Two-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 utterances and a candidate and uses the cross-entropy loss in learning of the model. This paper applies contrastive learning to the problem by using the supervised contrastive loss. In this way, the learned representations of positive examples and representations of negative examples can be more distantly separated in the embedding space, and the performance of matching can be enhanced. We further develop a new method for supervised contrastive learning, referred to as two-level supervised contrastive learning, and employ the method in response selection in multi-turn dialogue. Our method exploits two techniques: sentence token shuffling (STS) and sentence re-ordering (SR) for supervised contrastive learning. Experimental results on three benchmark datasets demonstrate that the proposed method significantly outperforms the contrastive learning baseline and the state-of-the-art methods for the task.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningRetrievalSentenceSTSMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Two-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+1A comprehensive solution to retrieval-based chatbot construction
In this paper we present the results of our experiments in training and deploying a self-supervised retrieval-based chatbot trained with contrastive learning for assisting customer support agents. In contrast to most exi…
Binary ClassificationChatbotContrastive LearningMulti-class Classification+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 LearningMCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling
Personalized chatbots focus on endowing the chatbots with a consistent personality to behave like real users and further act as personal assistants. Previous studies have explored generating implicit user profiles from t…
Response GenerationSelf-Supervised LearningGradient based Severity Labeling for Biomarker Classification in OCT
In this paper, we propose a novel selection strategy for contrastive learning for medical images. On natural images, contrastive learning uses augmentations to select positive and negative pairs for the contrastive loss.…
Contrastive LearningAnomaly Detection