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Modeling Speech Acts in Asynchronous Conversations: A Neural-CRF Approach

What it is about

The article tags sentences of forum and e-mail threads with five speech acts (statement, question, suggestion, polite, response), on two older corpora (TripAdvisor forum, BC3 e-mail) and a new one, QC3, from a community question-answering site (50 threads, two annotators). Step one encodes each sentence with a bidirectional LSTM over word embeddings trained on a large conversational corpus, adapted from labelled meeting data (MRDA) by adversarial domain adaptation. Step two is a pairwise CRF over a graph of the thread's sentences, so that, for example, a suggestion is supported by a question in the first post. Which graph works matters: no links across comments, or a plain chain in time order, help little; links from every comment to the sentences of the first comment, or a fully connected graph, give the best results (accuracy up to 81–89%, macro-F1 64–72 depending on the corpus, against 80–87% / 63–70 for the local model).

Ideas

For Mova

31. Speech acts §9 (how Mova labels acts), 38-dialogue-and-discourse-markers.md §10. Code: prag/src/model.rs (structured perceptron with Viterbi), prag/src/feats.rs, prag/src/eval.rs. Measures: act macro-F1 and accuracy, greedy vs joint decoding, by-document cross-validation.

Topics: Speech act theory

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00339

@article{joty2018modeling,
  title = {{Modeling Speech Acts in Asynchronous Conversations: A Neural-CRF Approach}},
  author = {Joty, Shafiq and Mohiuddin, Tasnim},
  journal = {Computational Linguistics},
  year = {2018},
  doi = {10.1162/coli\_a\_00339},
  url = {https://doi.org/10.1162/coli_a_00339}
}

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