DSTC7 Task 1: Noetic End-to-End Response Selection
Chulaka Gunasekara, Jonathan K. Kummerfeld, Lazaros Polymenakos, Walter Lasecki
Abstract
Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets. This task provided two new resources that presented different challenges: one was focused but small, while the other was large but diverse. We also considered several new variations on the next utterance selection problem: (1) increasing the number of candidates, (2) including paraphrases, and (3) not including a correct option in the candidate set. Twenty teams participated, developing a range of neural network models, including some that successfully incorporated external data to boost performance. Both datasets have been publicly released, enabling future work to build on these results, working towards robust goal-oriented dialogue systems.- Anthology ID:
- W19-4107
- Volume:
- Proceedings of the First Workshop on NLP for Conversational AI
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Yun-Nung Chen, Tania Bedrax-Weiss, Dilek Hakkani-Tur, Anuj Kumar, Mike Lewis, Thang-Minh Luong, Pei-Hao Su, Tsung-Hsien Wen
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 60–67
- Language:
- URL:
- https://aclanthology.org/W19-4107
- DOI:
- 10.18653/v1/W19-4107
- Cite (ACL):
- Chulaka Gunasekara, Jonathan K. Kummerfeld, Lazaros Polymenakos, and Walter Lasecki. 2019. DSTC7 Task 1: Noetic End-to-End Response Selection. In Proceedings of the First Workshop on NLP for Conversational AI, pages 60–67, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- DSTC7 Task 1: Noetic End-to-End Response Selection (Gunasekara et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W19-4107.pdf
- Data
- Advising Corpus, DSTC7 Task 1