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.
Libraries
Use these libraries to find Conversational Response Selection models and implementationsDatasets
Latest papers
Knowledge-aware response selection with semantics underlying multi-turn open-domain conversations
Then, SemSol improves the accuracy of the response by exploiting the semantic information in a knowledge graph in accordance with the dialogue context.
Dial-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.
Learning Dialogue Representations from Consecutive Utterances
In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive learning method that learns effective dialogue representations suitable for a wide range of dialogue tasks.
One Agent To Rule Them All: Towards Multi-agent Conversational AI
To address these problems, we introduce a new task BBAI: Black-Box Agent Integration, focusing on combining the capabilities of multiple black-box CAs at scale.
Exploring Dense Retrieval for Dialogue Response Selection
In this study, we present a solution to directly select proper responses from a large corpus or even a nonparallel corpus that only consists of unpaired sentences, using a dense retrieval model.
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues
To tackle these challenges, we propose a representation[K]-interaction[L]-matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection.
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems
The current state-of-the-art ranking methods mainly use an encoding paradigm called Cross-Encoder, which separately encodes each context-candidate pair and ranks the candidates according to their fitness scores.
Fine-grained Post-training for Improving Retrieval-based Dialogue Systems
During the multi-turn response selection, BERT focuses on training the relationship between the context with multiple utterances and the response.
Open-domain question classification and completion in conversational information search
Searching for new information requires talking to the system.
Dialogue Response Selection with Hierarchical Curriculum Learning
As for IC, it progressively strengthens the model's ability in identifying the mismatching information between the dialogue context and a response candidate.