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Multi-level Gated Recurrent Neural Network for dialog act classification

Summary

The paper addresses dialog act (DA) classification: assigning an intention label to each utterance in a conversation. This is hard because many utterances in conversational data are extremely short, such as "Okay" or a laughter marker, and earlier neural models mostly ignored surrounding turns. Li and Wu propose a hierarchical, two-level gated recurrent neural network (GRNN). The lower level reads the words of each utterance and max-pools the GRU outputs into a sentence vector. A small feed-forward network encodes non-textual cues, such as utterance length and the utterance's position in the dialog. The two vectors are concatenated and passed to an upper-level GRNN, which runs across consecutive utterances, so the model can choose which context to keep or forget. Experiments on the Switchboard Dialog Act (SWDA) corpus show that this design beats prior state-of-the-art systems by more than 6 accuracy points. The approach needs little hand-crafted feature engineering. It also shows that modelling conversation context is the main driver of better DA tagging, especially for short, ambiguous turns.

Key points

  • Data: SWDA uses 43 DAMSL-based tags. 41.4% of its utterances have fewer than 5 words, about 61% have fewer than 10, and 3,253 consist of a single word.
  • Best result: The full model (multi-level GRNN+non-textual) reaches 79.37% accuracy, up from the previous best of 73.1%. That is a gain of 6.27 points over Lee and Dernoncourt (2016) and 6.77 points over Shen and Lee (2016).
  • Baselines: CNN scores 68.25, single-level GRNN 69.75 and non-textual FFNN alone 43.60.
  • Effect of non-textual features: Adding them to a single model gives about 2–2.6 points.
  • Effect of context: The upper-level GRNN over utterances matters most. Placing it on top of the CNN yields 77.14, roughly 9 points above the plain CNN, and multi-level GRNN alone yields 77.65.
  • Error analysis: Context lets the final model correctly tag ambiguous short turns like "Okay" as bk (Response Acknowledgement), where the single-level GRNN fails.

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

Topics: Speech act theory

Cited by

8 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 6 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{li2016multi,
  title = {{Multi-level Gated Recurrent Neural Network for dialog act classification}},
  author = {Li, Wei and Wu, Yunfang},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  year = {2016},
  url = {https://aclanthology.org/C16-1185/}
}

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