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Hits 161 – 180 of 20.397

161	DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues
	Castilho, Sheila; Cavalheiro Camargo, João Lucas; Menezes, Miguel...
	In: Castilho, Sheila orcid:0000-0002-8416-6555 , Cavalheiro Camargo, João Lucas orcid:0000-0003-3746-1225 , Menezes, Miguel and Way, Andy orcid:0000-0001-5736-5930 (2021) DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues. In: Sixth Conference on Machine Translation (WMT21), 10-11 Nov 2021, Punta Cana, Dominican Republic (Online). ISBN 978-1-954085-94-7 (2021)
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162	English machine reading comprehension: new approaches to answering multiple-choice questions
	Dzendzik, Daria. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021
	In: Dzendzik, Daria (2021) English machine reading comprehension: new approaches to answering multiple-choice questions. PhD thesis, Dublin City University. (2021)
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163	Chinese character decomposition for neural MT with multi-word expressions
	Han, Lifeng; Jones, Gareth J.F.; Smeaton, Alan F....
	In: Han, Lifeng orcid:0000-0002-3221-2185 , Jones, Gareth J.F. orcid:0000-0003-2923-8365 , Smeaton, Alan F. orcid:0000-0003-1028-8389 and Bolzoni, Paolo (2021) Chinese character decomposition for neural MT with multi-word expressions. In: 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021), 31 May- 2 June 2021, Reykjavik, Iceland (Online). (In Press) (2021)
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164	cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations
	Han, Lifeng; Erofeev, Gleb; Gladkoff, Serge...
	In: Erofeev, Gleb, Sorokina, Irina, Han, Lifeng orcid:0000-0002-3221-2185 and Gladkoff, Serge (2021) cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations. In: Machine Translation Summit 2021, 16-20 Aug 2021, USA (online). (In Press) (2021)
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165	Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement
	Han, Lifeng; Alekseeva, Alexandra; Sorokina, Irina...
	In: Alekseeva, Alexandra orcid:0000-0002-7990-4592 , Gladkoff, Serge, Sorokina, Irina and Han, Lifeng orcid:0000-0002-3221-2185 (2021) Monte Carlo modelling of confidence intervals in translation quality evaluation (TQE) and post-editing dstance (PED) measurement. In: Metrics 2021: Workshop on Informetric and Scientometric Research (SIG-MET), 23-24 Oct 2021, Online. (2021)
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166	Meta-evaluation of machine translation evaluation methods
	Han, Lifeng
	In: Han, Lifeng orcid:0000-0002-3221-2185 (2021) Meta-evaluation of machine translation evaluation methods. In: Workshop on Informetric and Scientometric Research (SIG-MET), 23-24 Oct 2021, Online. (2021)
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167	Proactive information retrieval
	Sen, Procheta. - : Dublin City University. School of Computing, 2021. : Dublin City University. ADAPT, 2021
	In: Sen, Procheta (2021) Proactive information retrieval. PhD thesis, Dublin City University. (2021)
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168	Is there a bilingual disadvantage for word segmentation? A computational modeling approach
	Fibla, Laia; Sebastian-Galles, Nuria; Cristia, Alejandrina
	In: ISSN: 0305-0009 ; EISSN: 1469-7602 ; Journal of Child Language ; https://hal.archives-ouvertes.fr/hal-03498905 ; Journal of Child Language, Cambridge University Press (CUP), 2021, pp.1-28. ⟨10.1017/S0305000921000568⟩ (2021)
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169	SCALa: A blueprint for computational models of language acquisition in social context
	Tsuji, Sho; Cristia, Alejandrina; Dupoux, Emmanuel
	In: ISSN: 0010-0277 ; EISSN: 1873-7838 ; Cognition ; https://hal.inria.fr/hal-03373586 ; Cognition, Elsevier, 2021, 213, pp.104779. ⟨10.1016/j.cognition.2021.104779⟩ (2021)
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170	Buzz or Change: How the Social Network Structure Conditions the Fate of Lexical Innovations on Twitter
	Tarrade, Louise; Chevrot, Jean-Pierre; Magué, Jean-Philippe
	In: 8th Conference on CMC and Social Media Corpora for the Humanities (CMC-Corpora 2021) ; https://hal.archives-ouvertes.fr/hal-03426028 ; 8th Conference on CMC and Social Media Corpora for the Humanities (CMC-Corpora 2021), Oct 2021, Nijmegen, Radboud University, Netherlands (2021)
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171	Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics ; Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics: Dagstuhl Seminar 21351
	Baldwin, Timothy; Croft, William; Nivre,, Joakim. - : HAL CCSD, 2021
	In: Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03507948 ; Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics, Aug 2021, pp.89--138, 2021, 2192-5283. ⟨10.4230/DagRep.11.7.89⟩ ; https://gitlab.com/unlid/dagstuhl-seminar/-/wikis/home (2021)
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172	Do Infants Really Learn Phonetic Categories?
