual_contexts},
year = {2015},
date = {2015},
booktitle = {Paper presented at the 28th CUNY Conference on Human Sentence Processing},
publisher = {University of South California},
address = {Los Angeles, USA},
abstract = {
The taming of the data: Using text mining in building a corpus for diachronic analysis Inproceedings
Varieng - From Data to Evidence (d2e), University of Helsinki, 2015.Social and historical linguistic studies benefit from corpora encoding contextual metadata (e.g. time, register, genre) and relevant structural information (e.g. document structure). While small, handcrafted corpora control over selected contextual variables (e.g. the Brown/LOB corpora encoding variety, register, and time) and are readily usable for analysis, big data (e.g. Google or Microsoft n-grams) are typically poorly contextualized and considered of limited value for linguistic analysis (see, however, Lieberman et al. 2007). Similarly, when we compile new corpora, sources may not contain all relevant metadata and structural data (e.g. the Old Bailey sources vs. the richly annotated corpus in Huber 2007).
@inproceedings{Degaetano-etal2015,
title = {The taming of the data: Using text mining in building a corpus for diachronic analysis},
author = {Stefania Degaetano-Ortlieb and Hannah Kermes and Ashraf Khamis and Noam Ordan and Elke Teich},
url = {https://www.ashrafkhamis.com/d2e2015.pdf},
year = {2015},
date = {2015-10-01},
booktitle = {Varieng - From Data to Evidence (d2e)},
address = {University of Helsinki},
abstract = {Social and historical linguistic studies benefit from corpora encoding contextual metadata (e.g. time, register, genre) and relevant structural information (e.g. document structure). While small, handcrafted corpora control over selected contextual variables (e.g. the Brown/LOB corpora encoding variety, register, and time) and are readily usable for analysis, big data (e.g. Google or Microsoft n-grams) are typically poorly contextualized and considered of limited value for linguistic analysis (see, however, Lieberman et al. 2007). Similarly, when we compile new corpora, sources may not contain all relevant metadata and structural data (e.g. the Old Bailey sources vs. the richly annotated corpus in Huber 2007).},
pubstate = {published},
type = {inproceedings}
}
Project: B1
Uniform Information Density at the Level of Discourse Relations: Negation Markers and Discourse Connective Omission Inproceedings
IWCS 2015, pp. 118, 2015.About half of the discourse relations annotated in Penn Discourse Treebank (Prasad et al., 2008) are not explicitly marked using a discourse connective. But we do not have extensive theories of when or why a discourse relation is marked explicitly or when the connective is omitted. Asr and Demberg (2012a) have suggested an information-theoretic perspective according to which discourse connectives are more likely to be omitted when they are marking a relation that is expected or predictable. This account is based on the Uniform Information Density theory (Levy and Jaeger, 2007), which suggests that speakers choose among alternative formulations that are allowed in their language the ones that achieve a roughly uniform rate of information transmission. Optional discourse markers should thus be omitted if they would lead to a trough in information density, and be inserted in order to avoid peaks in information density. We here test this hypothesis by observing how far a specific cue, negation in any form, affects the discourse relations that can be predicted to hold in a text, and how the presence of this cue in turn affects the use of explicit discourse connectives.
@inproceedings{asr2015uniform,
title = {Uniform Information Density at the Level of Discourse Relations: Negation Markers and Discourse Connective Omission},
author = {Fatemeh Torabi Asr and Vera Demberg},
url = {https://www.semanticscholar.org/paper/Uniform-Information-Density-at-the-Level-of-Markers-Asr-Demberg/cee6437e3aba3e772ef8cc7e9aaf3d7ba1114d8b},
year = {2015},
date = {2015},
booktitle = {IWCS 2015},
pages = {118},
abstract = {About half of the discourse relations annotated in Penn Discourse Treebank (Prasad et al., 2008) are not explicitly marked using a discourse connective. But we do not have extensive theories of when or why a discourse relation is marked explicitly or when the connective is omitted. Asr and Demberg (2012a) have suggested an information-theoretic perspective according to which discourse connectives are more likely to be omitted when they are marking a relation that is expected or predictable. This account is based on the Uniform Information Density theory (Levy and Jaeger, 2007), which suggests that speakers choose among alternative formulations that are allowed in their language the ones that achieve a roughly uniform rate of information transmission. Optional discourse markers should thus be omitted if they would lead to a trough in information density, and be inserted in order to avoid peaks in information density. We here test this hypothesis by observing how far a specific cue, negation in any form, affects the discourse relations that can be predicted to hold in a text, and how the presence of this cue in turn affects the use of explicit discourse connectives.},
pubstate = {published},
type = {inproceedings}
}
Project: B2
To What Extent Do We Adapt Spoken Word Durations to a Domain? Inproceedings
Architectures and mechanisms for language processing (AMLaP), Malta, 2015.
