We’ve had this conversation before: A Novel Approach to Measuring Dialog Similarity
Ofer Lavi, Ella Rabinovich, Segev Shlomov, David Boaz, Inbal Ronen, Ateret Anaby Tavor
Abstract
Dialog is a core building block of human natural language interactions. It contains multi-party utterances used to convey information from one party to another in a dynamic and evolving manner. The ability to compare dialogs is beneficial in many real world use cases, such as conversation analytics for contact center calls and virtual agent design. We propose a novel adaptation of the edit distance metric to the scenario of dialog similarity. Our approach takes into account various conversation aspects such as utterance semantics, conversation flow, and the participants. We evaluate this new approach and compare it to existing document similarity measures on two publicly available datasets. The results demonstrate that our method outperforms the other approaches in capturing dialog flow, and is better aligned with the human perception of conversation similarity.- Anthology ID:
- 2021.emnlp-main.89
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1169–1177
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.89
- DOI:
- 10.18653/v1/2021.emnlp-main.89
- Cite (ACL):
- Ofer Lavi, Ella Rabinovich, Segev Shlomov, David Boaz, Inbal Ronen, and Ateret Anaby Tavor. 2021. We’ve had this conversation before: A Novel Approach to Measuring Dialog Similarity. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1169–1177, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- We’ve had this conversation before: A Novel Approach to Measuring Dialog Similarity (Lavi et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.emnlp-main.89.pdf
- Data
- SGD