	Feldman, Naomi; Goldwater, Sharon; Dupoux, Emmanuel...
	In: EISSN: 2470-2986 ; Open Mind ; https://hal.archives-ouvertes.fr/hal-03550830 ; Open Mind, MIT Press, 2021, 5, pp.113-131. ⟨10.1162/opmi_a_00046⟩ (2021)
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173	Type-logical investigations: proof-theoretic, computational and linguistic aspects of modern type-logical grammars
	Moot, Richard. - : HAL CCSD, 2021
	In: https://hal-lirmm.ccsd.cnrs.fr/tel-03452731 ; Computation and Language [cs.CL]. Université Montpellier, 2021 (2021)
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174	Weak supervision for learning discourse structure in multi-party dialogues ; Supervision distante pour l'apprentissage de structures discursives dans les conversations multi-locuteurs
	Badene, Sonia. - : HAL CCSD, 2021
	In: https://tel.archives-ouvertes.fr/tel-03622653 ; Artificial Intelligence [cs.AI]. Université Paul Sabatier - Toulouse III, 2021. English. ⟨NNT : 2021TOU30138⟩ (2021)
	Abstract: The main objective of this thesis is to improve the automatic capture of semantic information with the goal of modeling and understanding human communication. We have advanced the state of the art in discourse parsing, in particular in the retrieval of discourse structure from chat, in order to implement, at the industrial level, tools to help explore conversations. These include the production of automatic summaries, recommendations, dialogue acts detection, identification of decisions, planning and semantic relations between dialogue acts in order to understand dialogues. In multi-party conversations it is important to not only understand the meaning of a participant's utterance and to whom it is addressed, but also the semantic relations that tie it to other utterances in the conversation and give rise to different conversation threads. An answer must be recognized as an answer to a particular question; an argument, as an argument for or against a proposal under discussion; a disagreement, as the expression of a point of view contrasted with another idea already expressed. Unfortunately, capturing such information using traditional supervised machine learning methods from quality hand-annotated discourse data is costly and time-consuming, and we do not have nearly enough data to train these machine learning models, much less deep learning models. Another problem is that arguably, no amount of data will be sufficient for machine learning models to learn the semantic characteristics of discourse relations without some expert guidance; the data are simply too sparse. Long distance relations, in which an utterance is semantically connected not to the immediately preceding utterance, but to another utterance from further back in the conversation, are particularly difficult and rare, though often central to comprehension. It is therefore necessary to find a more efficient way to retrieve discourse structures from large corpora of multi-party conversations, such as meeting transcripts or chats. This is one goal this thesis achieves. In addition, we not only wanted to design a model that predicts discourse structure for multi-party conversation without requiring large amounts of hand-annotated data, but also to develop an approach that is transparent and explainable so that it can be modified and improved by experts. The method detailed in this thesis achieves this goal as well. ; L'objectif principal de cette thèse est d'améliorer l'inférence automatique pour la modélisation et la compréhension des communications humaines. En particulier, le but est de faciliter considérablement l'analyse du discours afin d'implémenter, au niveau industriel, des outils d'aide à l'exploration des conversations. Il s'agit notamment de la production de résumés automatiques, de recommandations, de la détection des actes de dialogue, de l'identification des décisions, de la planification et des relations sémantiques entre les actes de dialogue afin de comprendre les dialogues. Dans les conversations à plusieurs locuteurs, il est important de comprendre non seulement le sens de l'énoncé d'un locuteur et à qui il s'adresse, mais aussi les relations sémantiques qui le lient aux autres énoncés de la conversation et