| 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. |
| Outcome: | The proposed system produces meaningful and effective conversations significantly better than other approaches. |
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| Challenge: | Existing studies on goal-oriented conversational tasks lack planning . prior studies on this topic have focused on generating proactive behavior in agents . |
| Approach: | They propose a task to evaluate whether neural conversational agents have goal-oriented conversation planning abilities. |
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Thoughts to Target: Enhance Planning for Target-driven Conversation (2024.emnlp-main)
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| Challenge: | Empirical results demonstrate that our method significantly improves the planning ability of LLMs, especially in target-driven conversations. |
| Approach: | They propose a two-stage framework to improve the LLMs’ capability in planning conversations towards designated targets by distilling natural language plans from a target-driven conversation corpus and generating new plans with demonstration-guided in-context learning. |
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ChatCRS: Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems (2025.findings-naacl)
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| Challenge: | Experimental results show that ChatCRS improves language quality and informativeness by 17% and proactivity by 27%. |
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Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing methods for target-guided response generation are inconsistent with human judgement ratings. |
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Reinforced Target-driven Conversational Promotion (2023.emnlp-main)
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| Challenge: | Existing conversational recommendation methods focus on acquiring user preferences while ignoring strategic planning for nudging users towards accepting a designated item. |
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Recipes for Building an Open-Domain Chatbot (2021.eacl-main)
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Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Eric Michael Smith, Y-Lan Boureau, Jason Weston
| Challenge: | Existing work shows that scaling models in the number of parameters and the size of the data they are trained on gives improved results, but other factors are important. |
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Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation (2023.emnlp-main)
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| Challenge: | a recent study defines a conversation target from the system side to proactively steer conversations toward predefined targets or accomplish specific system-side goals. |
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Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models (2022.naacl-main)
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| Challenge: | Recent large-scale language models have produced human-like responses in open-domain dialogue systems. |
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Context-Sensitive Generation of Open-Domain Conversational Responses (C18-1)
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| Challenge: | Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses. |
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RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System (2023.eacl-main)
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| Challenge: | Existing systems that control concept transitions in a conversation lack a persona-aware topic transition dataset. |
| Approach: | They propose a persona-aware topic-guiding conversational system that leads the conversation to drift to a set of target concepts depending on the persona of the speaker and the context of the conversation. |
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