| Challenge: | Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses. |
| Approach: | They propose static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses. |
| Outcome: | The proposed model outperforms all baselines on automatic and human evaluation on two public datasets. |
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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 . |
| Outcome: | The proposed model outperforms answer-aware and answer-unaware SOTA baselines significantly . it generates different types of questions with improved fluidity and coreference alignment. |
CoHS-CQG: Context and History Selection for Conversational Question Generation (2022.coling-1)
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| Challenge: | Existing studies focus on single-turn question generation, but few studies have studied the challenges of multiturn QG. |
| Approach: | They propose a two-stage conversational question generation framework that shortens the context and history of the input and calculates relevance scores. |
| Outcome: | The proposed framework achieves state-of-the-art on CoQA in answer-aware and answer-unaware settings. |
Grounding in social media: An approach to building a chit-chat dialogue model (2022.naacl-srw)
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| Challenge: | Existing open-domain dialogue models fail to capture and utilize external knowledge, leading to repetitive or generic responses to unseen utterances. |
| Approach: | They propose to use social media comments to improve the raw conversation ability of open-domain dialogue systems. |
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Plug-and-Play Conversational Models (2020.findings-emnlp)
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| Challenge: | Large conversational models that generate coherent and fluent responses often require large dialogue datasets. |
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Target-Guided Open-Domain Conversation (P19-1)
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| Challenge: | a new study aims to improve opendomain chat systems by integrating goals and strategy into the system. |
| Approach: | They propose a structured approach that introduces coarse-grained keywords to control intended content of system responses and attains smooth conversation transition through turn-level supervised learning. |
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Summary Grounded Conversation Generation (2021.findings-acl)
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| Challenge: | Existing datasets for conversation summarization are small due to the lack of large-scale datasets. |
| Approach: | They propose three approaches to generate summary grounded conversations, and evaluate the generated conversations using automatic measures and human judgements. |
| Outcome: | The proposed models can generate entire conversations with only a summary of a conversation as the input. |
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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Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations (2023.findings-emnlp)
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| Challenge: | Open-domain dialog generates search queries that help obtain relevant knowledge for holding informative conversations. |
| Approach: | They propose to integrate social commonsense reasoning into internet search queries . they use a commonsensible dialog system to establish connections related to the conversation topic . |
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Non-Emotion-Centric Empathetic Dialogue Generation (2025.coling-main)
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| Challenge: | Empathy is a social psychology theory that enables individuals to comprehend each other's experiences and emotions, thereby fostering more intimate interpersonal relationships. |
| Approach: | They propose a framework for empathetic dialogue generation based on contrastive learning and context-sensitive entity and social commonsense that punishes responses with incorrect emotions and improves the quality of emotions. |
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Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation (2022.coling-1)
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| Challenge: | Existing methods for generating open-domain dialogue systems underutilize training data. |
| Approach: | They propose a retrieval-generation training framework that takes advantage of heterogeneous training data by considering them as "evidence" they use BERTScore retrieval framework which gives better qualities of the training data, they show . |
| Outcome: | The proposed method performs well on zero-shot experiments and is more robust to real-world data. |