Abstract
In this paper we introduce a practical first step towards the creation of an automated debate agent: a state-of-the-art recurrent predictive model for predicting debate winners. By having an accurate predictive model, we are able to objectively rate the quality of a statement made at a specific turn in a debate. The model is based on a recurrent neural network architecture with attention, which allows the model to effectively account for the entire debate when making its prediction. Our model achieves state-of-the-art accuracy on a dataset of debate transcripts annotated with audience favorability of the debate teams. Finally, we discuss how future work can leverage our proposed model for the creation of an automated debate agent. We accomplish this by determining the model input that will maximize audience favorability toward a given side of a debate at an arbitrary turn.- Anthology ID:
- D17-1261
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2465–2475
- Language:
- URL:
- https://aclanthology.org/D17-1261
- DOI:
- 10.18653/v1/D17-1261
- Cite (ACL):
- Peter Potash and Anna Rumshisky. 2017. Towards Debate Automation: a Recurrent Model for Predicting Debate Winners. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2465–2475, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Towards Debate Automation: a Recurrent Model for Predicting Debate Winners (Potash & Rumshisky, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D17-1261.pdf