@inproceedings{fan-etal-2022-sentiment,
title = "Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis",
author = "Fan, Shuai and
Lin, Chen and
Li, Haonan and
Lin, Zhenghao and
Su, Jinsong and
Zhang, Hang and
Gong, Yeyun and
Guo, JIan and
Duan, Nan",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.332",
doi = "10.18653/v1/2022.emnlp-main.332",
pages = "4984--4994",
abstract = "Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information. In this paper, we propose SentiWSP, a novel Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks. The word level pre-training task detects replaced sentiment words, via a generator-discriminator framework, to enhance the PLM{'}s knowledge about sentiment words. The sentence level pre-training task further strengthens the discriminator via a contrastive learning framework, with similar sentences as negative samples, to encode sentiments in a sentence. Extensive experimental results show that SentiWSP achieves new state-of-the-art performance on various sentence-level and aspect-level sentiment classification benchmarks. We have made our code and model publicly available at \url{https://github.com/XMUDM/SentiWSP}.",
}
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<abstract>Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information. In this paper, we propose SentiWSP, a novel Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks. The word level pre-training task detects replaced sentiment words, via a generator-discriminator framework, to enhance the PLM’s knowledge about sentiment words. The sentence level pre-training task further strengthens the discriminator via a contrastive learning framework, with similar sentences as negative samples, to encode sentiments in a sentence. Extensive experimental results show that SentiWSP achieves new state-of-the-art performance on various sentence-level and aspect-level sentiment classification benchmarks. We have made our code and model publicly available at https://github.com/XMUDM/SentiWSP.</abstract>
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%0 Conference Proceedings
%T Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis
%A Fan, Shuai
%A Lin, Chen
%A Li, Haonan
%A Lin, Zhenghao
%A Su, Jinsong
%A Zhang, Hang
%A Gong, Yeyun
%A Guo, JIan
%A Duan, Nan
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F fan-etal-2022-sentiment
%X Most existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information. In this paper, we propose SentiWSP, a novel Sentiment-aware pre-trained language model with combined Word-level and Sentence-level Pre-training tasks. The word level pre-training task detects replaced sentiment words, via a generator-discriminator framework, to enhance the PLM’s knowledge about sentiment words. The sentence level pre-training task further strengthens the discriminator via a contrastive learning framework, with similar sentences as negative samples, to encode sentiments in a sentence. Extensive experimental results show that SentiWSP achieves new state-of-the-art performance on various sentence-level and aspect-level sentiment classification benchmarks. We have made our code and model publicly available at https://github.com/XMUDM/SentiWSP.
%R 10.18653/v1/2022.emnlp-main.332
%U https://aclanthology.org/2022.emnlp-main.332
%U https://doi.org/10.18653/v1/2022.emnlp-main.332
%P 4984-4994
Markdown (Informal)
[Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis](https://aclanthology.org/2022.emnlp-main.332) (Fan et al., EMNLP 2022)
ACL
- Shuai Fan, Chen Lin, Haonan Li, Zhenghao Lin, Jinsong Su, Hang Zhang, Yeyun Gong, JIan Guo, and Nan Duan. 2022. Sentiment-Aware Word and Sentence Level Pre-training for Sentiment Analysis. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4984–4994, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.