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Reasoning with Sarcasm by Reading In-Between

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/P18-1093.pdf

Summary

Sarcasm often inverts the apparent polarity of a text, which undermines sentiment analysis and opinion mining. The authors argue that sarcasm usually rests on an internal clash, such as "love" paired with "ignored." Standard recurrent encoders and conventional attention process words sequentially or score them individually, so they never compare word pairs directly.

The paper introduces MIARN (Multi-dimensional Intra-Attention Recurrent Network), which has two parts:

  • Intra-attention branch: scores every pair of words in a sentence, max-pools these affinities row-wise into attention weights, and builds a weighted sentence vector.
  • LSTM branch: captures compositional meaning, such as negation.

Both representations feed the classifier. A single-dimensional variant, SIARN, is also tested.

Across six benchmark datasets sourced from the Internet Argument Corpus, Reddit and Twitter, MIARN achieves state-of-the-art results. The largest gains appear on long debate texts. Its attention maps highlight the contrasting words, making it a step toward interpretable neural sarcasm detection.

Key points

  • The core idea is "looking in-between": the model learns affinities between word pairs to capture contrast and incongruity, instead of attending to tokens one at a time.
  • MIARN beats GRNN and CNN-LSTM-DNN by roughly 8–10% on IAC-V1 and IAC-V2.
  • Compared with a vanilla LSTM, adding intra-attention yields about a 10% gain in F1 and accuracy on long texts, versus a modest 2–3% on short texts.
  • MIARN is usually slightly better than SIARN, with exceptions such as the Riloff et al. (2013) Tweets set.
  • Attention-map analysis on the Ptáček et al. (2014) Tweets data shows a contrast with the baselines. MIARN focuses on "love" and "ignored," whereas ATT-LSTM mostly attends to the final token.

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

55 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 49 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/p18-1093 · arXiv 1805.02856

@inproceedings{tay2018reasoning,
  title = {{Reasoning with Sarcasm by Reading In-Between}},
  author = {Tay, Yi and Luu, Anh Tuan and Hui, Siu Cheung and Su, Jian},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2018},
  doi = {10.18653/v1/p18-1093},
  eprint = {1805.02856},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/p18-1093}
}

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