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161
DELA Corpus - A Document-Level Corpus Annotated with Context-Related Issues
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
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
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
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
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
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
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
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
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?
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)
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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
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)
Abstract: International audience ; Efforts to understand the brain bases of language face the mapping problem: at what level do linguistic computations and representations connect to human neurobiology? We review one approach to this problem that relies on rigorously defined computational models to specify the links between linguistic features and neural signals. Such tools can be used to estimate linguistic predictions, model linguistic features, or specify a sequence of processing steps that may be quantitatively fit to neural signals collected while participants use language. Progress has been helped by advances in machine learning, attention to linguistically interpretable models, and openly shared datasets that allow researchers to compare and contrast a variety of models. We describe one such dataset in detail in the supplementary materials.
Keyword: [SCCO.COMP]Cognitive science/Computer science; [SCCO.LING]Cognitive science/Linguistics; [SCCO.NEUR]Cognitive science/Neuroscience; brain; computational model; deep learning; lexicon; neurolinguistics; parsing
URL: https://hal.archives-ouvertes.fr/hal-03334485
https://hal.archives-ouvertes.fr/hal-03334485/document
https://hal.archives-ouvertes.fr/hal-03334485/file/haleEtAl_21_Neuro-computa.2.pdf
https://doi.org/10.1146/lingbuzz/006147
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178
Handling Heavily Abbreviated Manuscripts: HTR engines vs text normalisation approaches
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
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
In: https://hal.archives-ouvertes.fr/hal-02005884 ; 2021 (2021)
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