Open IE-Triples Inference - Corpora Development and DNN Architectures

• Autorzy: Víta Martin , Škoda Petr

Identyfikatory

DOI

10.15439/2020F78

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

Natural language inference (NLI) is a well established part of natural language understanding (NLU). This task is usually stated as a 3-way classification of sentence pairs with respect to entailment relation (entailment, neutral, contradiction). In this work, we focus on a derived task of relation inference: we propose a method of transforming a general NLI corpus to an annotated corpus for relation inference that utilizes existing NLI annotations. We subsequently introduce a novel relation inference corpus obtained from a well known SNLI corpus and provide its brief characterization. We investigate several DNN siamese architectures for this task and this particular corresponding corpus. We set several baselines including hypothesis only baseline. Our best architecture achieved 96.92% accuracy.

Słowa kluczowe

EN

inference mechanism; natural language processing; neural net architecture; text analysis

PL

mechanizm wnioskowania; przetwarzanie języka naturalnego; architektura sieci neuronowej; analiza tekstu

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 199--204
• Opis fizyczny: Bibbliogr. 32 poz., il.

Twórcy

autor

Víta Martin

• NLP Centre, Faculty of Informatics, Masaryk University Botanická 68a, 602 00 Brno Czech Republic

autor

Škoda Petr

• Department of Software Engineering, Faculty of Mathematics and Physics, Charles University Malostranské nám. 2/25, 118 25 Prague Czech Republic

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Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 5th International Workshop on Language Technologies and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).