| Challenge: | Instruction-tuned language models often use noisy multi-turn dialogue datasets with topic drift, repetitive chitchat, and mismatched answer formats across turns. |
| Approach: | They propose a dialogue-level framework that scores whole conversations rather than isolated turns. |
| Outcome: | The proposed framework outperforms strong single-turn selectors, dialogue-level LLM scorers and heuristic baselines on three multi-turn benchmarks and an in-domain Banking test set. |
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Ge Bai, Jie Liu, Xingyuan Bu, Yancheng He, Jiaheng Liu, Zhanhui Zhou, Zhuoran Lin, Wenbo Su, Tiezheng Ge, Bo Zheng, Wanli Ouyang
| Challenge: | Large Language Models (LLMs) have greatly enhanced dialogue systems, but evaluation of their capabilities remains a challenge. |
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Multi-turn Response Selection using Dialogue Dependency Relations (2020.emnlp-main)
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| Challenge: | Existing models for multi-turn response selection ignore the dependencies between the turns. |
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One Battle After Another: Probing LLMs’ Limits on Multi-Turn Instruction Following with a Benchmark Evolving Framework (2026.acl-long)
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| Challenge: | Existing benchmarks for instruction-following in multi-topic dialogues are limited to a fixed number of turns, susceptible to saturation and failing to account for users’ interactive experience. |
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A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations (2021.acl-long)
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| Challenge: | Existing methods to train retrieval-based dialogue systems rely on crowd-sourced data . however, it is difficult to collect large-scale dialogues that are grounded on background knowledge . |
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CMT-Eval: A Novel Chinese Multi-turn Dialogue Evaluation Dataset Addressing Real-world Conversational Challenges (2025.findings-emnlp)
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| Challenge: | Existing evaluation benchmarks fail to capture users’ evolving needs and how their diverse conversation styles affect the dialogue flow. |
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Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking (2022.acl-long)
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| Challenge: | Experimental results show that task-oriented dialogue systems have attracted growing attention and achieved substantial progress. |
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| Challenge: | Existing work on building a conversational system for open domain human-machine conversation is attracting more attention . early models concatenate all utterances or independently encode each dialogue turn, which may lead to an inadequate understanding of dialogue status. |
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Source-primed Multi-turn Conversation Helps Large Language Models Translate Documents (2025.findings-emnlp)
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| Challenge: | Document-level machine translations have paved the way for truly simple document-level translation, but challenges such as omission errors remain. |
| Approach: | They propose a method for document-level machine translation that leverages previous contexts in a multi-turn conversational manner by decomposing documents into segments and iteratively translating them while maintaining previous turns. |
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An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model (2021.emnlp-main)
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| Challenge: | Multi-turn response selection models have shown comparable performance to humans in several benchmark datasets, but in the real environment, they often have weaknesses, such as giving the highest score to the wrong response candidate containing several keywords related to the context. |
| Approach: | They propose to build a robust multi-turn response selection model in an adversarial environment and to use it to evaluate weaknesses. |
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