Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations
Open accessAbstract
We investigate the problem of generating informative questions in information-asymmetric conversations.Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history.We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates.To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions.We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans.
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[99] addresses generating informative questions.
“The problem of generating informative questions is dwelled upon in [99].”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [99]
The [99] experiments on QuAC show that combined informativeness and specificity metrics steer conversation toward rationally relevant questions.
“The experiments are performed on the conversational QuAC dataset and the combined metrics for informativeness and specificity help to direct the conversation towards rationally relevant questions.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [99]
The [99] architecture has two parts: an automatic question generation model and a model measuring informativeness and specificity of the generated question.
“The architecture used in this work consists of two fragments: the automatic question generation model and the informativeness and specificity measurement model for the generated question.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [99]
In [99], informativeness is measured by the extra information in a student's answer that was not in the conversation history.
“The informativeness is measured using what additional information a given answer by the student provides which was not present the history of the conversation so far.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [99]
No existing approach completely solves generating relevant questions for text given a topic, although a few topic-based approaches exist.
“Although there are a few approaches where topic-based questions [99] have been looked at, there is no existing approach that completely solves the problem.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [99]
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- Authors
- Qi, P., Zhang, Y., & Manning, C. D.
- Affiliations
- Stanford University (US)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Published
- 2020
- Pages
- 25-40
- OpenAlex
- W3105144279
- Cited by
- 23
- References
- 62
- Citations by year
- 2023: 6 · 2022: 5 · 2021: 9 · 2020: 3
- FWCI
- 2.20
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Samsung
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- question generation, informativeness, information asymmetry, pragmatic reasoning, reinforcement learning, reward function design, chatbots
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