Mohammad Aliannejadi
2021
Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions
Mohammad Aliannejadi
|
Julia Kiseleva
|
Aleksandr Chuklin
|
Jeff Dalton
|
Mikhail Burtsev
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response. Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer. To address the problem of ‘asking clarifying questions in open-domain dialogues’: (1) we collect and release a new dataset focused on open-domain single- and multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues. These contributions are suitable as a foundation for further research.
2014
Graph-Based Semi-Supervised Conditional Random Fields For Spoken Language Understanding Using Unaligned Data
Mohammad Aliannejadi
|
Masoud Kiaeeha
|
Shahram Khadivi
|
Saeed Shiry Ghidary
Proceedings of the Australasian Language Technology Association Workshop 2014
Search
Co-authors
- Julia Kiseleva 1
- Aleksandr Chuklin 1
- Jeff Dalton 1
- Mikhail Burtsev 1
- Masoud Kiaeeha 1
- show all...