Challenge: Existing studies focus on matching candidate responses with every context utterance, but it also brings noise signals and unnecessary information.
Approach: They propose a multi-hop selector network to match context with candidate responses . they propose to use a selector to filter the relevant utterances as context .
Outcome: The proposed model outperforms state-of-the-art methods on three public multi-turn dialogue datasets.

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Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (P18-1)

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Challenge: Existing models for matching dialogue responses rely on semantic and functional dependencies . a recent study only uses the last utterance in context for matching a reply .
Approach: They propose a model that matches a response with its multi-turn context using attention.
Outcome: The proposed model outperforms the state-of-the-art models on two large-scale multi-turn response selection tasks.
Lingke: a Fine-grained Multi-turn Chatbot for Customer Service (C18-2)

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Challenge: e-commerce chatbots usually need a mass of human dialogue data to train, but for multi-turn conversations, the performance is poor.
Approach: They propose an information retrieval augmented multi-turn chatbot which can answer questions based on unstructured documents and deal with multi-turned conversations.
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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 .
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How to Represent Context Better? An Empirical Study on Context Modeling for Multi-turn Response Selection (2022.findings-emnlp)

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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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Multi-Grained Conversational Graph Network for Retrieval-based Dialogue Systems (2024.lrec-main)

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Challenge: Existing methods for retrieval-based dialogues concatenate all turns in the dialogue history as input, ignoring dialogue dependency and structural information between the utterances.
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Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots (D19-1)

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Challenge: Existing models for personalized dialogues rank responses according to their semantic relevance with the given context.
Approach: They propose a dually interactive matching network (DIM) for presenting personalities of dialogue agents in retrieval-based chatbots.
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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.
Approach: They propose a dialogue extraction algorithm to transform a dialog history into threads based on their dependency relations.
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Intra-/Inter-Interaction Network with Latent Interaction Modeling for Multi-turn Response Selection (2020.coling-main)

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Challenge: Existing methods for multi-turn response selection are not practical as the turns of conversations vary.
Approach: They propose to use latent interaction modeling to model multi-level interactions between utterance and response.
Outcome: The proposed method outperforms state-of-the-art methods on three multi-turn response selection benchmark datasets.
Generative Context Pair Selection for Multi-hop Question Answering (2021.emnlp-main)

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Challenge: Recent studies have shown that discriminative training results in models that exploit these underlying biases to achieve a better held-out performance, without learning the right way to reason.
Approach: They propose a generative context selection model for multi-hop QA that reasons about how the given question could have been generated given a context pair and not just independent contexts.
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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.
Outcome: The proposed model makes incorrect predictions based heavily on superficial patterns without a comprehensive understanding of the context.

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