Conversational Response Selection
31 papers with code • 13 benchmarks • 11 datasets
Conversational response selection refers to the task of identifying the most relevant response to a given input sentence from a collection of sentences.
Benchmarks
These leaderboards are used to track progress in Conversational Response Selection
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Libraries
Use these libraries to find Conversational Response Selection models and implementationsDatasets
Reddit
Douban
UDC
DSTC7 Task 1
Reddit Corpus
Advising Corpus
Latest papers with no code
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue
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.
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning
The sequence representation plays a key role in the learning of matching degree between the dialogue context and the response.
Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues
To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models.
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection
Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary classifiers for this task: each response candidate is labeled either relevant (one) or irrelevant (zero).
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems
We study how to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems.
TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots
The heart of TripleNet is a novel attention mechanism named triple attention to model the relationships within the triple at four levels.
Multi-Granularity Representations of Dialog
Neural models of dialog rely on generalized latent representations of language.
DSTC7 Task 1: Noetic End-to-End Response Selection
Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets.
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs
The ensemble further improves the performance and it achieves a state-of-the-art result for the next utterance ranking on this dataset.
