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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Conversational Response Selection models and implementationsDatasets
Reddit
Douban
UDC
DSTC7 Task 1
Reddit Corpus
Advising Corpus
Most implemented papers
Do 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.
Dialogue Response Selection with Hierarchical Curriculum Learning
yxuansu/HCL
•
•
ACL 2021
As for IC, it progressively strengthens the model's ability in identifying the mismatching information between the dialogue context and a response candidate.
Open-domain question classification and completion in conversational information search
Searching for new information requires talking to the system.
Fine-grained Post-training for Improving Retrieval-based Dialogue Systems
hanjanghoon/BERT_FP
•
•
NAACL 2021
During the multi-turn response selection, BERT focuses on training the relationship between the context with multiple utterances and the response.
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.
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues
RayXu14/WDMN
•
•
ACM Transactions on Information Systems 2021
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.
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.
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.
Learning Dialogue Representations from Consecutive Utterances
amazon-research/dse
•
•
NAACL 2022
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.
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.
