@inproceedings{yang-etal-2017-character,
title = "Character-level Intra Attention Network for Natural Language Inference",
author = "Yang, Han and
Costa-juss{\`a}, Marta R. and
Fonollosa, Jos{\'e} A. R.",
editor = "Bowman, Samuel and
Goldberg, Yoav and
Hill, Felix and
Lazaridou, Angeliki and
Levy, Omer and
Reichart, Roi and
S{\o}gaard, Anders",
booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5309",
doi = "10.18653/v1/W17-5309",
pages = "46--50",
abstract = "Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.",
}
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<abstract>Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.</abstract>
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%0 Conference Proceedings
%T Character-level Intra Attention Network for Natural Language Inference
%A Yang, Han
%A Costa-jussà, Marta R.
%A Fonollosa, José A. R.
%Y Bowman, Samuel
%Y Goldberg, Yoav
%Y Hill, Felix
%Y Lazaridou, Angeliki
%Y Levy, Omer
%Y Reichart, Roi
%Y Søgaard, Anders
%S Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for NLP
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F yang-etal-2017-character
%X Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.
%R 10.18653/v1/W17-5309
%U https://aclanthology.org/W17-5309
%U https://doi.org/10.18653/v1/W17-5309
%P 46-50
Markdown (Informal)
[Character-level Intra Attention Network for Natural Language Inference](https://aclanthology.org/W17-5309) (Yang et al., RepEval 2017)
ACL