@inproceedings{AMLaP2015a,
title = {To What Extent Do We Adapt Spoken Word Durations to a Domain?},
author = {Asad Sayeed and Stefan Fischer and Vera Demberg},
url = {https://www.bibsonomy.org/bibtex/ddebcecc8adb8f40a0abf87294b11a02},
year = {2015},
date = {2015},
booktitle = {Architectures and mechanisms for language processing (AMLaP)},
address = {Malta},
pubstate = {published},
type = {inproceedings}
}
Project: B2
Dimensions of segmental variability: relationships between information density and prosodic structure Inproceedings
Workshop "Modeling variability in speech", Stuttgart, 2015.Contextual predictability variation affects phonological and phonetic structure. Reduction and expansion of acoustic-phonetic features is also characteristic of prosodic variability. In this study, we assess the impact of surprisal and prosodic structure on phonetic encoding, both independently of each other and in interaction. We model segmental duration, vowel space size and spectral characteristics of vowels and consonants as a function of surprisal as well as of syllable prominence, phrase boundary, and speech rate. Correlates of phonetic encoding density are extracted from a subset of the BonnTempo corpus for six languages: American English, Czech, Finnish, French, German, and Polish. Surprisal is estimated from segmental n-gram language models trained on large text corpora. Our findings are generally compatible with a weak version of Aylett and Turk’s Smooth Signal Redundancy hypothesis, suggesting that prosodic structure mediates between the requirements of efficient communication and the speech signal. However, this mediation is not perfect, as we found evidence for additional, direct effects of changes in surprisal on the phonetic structure of utterances. These effects appear to be stable across different speech rates.
@inproceedings{malisz15,
title = {Dimensions of segmental variability: relationships between information density and prosodic structure},
author = {Zofia Malisz and Erika Schulz and Yoon Mi Oh and Bistra Andreeva and Bernd M{\"o}bius},
url = {https://www.frontiersin.org/articles/10.3389/fcomm.2018.00025/full},
year = {2015},
date = {2015},
booktitle = {Workshop "Modeling variability in speech"},
address = {Stuttgart},
abstract = {
Project: C1
A resource for the diachronic study of scientific English: Introducing the Royal Society Corpus Inproceedings
Corpus Linguistics 2015, Lancaster, 2015.
@inproceedings{Khamis-etal2015,
title = {A resource for the diachronic study of scientific English: Introducing the Royal Society Corpus},
author = {Ashraf Khamis and Stefania Degaetano-Ortlieb and Hannah Kermes and J{\"o}rg Knappen and Noam Ordan and Elke Teich},
url = {https://www.researchgate.net/publication/331648570_A_resource_for_the_diachronic_study_of_scientific_English_Introducing_the_Royal_Society_Corpus},
year = {2015},
date = {2015-07-01},
booktitle = {Corpus Linguistics 2015},
address = {Lancaster},
abstract = {
Project: B1
Information Density in Scientific Writing: A Diachronic Perspective Inproceedings
"Challenging Boundaries" - 42nd International Systemic Functional Congress (ISFCW2015), RWTH Aachen University, 2015.
