Papers Conversational Response Selection
“Conversational Response Selection” 태그가 달린 논문 46편 · 필터 해제
Efficient Dynamic Hard Negative Sampling for Dialogue Selection
Recent studies have demonstrated significant improvements in selection tasks, and a considerable portion of this success is attributed to incorporating informative negative samples during training. While traditional meth…
Conversational Response SelectionP5: Plug-and-Play Persona Prompting for Personalized Response Selection
The use of persona-grounded retrieval-based chatbots is crucial for personalized conversations, but there are several challenges that need to be addressed. 1) In general, collecting persona-grounded corpus is very expens…
ChatbotConversational Response SelectionKnowledge-aware response selection with semantics underlying multi-turn open-domain conversations
Response selection is a critical issue in the AI community, with important applications on the Web. The accuracy of the selected responses, however, tends to be insufficient due to the lack of contextual awareness, espec…
Conversational Response SelectionDial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue Systems
Dialogue response selection aims to select an appropriate response from several candidates based on a given user and system utterance history. Most existing works primarily focus on post-training and fine-tuning tailored…
Conversational Response SelectionDecoderLanguage ModelingLanguage Modelling+2Learning Dialogue Representations from Consecutive Utterances
Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data scarcity. In this paper, we introduce Dial…
Contrastive LearningConversational Question AnsweringConversational Response SelectionDialogue Act Classification+14One Agent To Rule Them All: Towards Multi-agent Conversational AI
The increasing volume of commercially available conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accomplish their tasks. Though prior work has e…
AllConversational Response SelectionMulti-agent IntegrationText ClassificationTwo-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue
Selecting an appropriate response from many candidates given the utterances in a multi-turn dialogue is the key problem for a retrieval-based dialogue system. Existing work formalizes the task as matching between the utt…
Contrastive LearningConversational Response SelectionRetrievalSentence+1Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning
Retrieve-based dialogue response selection aims to find a proper response from a candidate set given a multi-turn context. Pre-trained language models (PLMs) based methods have yielded significant improvements on this ta…
Contrastive LearningConversational Response SelectionExploring Dense Retrieval for Dialogue Response Selection
Recent progress in deep learning has continuously improved the accuracy of dialogue response selection. In particular, sophisticated neural network architectures are leveraged to capture the rich interactions between dia…
Conversational Response SelectionRetrievalResponse Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues
Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is challenging in three aspects: (1) the meaning of a context–response pair is built upon language un…
Conversational Response SelectionRetrievalMPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding
Recently, various neural models for multi-party conversation (MPC) have achieved impressive improvements on a variety of tasks such as addressee recognition, speaker identification and response prediction. However, these…
Conversational Response SelectionLanguage ModelingLanguage ModellingSpeaker IdentificationUni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems
Sample-and-rank is a key decoding strategy for modern generation-based dialogue systems. It helps achieve diverse and high-quality responses by selecting an answer from a small pool of generated candidates. The current s…
Computational EfficiencyConversational Response SelectionFine-grained Post-training for Improving Retrieval-based Dialogue Systems
Retrieval-based dialogue systems display an outstanding performance when pre-trained language models are used, which includes bidirectional encoder representations from transformers (BERT). During the multi-turn response…
Conversational Response SelectionRetrievalOpen-domain question classification and completion in conversational information search
Searching for new information requires talking to the system. In this research, an Open-domain Conversational information search system has been developed. This system has been implemented using the TREC CAsT 2019 track,…
ClassificationConversational Response SelectionGeneral ClassificationDialogue Response Selection with Hierarchical Curriculum Learning
We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical…
Conversational Response SelectionDialogue Response Ranking Training with Large-Scale Human Feedback Data
Existing open-domain dialog models are generally trained to minimize the perplexity of target human responses. However, some human replies are more engaging than others, spawning more followup interactions. Current conve…
Conversational Response SelectionOpen-Domain DialogLearning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues
Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is a great challenging task. Existing studies focus on building a context-response matching model wit…
Conversational Response SelectionRetrievalDo Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection
In this paper, we study the task of selecting the optimal response given a user and system utterance history in retrieval-based multi-turn dialog systems. Recently, pre-trained language models (e.g., BERT, RoBERTa, and E…
Binary ClassificationConversational Response SelectionRetrievalSpeaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots
In this paper, we study the problem of employing pre-trained language models for multi-turn response selection in retrieval-based chatbots. A new model, named Speaker-Aware BERT (SA-BERT), is proposed in order to make th…
Conversational Response SelectionDisentanglementDomain AdaptationRetrievalThe World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection
Response selection plays a vital role in building retrieval-based conversation systems. Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary cl…
Conversational Response SelectionDiversityLearning-To-RankResponse Generation+1