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Dialogue Act-based Breakdown Detection in Negotiation Dialogues

Summary

Research on supporting human-human negotiation in natural language is held back by a shortage of corpora. The work that exists relies mostly on DealOrNoDeal (DN) and CraigslistBargain (CB), whose settings are relatively simple. The authors address this in two ways. First, they release the Job Interview (JI) corpus: crowd-sourced English dialogues in which a recruiter and an applicant bargain over several employment issues. Scoring uses per-negotiator preference weights, and two of the issues depend on each other, which makes optimal deals harder to reach. Second, they revisit negotiation breakdown detection, which means predicting from the conversation whether the parties will fail to agree. Their method extracts dialogue acts with rules and feeds the tag sequence to a GRU, so the model sees the flow of the negotiation instead of the wording. On DN and CB this GRU-TAG model performs about as well as text-based models. On JI, where breakdowns are rare, it does better. This suggests that dialogue-act representations carry over across corpora and could help warn negotiators before talks collapse.

Key points

  • JI corpus: 2,639 human-human dialogues, available on GitHub (gucci-j/negotiation-breakdown-detection).
    • Issues negotiated: salary, weekly days off, position, company and workplace. Position and company are interdependent.
    • About 9,920 possible bids per dialogue, compared with 22.5 in DN.
  • Comparison with Konovalov et al. (2016): that corpus is human-agent and has only 105 dialogues. JI offers roughly 11.5× more possible solutions per dialogue.
  • Breakdown ratios: 4.9% in JI, against 23.8% in DN and 18.9% in CB.
    • Text-based GRU models found no breakdowns at all in JI.
    • BERT reached TP ratios of 19.8% (base) and 19.0% (large).
  • Dialogue acts used: <greet>, <inquire>, <propose>, <agree>, <disagree> and <inform>.
    • These features helped only sequential models. LR-BOW with tags failed to detect breakdowns.
  • Ablation study: <agree> and <propose> proved the most informative tags.
  • Error analysis: euphemistic phrasing caused mistakes because dialogue-act extraction is rule-based. The authors see dialogue-act annotation of negotiation corpora as a future direction.

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

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.eacl-main.63

@inproceedings{yamaguchi2021dialogue,
  title = {{Dialogue Act-based Breakdown Detection in Negotiation Dialogues}},
  author = {Yamaguchi, Atsuki and Iwasa, Kosui and Fujita, Katsuhide},
  booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume},
  year = {2021},
  doi = {10.18653/v1/2021.eacl-main.63},
  url = {https://doi.org/10.18653/v1/2021.eacl-main.63}
}

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