Papers with multi-turn conversation
PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking (D19-3)
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Matthew Henderson, Ivan Vulić, Iñigo Casanueva, Paweł Budzianowski, Daniela Gerz, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, Pei-Hao Su
| Challenge: | a task-oriented dialogue system is based on task-specific ontologies that constrain slots to specific values . we present a conversational search engine that can be used to search for restaurant reservations . |
| Approach: | They propose a conversational search engine that supports task-oriented dialogue . the polyresponse engine is trained on hundreds of millions of examples extracted from real conversations . |
| Outcome: | The proposed system is available in 8 different languages. |
CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling (2022.coling-1)
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| Challenge: | Existing methods to determine a goal item by sequentially tracking users’ interests ignore the rich goal-aware implicit interest sequence patterns in a dialog. |
| Approach: | They propose to model goal-aware implicit user interest sequence patterns in a dialog and a hierarchical Star Transformer to guide multi-turn utterances generation. |
| Outcome: | The proposed framework achieves more accurate recommendations with more fluent and coherent dialog utterances. |
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension (2022.coling-1)
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| Challenge: | Existing methods require three steps to understand text, but span extraction and question rephrasing steps are not fully exploited. |
| Approach: | They propose a framework for conversational machine reading comprehension based on shared parameter mechanism . experimental results show the proposed framework achieves new state-of-the-art results on the ShARC leaderboard . |
| Outcome: | The proposed framework achieves state-of-the-art on the ShARC leaderboard with the BLEU-4 score of 55.2. |
TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph (2022.coling-1)
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| Challenge: | Existing target-oriented dialogs take a local and greedy strategy for response generation, where global planning is absent. |
| Approach: | They propose a global planning method for target-oriented dialog on a commonsense knowledge graph to adjust local response generation towards the global target. |
| Outcome: | The proposed method can reach the target with a higher success rate, fewer turns, and more coherent responses. |
PEARL: Preference Extraction with Exemplar Augmentation and Retrieval with LLM Agents (2024.emnlp-industry)
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| Challenge: | Existing systems specialize in extracting customer preferences from standalone queries . absence of a conversational interface often leaves customers feeling the need for humanlike assistance . |
| Approach: | They propose a shopping assistant chatbot that extracts customer preferences as key-value filters from a multi-turn conversation on an e-commerce website. |
| Outcome: | The proposed solution improves performance on exact match by 10% compared to baselines and improves inference latency by 1%. |
Constructing Multi-Modal Dialogue Dataset by Replacing Text with Semantically Relevant Images (2021.acl-short)
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| Challenge: | Existing training methods for multi-modal dialogue systems rely on image captioning or visual question answering datasets that are irrelevant to the dialogue context. |
| Approach: | They propose to create a 45k multi-modal dialogue dataset with minimal human intervention . they use text dialogue datasets, image-mixed dialogues and contextual-similarity filtering . |
| Outcome: | The proposed dataset can be used as training data for multi-modal dialogue systems . human evaluations show that the model can be effectively used . |
Be Helpful but Don’t Talk too Much - Enhancing Helpfulness in Conversations through Relevance in Multi-Turn Emotional Support (2024.emnlp-main)
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| Challenge: | a helpful speaker should maintain an "effect-effort" tradeoff for a conversation to help and support . a study aimed to cultivate the awareness of "optimal relevance" into the cognitive process of conversation agents . |
| Approach: | They integrate the "Cognitive Relevance Principle" into emotional support agents . they found that the "relevance principle" is effective in generating human-like, helpful, harmless conversations . |
| Outcome: | The proposed method improves human-likedness and support in multi-turn conversations . the source code will be available at https://github.com/CN-Eyetk/VLESA-ORL.git . |
RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark (2026.findings-acl)
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| Challenge: | Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both. |
| Approach: | They propose a framework that transforms complex queries into fact-grounded multi-turn dialogues through multi-level validation. |
| Outcome: | The proposed framework outperforms existing systems in a number of domains and can be used to improve multi-turn conversation retrieval. |
Capturing Conversational Interaction for Question Answering via Global History Reasoning (2022.findings-naacl)
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| Challenge: | Existing studies have studied history-dependent reasoning for question answering . utilizing global conversation history for enhancement is gaining interest . |
| Approach: | They propose to establish long-distance dependency among global utterances in multi-turn conversation. |
| Outcome: | The proposed method improves on QuAC by 1%, yielding the F1 score of 73.7%. |
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations (2023.emnlp-main)
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| Challenge: | a recent study validates the effectiveness of chat language models by fine-tuning instruction data. |
| Approach: | They propose to use a large-scale dataset of instructional conversations to fine-tune a conversational model on instruction data. |
| Outcome: | The proposed model outperforms open-source models in key metrics including scale, average length, diversity, coherence, etc. |
Conversational Machine Comprehension: a Literature Review (2020.coling-main)
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| Challenge: | Conversational machine comprehension (CMC) is a research track in conversational AI. |
| Approach: | They propose to synthesize a generic framework for CMC models and highlight differences in recent approaches. |
| Outcome: | The proposed model will be used as a compendium for future research. |
Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models (2026.findings-acl)
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| Challenge: | Unlike textonly large language models (LLMs), SLMs integrate audio encoders and vocoders to support end-to-end speech understanding and generation. |
