Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation (D19-1)
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| Challenge: | Existing methods for question generation suffer from dullness and deviation problem, which can lead to deviated or dull questions. |
| Approach: | They propose two methods to enhance semantic coherence between question and answer by using a coherent score and adversarial training to explicitly control question generation. |
| Outcome: | The proposed methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions. |
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Answer-focused and Position-aware Neural Question Generation (D18-1)
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| Challenge: | Recent neural network-based approaches generate interrogative words that do not match the answer type. |
| Approach: | They propose an answer-focused and position-aware neural question generation model to address these issues. |
| Outcome: | The proposed model outperforms the baseline and outperformed the state-of-the-art system. |
Towards Answer-unaware Conversational Question Generation (D19-58)
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| Challenge: | Existing frameworks for conversational question generation are answeraware, but are not able to generate corresponding answers . a number of question generation methods are developed for text-based question answering . |
| Approach: | They propose a framework for conversational question generation that is unaware of the corresponding answers. |
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Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations (2020.findings-emnlp)
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| Challenge: | Existing work on question generation assumes knowledge of what the answer might be . instead, questioner must reason pragmatically about how to acquire new information . |
| Approach: | They propose a question generation system that generates pragmatically relevant questions in information-asymmetric conversations. |
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Answer-based Adversarial Training for Generating Clarification Questions (N19-1)
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| Challenge: | a goal of natural language processing is to develop techniques that enable machines to process naturally occurring language. |
| Approach: | They propose a model where hypothetical answers are latent variables that can guide the model into generating more useful clarification questions. |
| Outcome: | The proposed model outperforms retrieval-based models and ablations that exclude utility model and adversarial training on two datasets. |
GTM: A Generative Triple-wise Model for Conversational Question Generation (2021.acl-long)
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| Challenge: | Experimental results show that opendomain conversational question generation improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines. |
| Approach: | They propose a triple-wise model with hierarchical variations for open-domain conversational question generation using a post-question-answer triple and one-to-many semantic mappings. |
| Outcome: | The proposed model significantly improves the quality of questions in terms of fluency, coherence and diversity over baselines. |
Answer-driven Deep Question Generation based on Reinforcement Learning (2020.coling-main)
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| Challenge: | Existing methods for deep question generation focus on enhancing document representations, but little attention is paid to the answer information. |
| Approach: | They propose a deep question generation model that makes better use of the target answer as a guidance to facilitate question generation. |
| Outcome: | The proposed model outperforms state-of-the-art models in automatic and human evaluations on the hotpotQA dataset. |
Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions (2021.emnlp-main)
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| Challenge: | Recent advances on neural approaches to natural language processing have triggered a renaissance in end-to-end neural open-domain chatbots. |
| Approach: | They propose to use offline and online steps to evaluate the quality of clarifying questions in various open-domain dialogues to improve the quality and accuracy of the system response. |
| Outcome: | The proposed pipeline is suitable as a foundation for further research. |
Controlling Dialogue Generation with Semantic Exemplars (2021.naacl-main)
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| Challenge: | Existing methods to control dialogue generation are manual labelling and manual editing of data. |
| Approach: | They propose a method to control dialogue generation using exemplar responses . they use semantic frames present in exemplars to guide response generation . |
| Outcome: | The proposed model improves coherence while preserving semantic meaning and conversation goals . exemplar responses are handwritten or strategically curated to promote highlevel goals without explicit labels . |
I already said that! Degenerating redundant questions in open-domain dialogue systems. (2023.acl-srw)
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| Challenge: | Neural text generation models have been successful in short open-domain conversations, but their performance degrades significantly in the long term. |
| Approach: | They propose a method to generate training data without crowdsourcing . they adapt negative training, decoding, and classification methods to mitigate redundancy problem . |
| Outcome: | The proposed method reduces the rate of redundant questions from 27.2% to 8.7% while improving the quality of the original model. |
Ask To The Point: Open-Domain Entity-Centric Question Generation (2023.findings-emnlp)
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| Challenge: | *entity-centric question generation (ECQG) is a task motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. |
| Approach: | They propose a PLM-based framework GenCONE with two modules: content focusing and question verification. |
| Outcome: | The proposed framework outperforms baselines and is effective and complementary in generating high-quality questions. |