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An investigation of English-Irish machine translation and associated resources
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Dowling, Meghan. - : Dublin City University. School of Computing, 2022. : Dublin City University. ADAPT, 2022
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In: Dowling, Meghan orcid:0000-0003-1637-4923 (2022) An investigation of English-Irish machine translation and associated resources. PhD thesis, Dublin City University. (2022)
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An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
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In: ISSN: 2375-4699 ; EISSN: 2375-4702 ; ACM Transactions on Asian and Low-Resource Language Information Processing ; https://hal.inria.fr/hal-03616853 ; ACM Transactions on Asian and Low-Resource Language Information Processing, ACM, In press, ⟨10.1145/3523179⟩ (2022)
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The contextual logic
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In: https://hal.archives-ouvertes.fr/hal-03195162 ; 2022 (2022)
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Abstract:
The propositional logic Lp is the smallest syntax that formalizes Aristotle's three principles. It is commonly accepted that it is insufficient to capture human reasoning. Many formalisms have been proposed to extend the modeling capabilities of formal languages. The most common approach is to extend or to impoverish the syntax of Lp. We propose, with the contextual logic Lc, to take a different path. It consists in automatically integrating into the set of atomic propositions of the language silent propositions, which we call thoughts. By identifying the formulae, they bring to the formalism a reflexive reasoning capacity. We use it to define a semantic interpretation function of models, which captures the notions of inconsistency and predicate. The contribution of Lc to the family of non-classical formalisms is that it models fallibilistic reasoning (an intelligent agent has no certainty and believes what seems justifiable to him) and perspectivist reasoning (his beliefs are obtained by summing up the beliefs he has from several disjoint perspectives). We illustrate the behavioral properties of the contextual logic by developing at length an example of application. It allows us to present how to use it in the framework of Symbolic Artificial Intelligence. ; La logique propositionnelle Lp est la plus petite syntaxe qui formalise les trois principes d'Aristote. Il est communément admis qu'elle est insuffisante pour capturer le raisonnement humain. De nombreux formalismes ont été proposés pour étendre les capacités de modélisation des langages formels. L’approche la plus souvent étudiée consiste à étendre ou à appauvrir la syntaxe de Lp. Nous proposons, avec la logique contextuelle Lc, d'emprunter une voie différente. Elle consiste à intégrer automatiquement dans l'ensemble des propositions atomiques du langage des propositions silencieuses, que nous appelons des pensées. En identifiant les formules, elles apportent au formalisme une capacité de raisonnement réflexif. Nous l'utilisons pour définir une fonction d’interprétation sémantique sur les modèles, qui capture les notions d'inconsistance et de prédicat. L’apport de Lc à la famille des formalismes non-classiques est qu'elle modélise le raisonnement faillibiliste (un agent intelligent n'a aucune certitude et croit ce qui lui semble justifiable) et le raisonnement perspectiviste (ses croyances sont obtenues en additionnant les croyances qu'il a de plusieurs perspectives disjointes). Nous illustrons les propriétés comportementales de la logique contextuelle en développant longuement un exemple d'application. Il nous permet de présenter comment l'utiliser dans le cadre de l'Intelligence Artificielle Symbolique.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]; [MATH.MATH-LO]Mathematics [math]/Logic [math.LO]; [SCCO.COMP]Cognitive science/Computer science; [SHS.PHIL]Humanities and Social Sciences/Philosophy
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URL: https://hal.archives-ouvertes.fr/hal-03195162
https://hal.archives-ouvertes.fr/hal-03195162v4/document
https://hal.archives-ouvertes.fr/hal-03195162v4/file/The%20contextual%20logic.pdf
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| 4 |
Is Old French tougher to parse?
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In: 20th International Workshop on Treebanks and Linguistic Theories ; https://hal.archives-ouvertes.fr/hal-03506500 ; 20th International Workshop on Treebanks and Linguistic Theories, Mar 2022, Sofia, Bulgaria (2022)
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A Novel Multimodal Approach for Studying the Dynamics of Curiosity in Small Group Learning
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In: https://hal.inria.fr/hal-03536340 ; 2022 (2022)
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Learning and controlling the source-filter representation of speech with a variational autoencoder
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In: https://hal.archives-ouvertes.fr/hal-03650569 ; 2022 (2022)
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| 7 |
Thirty Years of Machine Translation in Language Teaching and Learning: A Review of the Literature
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In: L2 Journal, vol 14, iss 1 (2022)
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| 9 |
Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events.
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In: Nature communications, vol 13, iss 1 (2022)
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Assessing the impact of OCR noise on multilingual event detection over digitised documents
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In: ISSN: 1432-5012 ; EISSN: 1432-1300 ; International Journal on Digital Libraries ; https://hal.archives-ouvertes.fr/hal-03635985 ; International Journal on Digital Libraries, Springer Verlag, 2022, ⟨10.1007/s00799-022-00325-2⟩ (2022)
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| 11 |
Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents
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In: Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II ; https://hal.archives-ouvertes.fr/hal-03635971 ; Matthias Hagen; Suzan Verberne; Craig Macdonald; Christin Seifert; Krisztian Balog; Kjetil Nørvåg; Vinay Setty. Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II, 13186, Springer International Publishing, pp.347-354, 2022, Lecture Notes in Computer Science, 978-3-030-99738-0. ⟨10.1007/978-3-030-99739-7_44⟩ (2022)
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| 12 |
Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios?
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In: Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03527328 ; Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021), Jan 2022, punta cana, Dominican Republic ; https://aclanthology.org/2021.wnut-1.47/ (2022)
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| 13 |
Annotation of Morphological Errors in L2 Russian Corpus Analysis
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In: 21st Annual Second Language Acquisition and Teaching Interdisciplinary Roundtable ; https://hal.archives-ouvertes.fr/hal-03620469 ; 21st Annual Second Language Acquisition and Teaching Interdisciplinary Roundtable, University of Arizona, Feb 2022, Tucson, United States (2022)
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| 14 |
Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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| 16 |
A Methodology for the Comparison of Human Judgments With Metrics for Coreference Resolution
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In: HumEval at ACL ; https://hal.archives-ouvertes.fr/hal-03650294 ; HumEval at ACL, May 2022, Dublin, Ireland ; https://humeval.github.io/ (2022)
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| 17 |
Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues
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In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022)
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| 18 |
The use of MT by undergraduate translation students for different learning tasks
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In: https://hal.archives-ouvertes.fr/hal-03547415 ; 2022 (2022)
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Can machines learn to see without visual databases?
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In: https://hal.archives-ouvertes.fr/hal-03526569 ; 2022 (2022)
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АКТУАЛЬНЫЕ ТЕНДЕНЦИИ ЦИФРОВИЗАЦИИ ИНОЯЗЫЧНОГО ОБУЧЕНИЯ В НЕЯЗЫКОВОМ ВУЗЕ ... : CURRENT TRENDS IN DIGITALIZATION OF FOREIGN LANGUAGE EDUCATION IN A NON-LINGUISTIC UNIVERSITY ...
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