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
Dialogue Response Ranking Training with Large-Scale Human Feedback Data
golsun/dialogrpt
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EMNLP 2020
Particularly, our ranker outperforms the conventional dialog perplexity baseline with a large margin on predicting Reddit feedback.
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.
Speaker-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.
Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
The distances between context and response utterances are employed as a prior component when calculating the attention weights.
ConveRT: Efficient and Accurate Conversational Representations from Transformers
golsun/dialogrpt
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Findings of the Association for Computational Linguistics 2020
General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train.
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots
chunyuanY/Dialogue
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IJCNLP 2019
Existing works mainly focus on matching candidate responses with every context utterance on multiple levels of granularity, which ignore the side effect of using excessive context information.
An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
We focus on multi-turn response selection in a retrieval-based dialog system.
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues
Currently, researchers have paid great attention to retrieval-based dialogues in open-domain.
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
The use of deep pre-trained bidirectional transformers has led to remarkable progress in a number of applications (Devlin et al., 2018).
A Repository of Conversational Datasets
PolyAI-LDN/conversational-datasets
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WS 2019
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.