| Approach: | They evaluate three proprietary and two open-source SLMs and show that none of them can maintain a consistent speaking style when instructed to do so. |
| Outcome: | The proposed models cannot maintain a consistent speaking style after several turns of interaction, but can recall the style instruction when prompted in later turns, but fail to express it. |
Modeling Multi-turn Conversation with Deep Utterance Aggregation (C18-1)
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| Challenge: | Existing work on retrieval-based context modeling for multi-turn conversation ignores interactions among previous utterances. |
| Approach: | They propose retrieval-based response matching for multi-turn conversation . they propose to combine previous utterances into context using a deep utterrance aggregation model . |
| Outcome: | The proposed model outperforms state-of-the-art methods on three multi-turn conversation benchmarks including an e-commerce dialogue corpus. |
Phrase Retrieval for Open Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning (2023.findings-acl)
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| Challenge: | Open-Domain Conversational Question Answering (ODConvQA) aims to answer questions through a multi-turn conversation . however, such a pipeline approach makes the reader vulnerable to errors propagated from the retriever, which makes it slower since they are not runnable in parallel. |
| Approach: | They propose a method to directly predict answers with a phrase retrieval scheme for a sequence of words. |
| Outcome: | The proposed method outperforms the baselines on two ODConvQA datasets. |
InstructoR: Instructing Unsupervised Conversational Dense Retrieval with Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for conversational retrieval only fine-tune on limited supervised data, making it difficult for the retriever to fully grasp the entire conversation. |
| Approach: | They propose a method to instruct unsupervised conversational dense retrieval with large language models (LLMs) they use supervised data to discover the user's query intent from the conversation context . |
| Outcome: | The proposed method can bring significant improvements across various ad-hoc retrievers, surpassing the current state-of-the-art method. |
IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance (2024.naacl-long)
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| Challenge: | Existing methods for conversational query reformulation depend on human annotations. |
| Approach: | They propose a method that reformulates context-dependent conversational queries without relying on human rewrites. |
| Outcome: | The proposed method shows state-of-the-art performance on two widely-used datasets. |
StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation (2026.findings-acl)
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| Challenge: | Motivational interviewing (MI) is a directive, client-centered counseling approach for eliciting clients' motivation for behavioral change. |
| Approach: | They propose a multi-LLM agent framework for controllable MI dialogue generation . therapist and client agents generate MI-coded utterances guided by MI codes . |
| Outcome: | The proposed framework can generate fluent dialogues with minimal intervention time and a high level of evaluation. |
AI-LieDar : Examine the Trade-off Between Utility and Truthfulness in LLM Agents (2025.naacl-long)
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| Challenge: | LieDar is a framework to study how LLM-based agents navigate these scenarios in a multi-turn interactive setting. |
| Approach: | They propose a framework to study how LLM-based agents navigate these scenarios in an interactive multi-turn setting. |
| Outcome: | The proposed framework shows that all models are truthful less than 50% of the time, although truthfulness and goal achievement rates vary across models. |
PsyAdvisor: A Plug-and-Play Strategy Advice Planner with Proactive Questioning in Psychological Conversations (2025.acl-long)
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| 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. |
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (2024.emnlp-main)
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| Challenge: | Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect. |
| Approach: | They propose a new zero-shot RE task where only relation definitions are provided instead of seen-unseen relation instances. |
| Outcome: | The proposed task significantly improves cost-effective zero-shot performance by large margins. |
BiMediX2 : Bio-Medical EXpert LMM for Diverse Medical Modalities (2025.findings-emnlp)
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Sahal Shaji Mullappilly, Mohammed Irfan Kurpath, Sara Pieri, Saeed Yahya Alseiari, Shanavas Cholakkal, Khaled M Aldahmani, Fahad Shahbaz Khan, Rao Muhammad Anwer, Salman Khan, Timothy Baldwin, Hisham Cholakkal
| Challenge: | BiMediX2 is a bilingual (Arabic-English) large multimodal model that supports text-based and image-based medical interactions. |
| Approach: | They introduce BiMediX2, a bilingual (Arabic-English) Bio-Medical EXpert Large Multimodal Model that supports text-based and image-based medical interactions. |
| Outcome: | The model outperforms existing models by over 9% in English and more than 20% in Arabic evaluations. |
ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have excellent performance in evaluation benchmarks, but struggle in complex reasoning tasks. |
| Approach: | They propose a tool-augmented chain-of-thought reasoning framework for chat-based LLMs . they model chain- of-thoughting reasoning as multi-turn conversations to utilize tools . |
| Outcome: | The proposed framework can outperform state-of-the-art models on complex reasoning tasks. |
RedCoder: Automated Multi-Turn Red Teaming for Code LLMs (2026.acl-long)
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Wenjie Jacky Mo, Qin Liu, Xiaofei Wen, Dongwon Jung, Hadi Askari, Wenxuan Zhou, Zhe Zhao, Muhao Chen
| Challenge: | Existing red-teaming approaches for code generation rely on extensive human effort and are prone to generating malicious code under adversarial environments. |
| Approach: | They propose a red-teaming agent that engages victim models in multi-turn conversations to elicit vulnerable code. |
| Outcome: | Experiments show that RedCoder outperforms red-teaming methods in inducing vulnerabilities in code generation. |
Confidence Should Be Calibrated More Than One Turn Deep (2026.acl-long)
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| Challenge: | Existing work on confidence estimation and calibration focuses on single-turn settings . existing work on multi-turn calibration ignores the risks and potential of multi-turned conversations . |
| Approach: | They propose a multi-turn calibration task that reframes calibration from a static property into a dynamic challenge central to reliable multi- turn conversations. |
| Outcome: | The proposed model minimizes ECE@T and leverages ConfChat to improve confidence . the proposed model preserves and even enhances model performance in multi-turn interactions. |