Mohammad Aliannejadi


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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.


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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