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A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict

Summary

Sarcasm typically arises when the sentiment a sentence states on its surface clashes with the sentiment its factual content implies. For example, praising an exam that falls on one's birthday. Earlier detectors either classify the whole sentence as a single unit or look for contrasting word and phrase pairs. Both approaches lean on explicit sentiment cues and give little weight to the situational part that carries the hidden meaning.

The authors propose a Dual-Channel Framework and implement it as DC-Net, which has four modules:

  • a decomposer, which uses the Wilson et al. (2005) sentiment lexicon to separate sentiment words from the rest of the text;
  • a literal channel, which encodes the sentiment words;
  • an implied channel, which encodes the remaining text;
  • an analyzer, which decides on sarcasm by detecting conflict between the two channels.

Each channel is also supervised with its own sentiment loss. The framework is encoder-agnostic, and DC-Net itself uses Bi-LSTM.

On IAC-V1, IAC-V2 and a Twitter dataset, DC-Net reports state-of-the-art results. Wrapping other models in the framework improves every one of them. This shows that modelling literal and implied sentiment separately is a reusable idea, not a one-off architecture.

Key points

  • Between 88% and 96% of texts in the three benchmarks contain lexicon sentiment words, which supports splitting inputs on these words. If no sentiment word is found, the full text is fed to both channels.
  • The training objective combines three cross-entropy losses: sarcasm prediction, literal sentiment and implied sentiment.
  • Applying the framework as a wrapper raised macro F1 for all tested encoders (AT-LSTM, Bi-LSTM, MIARN, BERT). Gains reach up to 4.8 points for simpler models and up to 2.3 for BERT.
  • A concatenation analyzer outperforms subtraction, reaching F1 66.4 on IAC-V1, 82.1 on IAC-V2 and 76.3 on Tweets, compared with 65.1, 80.7 and 75.2.
  • Stated limitations: the static lexicon-based decomposer does not cover all sarcasm patterns, and sentiment labels derived from the lexicon only approximate the ground truth. Code is available at github.com/yiyi-ict/dual-channel-for-sarcasm.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

12 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 10 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2022.findings-naacl.126 · arXiv 2109.03587

@inproceedings{liu2022dual,
  title = {{A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict}},
  author = {Liu, Yiyi and Wang, Yequan and Sun, Aixin and Meng, Xuying and Li, Jing and Guo, Jiafeng},
  booktitle = {Findings of the Association for Computational Linguistics: NAACL 2022},
  year = {2022},
  doi = {10.18653/v1/2022.findings-naacl.126},
  eprint = {2109.03587},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2022.findings-naacl.126}
}

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