qui donnent lieu à différents fils de discussion. Une réponse doit être reconnue comme une réponse à une question particulière ; un argument, comme un argument pour ou contre une proposition en cours de discussion ; un désaccord, comme l'expression d'un point de vue contrasté par rapport à une autre idée déjà exprimée. Malheureusement, les données de discours annotées à la main et de qualités sont coûteuses et prennent du temps, et nous sommes loin d'en avoir assez pour entraîner des modèles d'apprentissage automatique traditionnels, et encore moins des modèles d'apprentissage profond. Il est donc nécessaire de trouver un moyen plus efficace d'annoter en structures discursives de grands corpus de conversations multi-locuteurs, tels que les transcriptions de réunions ou les chats. Un autre problème est qu'aucune quantité de données ne sera suffisante pour permettre aux modèles d'apprentissage automatique d'apprendre les caractéristiques sémantiques des relations discursives sans l'aide d'un expert ; les données sont tout simplement trop rares. Les relations de longue distance, dans lesquelles un énoncé est sémantiquement connecté non pas à l'énoncé qui le précède immédiatement, mais à un autre énoncé plus antérieur/tôt dans la conversation, sont particulièrement difficiles et rares, bien que souvent centrales pour la compréhension. Notre objectif dans cette thèse a donc été non seulement de concevoir un modèle qui prédit la structure du discours pour une conversation multipartite sans nécessiter de grandes quantités de données annotées manuellement, mais aussi de développer une approche qui soit transparente et explicable afin qu'elle puisse être modifiée et améliorée par des experts.
	Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; Attachements; Attachment; Computational linguistics; Data programming; Discourse relations; Discourse structure; Linguistique computationnelle; Programmation par les données; Relations discursives; Structure discursive; Supervision distante; Weak supervision
	URL: https://tel.archives-ouvertes.fr/tel-03622653/document https://tel.archives-ouvertes.fr/tel-03622653/file/2021TOU30138b.pdf https://tel.archives-ouvertes.fr/tel-03622653
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175	Functional pressures and linguistic typology
	Meinhardt, Eric. - : eScholarship, University of California, 2021
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176	Arc-Eager Construction Provides Learning Advantage Beyond Stack Management
	Barnett, Phillip A. - : eScholarship, University of California, 2021
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177	Neuro-computational models of language processing
	Hale, John,; Campanelli, Luca; Li, Jixing...
	In: EISSN: 2333-9691 ; Annual Review of Linguistics ; https://hal.archives-ouvertes.fr/hal-03334485 ; Annual Review of Linguistics, Annual Reviews, In press, ⟨10.1146/lingbuzz/006147⟩ (2021)
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178	Handling Heavily Abbreviated Manuscripts: HTR engines vs text normalisation approaches
	Camps, Jean-Baptiste; Vidal-Gorène, Chahan; Vernet, Marguerite
	In: International Conference on Document Analysis and Recognition 2021 ; https://hal-enc.archives-ouvertes.fr/hal-03279602 ; International Conference on Document Analysis and Recognition 2021, 2021, Lausanne, Switzerland. pp.306-316, ⟨10.1007/978-3-030-86159-9_21⟩ (2021)
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179	Machine Learning Meets Natural Language Processing - The Story so Far
	Galanis, Nikolaos-Ioannis; Vafiadis, Panagiotis; Mirzaev, Kostas-Gkouram...
	In: IFIP Advances in Information and Communication Technology ; 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI) ; https://hal.inria.fr/hal-03287671 ; 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.673-686, ⟨10.1007/978-3-030-79150-6_53⟩ (2021)
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180	Manual annotation of occurrences of terms candidates in scientific papers ; Annotation manuelle d'occurrences de candidats termes et écrit scientifique
	Jacquey, Evelyne; Kister, Laurence; Méoni, Simon...
	In: https://hal.archives-ouvertes.fr/hal-02005884 ; 2021 (2021)
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