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Composition of Embeddings : Lessons from Statistical Relational Learning
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In: Proceedings of SEM 2019 ; 8th Joint Conference on Lexical and Computational Semantics (SEM 2019) ; https://hal.archives-ouvertes.fr/hal-02397476 ; 8th Joint Conference on Lexical and Computational Semantics (SEM 2019), Jun 2019, Minneapolis, United States. pp.33-43 (2019)
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Mining Discourse Markers for Unsupervised Sentence Representation Learning
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In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2019) ; https://hal.archives-ouvertes.fr/hal-02397473 ; Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL 2019), Jun 2019, Minneapolis, United States. pp.3477-3486 (2019)
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Abstract:
International audience ; Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data – such as discourse markers between sentences – mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as “coincidentally” or “amazingly”. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it’s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; Computational linguistics
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URL: https://hal.archives-ouvertes.fr/hal-02397473/file/sileo_24994.pdf
https://hal.archives-ouvertes.fr/hal-02397473
https://hal.archives-ouvertes.fr/hal-02397473/document
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Système d’ensemble pour la classification de tweets, DEFT 2017
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In: Atelier Défi Fouille de Textes : Analyse d'opinion et langage figuratif dans des tweets en français@ TALN/RECITAL 2017 (DEFT 2017) ; https://hal.archives-ouvertes.fr/hal-03120281 ; Atelier Défi Fouille de Textes : Analyse d'opinion et langage figuratif dans des tweets en français@ TALN/RECITAL 2017 (DEFT 2017), Jun 2017, Orléans, France. pp.27-31 ; http://talnarchives.atala.org/ateliers/2017/DEFT/2.pdf (2017)
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SWIP at QALD-3 : results, criticisms and lesson learned
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In: QALD-3 : Multilingual Question Answering over Linked Data ; 3rd open challenge on Question Answering over Linked Data (QALD 2013) ; https://hal.archives-ouvertes.fr/hal-01193095 ; 3rd open challenge on Question Answering over Linked Data (QALD 2013), Sep 2013, Valencia, Spain. pp. 1-13 (2013)
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