@inproceedings{ke-etal-2020-sentilare,
title = "{S}enti{LARE}: Sentiment-Aware Language Representation Learning with Linguistic Knowledge",
author = "Ke, Pei and
Ji, Haozhe and
Liu, Siyang and
Zhu, Xiaoyan and
Huang, Minlie",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.567",
doi = "10.18653/v1/2020.emnlp-main.567",
pages = "6975--6988",
abstract = "Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, which can promote language understanding in NLP tasks. To benefit the downstream tasks in sentiment analysis, we propose a novel language representation model called SentiLARE, which introduces word-level linguistic knowledge including part-of-speech tag and sentiment polarity (inferred from SentiWordNet) into pre-trained models. We first propose a context-aware sentiment attention mechanism to acquire the sentiment polarity of each word with its part-of-speech tag by querying SentiWordNet. Then, we devise a new pre-training task called label-aware masked language model to construct knowledge-aware language representation. Experiments show that SentiLARE obtains new state-of-the-art performance on a variety of sentiment analysis tasks.",
}
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<abstract>Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, which can promote language understanding in NLP tasks. To benefit the downstream tasks in sentiment analysis, we propose a novel language representation model called SentiLARE, which introduces word-level linguistic knowledge including part-of-speech tag and sentiment polarity (inferred from SentiWordNet) into pre-trained models. We first propose a context-aware sentiment attention mechanism to acquire the sentiment polarity of each word with its part-of-speech tag by querying SentiWordNet. Then, we devise a new pre-training task called label-aware masked language model to construct knowledge-aware language representation. Experiments show that SentiLARE obtains new state-of-the-art performance on a variety of sentiment analysis tasks.</abstract>
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%0 Conference Proceedings
%T SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge
%A Ke, Pei
%A Ji, Haozhe
%A Liu, Siyang
%A Zhu, Xiaoyan
%A Huang, Minlie
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ke-etal-2020-sentilare
%X Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, which can promote language understanding in NLP tasks. To benefit the downstream tasks in sentiment analysis, we propose a novel language representation model called SentiLARE, which introduces word-level linguistic knowledge including part-of-speech tag and sentiment polarity (inferred from SentiWordNet) into pre-trained models. We first propose a context-aware sentiment attention mechanism to acquire the sentiment polarity of each word with its part-of-speech tag by querying SentiWordNet. Then, we devise a new pre-training task called label-aware masked language model to construct knowledge-aware language representation. Experiments show that SentiLARE obtains new state-of-the-art performance on a variety of sentiment analysis tasks.
%R 10.18653/v1/2020.emnlp-main.567
%U https://aclanthology.org/2020.emnlp-main.567
%U https://doi.org/10.18653/v1/2020.emnlp-main.567
%P 6975-6988
Markdown (Informal)
[SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge](https://aclanthology.org/2020.emnlp-main.567) (Ke et al., EMNLP 2020)
ACL