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Sarcasm Analysis Using Conversation Context

Summary

Most sarcasm detectors judge a message on its own. Yet the ironic intent of a reply often becomes clear only when you see what it answers. This work treats the current message (C TURN) as the unit to label. It uses the preceding message (P TURN) and, when available, the following reply (S TURN) as conversational context. The authors compare SVM classifiers with linguistically motivated features against several LSTM architectures that encode both the context and the current turn. These include a conditional LSTM and models with sentence-level attention. Experiments cover Twitter and two discussion-forum corpora, IAC v2 and Reddit, which differ in post length and in how gold labels were produced. The context-aware LSTMs outperform an LSTM that sees only the current turn, with the prior turn being especially useful. The authors also analyse attention weights qualitatively and compare them with crowdsourced human judgments. The judgments cover which context sentences provoke the sarcasm and which sentence of a reply carries it. The study argues that context modelling matters for reading speaker intent in online dialogue.

Key points

  • Three research questions:
    • Whether context helps classification.
    • Which part of the P TURN triggers a sarcastic reply.
    • Which sentence in a multi-sentence sarcastic C TURN is the sarcastic one.
  • Data and labels:
    • Twitter and Reddit are self-labeled by their authors.
    • IAC v2 (Oraby et al. 2016) is labeled through crowdsourcing, so its labels reflect perceived rather than intended sarcasm.
    • A cross-corpus experiment trains on Reddit and tests on IAC v2.
  • Models:
    • The baselines are SVMs with discrete features.
    • The neural models are LSTM variants: a conditional LSTM (Rocktäschel et al. 2016) and LSTMs with sentence-level attention over context and current turn.
    • The models receive as context the preceding turn, the following turn, or a combination of the two.
  • Human comparison: Two crowdsourcing tasks on forum posts asked annotators to:
    • Mark the trigger sentences in the previous post.
    • Pick the single sarcastic sentence in the reply.
  • Resources: The experiments use Theano. Data and code are released on GitHub (debanjanghosh/sarcasm_context, Alex-Fabbri/deep_learning_nlp_sarcasm). The paper appeared in Computational Linguistics (2018) under CC BY-NC-ND 4.0.

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

29 papers of our library cite it (of the 140673 whose bibliographies we read); 13 of them have a page here, 16 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00336 · arXiv 1808.07531

@article{ghosh2018sarcasm,
  title = {{Sarcasm Analysis Using Conversation Context}},
  author = {Ghosh, Debanjan and Fabbri, Alexander R. and Muresan, Smaranda},
  journal = {Computational Linguistics},
  year = {2018},
  doi = {10.1162/coli\_a\_00336},
  eprint = {1808.07531},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1162/coli_a_00336}
}

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