Exploiting BERT for End-to-End Aspect-based Sentiment Analysis

Xin Li; Lidong Bing; Wenxuan Zhang; Wai Lam

doi:10.18653/v1/D19-5505

Exploiting BERT for End-to-End Aspect-based Sentiment Analysis

Xin Li, Lidong Bing, Wenxuan Zhang, Wai Lam

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Abstract

In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA.

Anthology ID:: D19-5505
Volume:: Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
Month:: November
Year:: 2019
Address:: Hong Kong, China
Editors:: Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:: WNUT
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 34–41
Language:
URL:: https://aclanthology.org/D19-5505/
DOI:: 10.18653/v1/D19-5505
Bibkey:
Cite (ACL):: Xin Li, Lidong Bing, Wenxuan Zhang, and Wai Lam. 2019. Exploiting BERT for End-to-End Aspect-based Sentiment Analysis. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 34–41, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):: Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (Li et al., WNUT 2019)
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PDF:: https://aclanthology.org/D19-5505.pdf

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@inproceedings{li-etal-2019-exploiting,
    title = "Exploiting {BERT} for End-to-End Aspect-based Sentiment Analysis",
    author = "Li, Xin  and
      Bing, Lidong  and
      Zhang, Wenxuan  and
      Lam, Wai",
    editor = "Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim  and
      Rahimi, Afshin",
    booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5505/",
    doi = "10.18653/v1/D19-5505",
    pages = "34--41",
    abstract = "In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA."
}

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%0 Conference Proceedings
%T Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
%A Li, Xin
%A Bing, Lidong
%A Zhang, Wenxuan
%A Lam, Wai
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F li-etal-2019-exploiting
%X In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA.
%R 10.18653/v1/D19-5505
%U https://aclanthology.org/D19-5505/
%U https://doi.org/10.18653/v1/D19-5505
%P 34-41

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Markdown (Informal)

[Exploiting BERT for End-to-End Aspect-based Sentiment Analysis](https://aclanthology.org/D19-5505/) (Li et al., WNUT 2019)

Exploiting BERT for End-to-End Aspect-based Sentiment Analysis (Li et al., WNUT 2019)

ACL

Xin Li, Lidong Bing, Wenxuan Zhang, and Wai Lam. 2019. Exploiting BERT for End-to-End Aspect-based Sentiment Analysis. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 34–41, Hong Kong, China. Association for Computational Linguistics.