Proactive User Information Acquisition via Chats on User-Favored Topics (2025.findings-emnlp)
Copied to clipboard
| Challenge: | PIA tasks require a system to acquire user information without making the user feel abrupt while engaging in a chat on a predefined topic. |
| Approach: | They propose a task to acquire user's answers to predefined questions without making the user feel abrupt while engaging in a chat on a predefined topic. |
| Outcome: | The proposed system outperforms LLMs prompted with task instructions in a dataset of 650 PIA chats and shows that it is reasonably accurate. |
Similar Papers
A Textual Dataset for Situated Proactive Response Selection (2023.acl-long)
Copied to clipboard
| Challenge: | Recent data-driven conversational models can return fluent, consistent, and informative responses to many kinds of requests and utterances in task-oriented scenarios. |
| Approach: | They propose a task of proactive response selection based on situational information and a dataset of 1.7k English conversation examples that include situational background information and for each conversation a set of responses. |
| Outcome: | The proposed model can only provide fluent, consistent, and informative responses to a set of 1.7k English conversation examples and is not easy to perform for strong neural models. |
User Willingness-aware Sales Talk Dataset (2025.coling-main)
Copied to clipboard
| Challenge: | Despite the importance of user willingness, to the best of our knowledge, no previous study has addressed the development of automated sales talk dialogue systems that explicitly consider user willingness. |
| Approach: | They developed a user willingness–aware sales talk collection by leveraging the ecological validity concept to elicit natural user willingness. |
| Outcome: | The proposed system elicited user willingness at the utterance level from multiple perspectives and was able to improve the user's intent to purchase. |
Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation (2026.findings-acl)
Copied to clipboard
| Challenge: | a new technology is transforming customer service chatbots into strategic bridges for business intelligence . a recent study shows that customer service bots are increasingly being used as reactive support tools . |
| Approach: | They propose a task of Proactive Information Probing which optimizes when to probe users for pre-specified information while minimizing conversation turns and user friction. |
| Outcome: | The proposed framework outperforms baselines in both information probing and service quality. |
ProTOD: Proactive Task-oriented Dialogue System Based on Large Language Model (2025.coling-main)
Copied to clipboard
| Challenge: | Existing task-oriented dialogue systems engage with users in a reactive manner, relying on a basic single-query mechanism and employing passive policy planning. |
| Approach: | They propose a novel LLM-based proactive TOD framework to improve system proactivity and goal completion. |
| Outcome: | The proposed framework improves system proactivity and goal completion rates by 10% while enhancing proactive engagement. |
Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition (2022.naacl-main)
Copied to clipboard
| Challenge: | a novel AI-empowered chat bot for learning as conversation can be applied to various domains without in-domain dialogue data. |
| Approach: | They propose a novel task where a user does not read a passage but gains information and knowledge through conversation with a teacher bot. |
| Outcome: | The proposed system can be transferred to various domains without in-domain dialogue data and can carry out conversations both informative and attentive to users. |
Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation (2023.emnlp-main)
Copied to clipboard
| 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. |
| Approach: | They propose a dataset curation framework that automatically curations a large-scale personalized dialogue dataset using a role-playing approach. |
| Outcome: | The proposed dataset is of high quality and could contribute to exploring personalized target-oriented dialogue. |
ProDial – An Annotated Proactive Dialogue Act Corpus for Conversational Assistants using Crowdsourcing (2022.lrec-1)
Copied to clipboard
| Challenge: | Especially in the household domain, robots may become indispensable helpers by overtaking tedious tasks, e.g. keeping the place tidy. |
| Approach: | They propose a conversational approach for explicitly collecting personal user information using natural dialogue. |
| Outcome: | The proposed approach is compared to a baseline dialogue strategy for interactive personalization and has shown that it is friendlier. |
TRAVEL: Tag-Aware Conversational FAQ Retrieval via Reinforcement Learning (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods aim to fully utilize the dynamic conversation context to enhance the semantic association between the user query and FAQ questions, but they are limited by noise and e.g., users may click questions they don't like, leading to inaccurate semantics modeling. |
| Approach: | They propose to introduce tags of FAQ questions to reduce noise in the conversation context and integrate them into a reinforcement learning framework to minimize the negative impact of irrelevant information. |
| Outcome: | The proposed method can eliminate irrelevant information and minimize negative impact of irrelevant information in the dynamic conversation context. |
Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations (2020.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | The proposed questioner significantly improves the informativeness and specificity of questions generated over a baseline model as evaluated by metrics as well as humans. |
PsyAdvisor: A Plug-and-Play Strategy Advice Planner with Proactive Questioning in Psychological Conversations (2025.acl-long)
Copied to clipboard
| Challenge: | Current psychological LLMs are constrained by passive response mechanisms, limiting their capacity to deploy proactive strategies for psychological counseling. |
| Approach: | They propose a dataset that provides a multi-turn conversation dataset with interpretive labels including strategy decision logic and reaction attribution. |
| Outcome: | The proposed model significantly improves proactive questioning capacity, conversation depth, and response quality. |