| Challenge: | Existing frameworks for conversational question-answer generation generate a large-scale dataset based on input passages. |
| Approach: | They propose a conversational question-answer generation framework that extracts question-worthy phrases from passages and generates corresponding questions considering previous conversations. |
| Outcome: | The proposed framework improves the quality of synthetic data and can be used for domain adaptation of conversational question answering. |
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| Challenge: | Existing dialog inpainting methods generate ConvQA datasets with low contextual relevance due to insufficient learning of question-answer alignment. |
| Approach: | They propose a dialog inpainting method that generates ConvQA datasets from documents . they propose re-ranking tasks and a framework that generate contextually relevant questions . |
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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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Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions (2022.aacl-short)
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| Challenge: | Conversational question answering (CQA) aims to answer a question based on a given passage and previous conversation. |
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A Framework for Automatic Generation of Spoken Question-Answering Data (2022.findings-emnlp)
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| Challenge: | Existing frameworks to automatically generate a spoken question answering dataset are limited by the amount of spoken text documents available. |
| Approach: | They propose to use QG module to generate questions from text documents, TTS module to convert text documents into spoken form and automatic speech recognition module to transcribe spoken content. |
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Reinforced Dynamic Reasoning for Conversational Question Generation (P19-1)
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| Challenge: | Empirical results on the recently released CoQA dataset demonstrate the effectiveness of our method . large-scale highquality conversational question answering datasets such as CoQA and QuAC can help train models to answer sequential questions. |
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Fluent Response Generation for Conversational Question Answering (2020.acl-main)
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| Challenge: | Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA subtask. |
| Approach: | They propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. |
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Synthetic QA Corpora Generation with Roundtrip Consistency (P19-1)
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| Challenge: | Existing methods for generating synthetic question answering corpora are not suitable for QA, but can be constructed from widely available natural text. |
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Consistency Training by Synthetic Question Generation for Conversational Question Answering (2024.acl-short)
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| Challenge: | Existing methods that use historical information to address user queries in conversational question-answering (CQA) contexts use the gold answers of history instead of the predicted ones. |
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ChainCQG: Flow-Aware Conversational Question Generation (2021.eacl-main)
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| Challenge: | Current datasets for conversational question answering lack realistic, domain-specific training data. |
| Approach: | They propose a model that generates question-answer representations across dialogue turns . they use flow propagation training to improve conversational flow and fluidity . |
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Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting (2023.findings-emnlp)
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| Challenge: | Existing approaches to identifying ambiguous questions as part of a conversation have not addressed this challenge. |
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| Outcome: | The proposed approach outperforms single-task learning baselines on three LIF test sets. |