Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling
Open accessAbstract
We study the problem of generating interconnected questions in question-answering style conversations.Compared with previous works which generate questions based on a single sentence (or paragraph), this setting is different in two major aspects: (1) Questions are highly conversational.Almost half of them refer back to conversation history using coreferences.(2) In a coherent conversation, questions have smooth transitions between turns.We propose an end-to-end neural model with coreference alignment and conversation flow modeling.The coreference alignment modeling explicitly aligns coreferent mentions in conversation history with corresponding pronominal references in generated questions, which makes generated questions interconnected to conversation history.The conversation flow modeling builds a coherent conversation by starting questioning on the first few sentences in a text passage and smoothly shifting the focus to later parts.Extensive experiments show that our system outperforms several baselines and can generate highly conversational questions.The code implementation is released at https://github.com/ Evan-Gao/conversaional-QG.
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The [91] model uses a multi-source encoder and an attention-and-copy decoder, and was tested on CoQA against baselines and ablations.
“A multi-source encoder along with a decoder based on attention and copy mechanism is employed for this task.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [91]
A neural-network method in [91] uses coreference alignment and conversational flow so that questions in later turns relate to the conversation history.
“In [91], a neural-network-based approach is proposed which makes use of coreference alignment along with maintaining a conversational flow.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [91]
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- Authors
- Gao, Y., Li, P., King, I., & Lyu, M. R.
- Affiliations
- Chinese University of Hong Kong (HK)
- Tencent (China) (CN)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 4853-4862
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2950970512
- Cited by
- 44
- References
- 28
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 11 · 2022: 5 · 2021: 13 · 2020: 8 · 2019: 3
- FWCI
- 2.77
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Chinese University of Hong Kong (14210717)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- question generation, pronominal reference, conversation history, turn transitions, end-to-end neural models
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