ALBERT-BiLSTM for Sequential Metaphor Detection

Shuqun Li; Jingjie Zeng; Jinhui Zhang (张瑾晖); Tao Peng; Liang Yang (杨亮); Hongfei Lin (林鸿飞)

doi:10.18653/v1/2020.figlang-1.17

ALBERT-BiLSTM for Sequential Metaphor Detection

Shuqun Li, Jingjie Zeng, Jinhui Zhang, Tao Peng, Liang Yang, Hongfei Lin

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Abstract

In our daily life, metaphor is a common way of expression. To understand the meaning of a metaphor, we should recognize the metaphor words which play important roles. In the metaphor detection task, we design a sequence labeling model based on ALBERT-LSTM-softmax. By applying this model, we carry out a lot of experiments and compare the experimental results with different processing methods, such as with different input sentences and tokens, or the methods with CRF and softmax. Then, some tricks are adopted to improve the experimental results. Finally, our model achieves a 0.707 F1-score for the all POS subtask and a 0.728 F1-score for the verb subtask on the TOEFL dataset.

Anthology ID:: 2020.figlang-1.17
Volume:: Proceedings of the Second Workshop on Figurative Language Processing
Month:: July
Year:: 2020
Address:: Online
Editors:: Beata Beigman Klebanov, Ekaterina Shutova, Patricia Lichtenstein, Smaranda Muresan, Chee Wee, Anna Feldman, Debanjan Ghosh
Venue:: Fig-Lang
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 110–115
Language:
URL:: https://aclanthology.org/2020.figlang-1.17/
DOI:: 10.18653/v1/2020.figlang-1.17
Bibkey:
Cite (ACL):: Shuqun Li, Jingjie Zeng, Jinhui Zhang, Tao Peng, Liang Yang, and Hongfei Lin. 2020. ALBERT-BiLSTM for Sequential Metaphor Detection. In Proceedings of the Second Workshop on Figurative Language Processing, pages 110–115, Online. Association for Computational Linguistics.
Cite (Informal):: ALBERT-BiLSTM for Sequential Metaphor Detection (Li et al., Fig-Lang 2020)
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PDF:: https://aclanthology.org/2020.figlang-1.17.pdf
Video:: http://slideslive.com/38929718

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@inproceedings{li-etal-2020-albert,
    title = "{ALBERT}-{B}i{LSTM} for Sequential Metaphor Detection",
    author = "Li, Shuqun  and
      Zeng, Jingjie  and
      Zhang, Jinhui  and
      Peng, Tao  and
      Yang, Liang  and
      Lin, Hongfei",
    editor = "Klebanov, Beata Beigman  and
      Shutova, Ekaterina  and
      Lichtenstein, Patricia  and
      Muresan, Smaranda  and
      Wee, Chee  and
      Feldman, Anna  and
      Ghosh, Debanjan",
    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.figlang-1.17/",
    doi = "10.18653/v1/2020.figlang-1.17",
    pages = "110--115",
    abstract = "In our daily life, metaphor is a common way of expression. To understand the meaning of a metaphor, we should recognize the metaphor words which play important roles. In the metaphor detection task, we design a sequence labeling model based on ALBERT-LSTM-softmax. By applying this model, we carry out a lot of experiments and compare the experimental results with different processing methods, such as with different input sentences and tokens, or the methods with CRF and softmax. Then, some tricks are adopted to improve the experimental results. Finally, our model achieves a 0.707 F1-score for the all POS subtask and a 0.728 F1-score for the verb subtask on the TOEFL dataset."
}

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%0 Conference Proceedings
%T ALBERT-BiLSTM for Sequential Metaphor Detection
%A Li, Shuqun
%A Zeng, Jingjie
%A Zhang, Jinhui
%A Peng, Tao
%A Yang, Liang
%A Lin, Hongfei
%Y Klebanov, Beata Beigman
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%Y Feldman, Anna
%Y Ghosh, Debanjan
%S Proceedings of the Second Workshop on Figurative Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-albert
%X In our daily life, metaphor is a common way of expression. To understand the meaning of a metaphor, we should recognize the metaphor words which play important roles. In the metaphor detection task, we design a sequence labeling model based on ALBERT-LSTM-softmax. By applying this model, we carry out a lot of experiments and compare the experimental results with different processing methods, such as with different input sentences and tokens, or the methods with CRF and softmax. Then, some tricks are adopted to improve the experimental results. Finally, our model achieves a 0.707 F1-score for the all POS subtask and a 0.728 F1-score for the verb subtask on the TOEFL dataset.
%R 10.18653/v1/2020.figlang-1.17
%U https://aclanthology.org/2020.figlang-1.17/
%U https://doi.org/10.18653/v1/2020.figlang-1.17
%P 110-115

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Markdown (Informal)

[ALBERT-BiLSTM for Sequential Metaphor Detection](https://aclanthology.org/2020.figlang-1.17/) (Li et al., Fig-Lang 2020)

ALBERT-BiLSTM for Sequential Metaphor Detection (Li et al., Fig-Lang 2020)

ACL

Shuqun Li, Jingjie Zeng, Jinhui Zhang, Tao Peng, Liang Yang, and Hongfei Lin. 2020. ALBERT-BiLSTM for Sequential Metaphor Detection. In Proceedings of the Second Workshop on Figurative Language Processing, pages 110–115, Online. Association for Computational Linguistics.