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2017 | OriginalPaper | Chapter

Textual Inference with Tree-Structured LSTM

Authors : Adebayo Kolawole John, Luigi Di Caro, Livio Robaldo, Guido Boella

Published in: BNAIC 2016: Artificial Intelligence

Publisher: Springer International Publishing

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Abstract

Textual Inference is a research trend in Natural Language Processing (NLP) that has recently received a lot of attention by the scientific community. Textual Entailment (TE) is a specific task in Textual Inference that aims at determining whether a hypothesis is entailed by a text. This paper employs the Child-Sum Tree-LSTM for solving the challenging problem of textual entailment. Our approach is simple and able to generalize well without excessive parameter optimization. Evaluation done on SNLI, SICK and other TE datasets shows the competitiveness of our approach.

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Metadata
Title
Textual Inference with Tree-Structured LSTM
Authors
Adebayo Kolawole John
Luigi Di Caro
Livio Robaldo
Guido Boella
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-67468-1_2

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