@inproceedings{Degaetano-etal2015b,
title = {Information Density in Scientific Writing: A Diachronic Perspective},
author = {Stefania Degaetano-Ortlieb and Hannah Kermes and Ashraf Khamis and J{\"o}rg Knappen and Elke Teich},
url = {https://www.researchgate.net/publication/331648534_Information_Density_in_Scientific_Writing_A_Diachronic_Perspective},
year = {2015},
date = {2015-07-01},
booktitle = {"Challenging Boundaries" - 42nd International Systemic Functional Congress (ISFCW2015)},
address = {RWTH Aachen University},
abstract = {
Project: B1
Verb Polysemy and Frequency Effects in Thematic Fit Modeling Inproceedings
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics, Association for Computational Linguistics, pp. 48-57, Denver, Colorado, 2015.While several data sets for evaluating thematic fit of verb-role-filler triples exist, they do not control for verb polysemy. Thus, it is unclear how verb polysemy affects human ratings of thematic fit and how best to model that. We present a new dataset of human ratings on high vs. low-polysemy verbs matched for verb frequency, together with high vs. low-frequency and well-fitting vs. poorly-fitting patient rolefillers. Our analyses show that low-polysemy verbs produce stronger thematic fit judgements than verbs with higher polysemy. Rolefiller frequency, on the other hand, had little effect on ratings. We show that these results can best be modeled in a vector space using a clustering technique to create multiple prototype vectors representing different “senses” of the verb.
@inproceedings{greenberg-demberg-sayeed:2015:CMCL,
title = {Verb Polysemy and Frequency Effects in Thematic Fit Modeling},
author = {Clayton Greenberg and Vera Demberg and Asad Sayeed},
url = {http://www.aclweb.org/anthology/W15-1106},
year = {2015},
date = {2015-06-01},
booktitle = {Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics},
pages = {48-57},
publisher = {Association for Computational Linguistics},
address = {Denver, Colorado},
abstract = {While several data sets for evaluating thematic fit of verb-role-filler triples exist, they do not control for verb polysemy. Thus, it is unclear how verb polysemy affects human ratings of thematic fit and how best to model that. We present a new dataset of human ratings on high vs. low-polysemy verbs matched for verb frequency, together with high vs. low-frequency and well-fitting vs. poorly-fitting patient rolefillers. Our analyses show that low-polysemy verbs produce stronger thematic fit judgements than verbs with higher polysemy. Rolefiller frequency, on the other hand, had little effect on ratings. We show that these results can best be modeled in a vector space using a clustering technique to create multiple prototype vectors representing different “senses” of the verb.},
pubstate = {published},
type = {inproceedings}
}
Information Density and Linguistic Encoding (IDeaL) Journal Article
KI - Künstliche Intelligenz, 30, pp. 77-81, 2015.We introduce IDEAL (Information Density and Linguistic Encoding), a collaborative research center that investigates the hypothesis that language use may be driven by the optimal use of the communication channel. From the point of view of linguistics, our approach promises to shed light on selected aspects of language variation that are hitherto not sufficiently explained. Applications of our research can be envisaged in various areas of natural language processing and AI, including machine translation, text generation, speech synthesis and multimodal interfaces.
@article{crocker:demberg:teich,
title = {Information Density and Linguistic Encoding (IDeaL)},
author = {Matthew W. Crocker and Vera Demberg and Elke Teich},
url = {http://link.springer.com/article/10.1007/s13218-015-0391-y/fulltext.html},
doi = {https://doi.org/10.1007/s13218-015-0391-y},
year = {2015},
date = {2015},
journal = {KI - K{\"u}nstliche Intelligenz},
pages = {77-81},
volume = {30},
number = {1},
abstract = {
We introduce IDEAL (Information Density and Linguistic Encoding), a collaborative research center that investigates the hypothesis that language use may be driven by the optimal use of the communication channel. From the point of view of linguistics, our approach promises to shed light on selected aspects of language variation that are hitherto not sufficiently explained. Applications of our research can be envisaged in various areas of natural language processing and AI, including machine translation, text generation, speech synthesis and multimodal interfaces.
pubstate = {published},
type = {article}
}