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Conversational Response Selection

Conversational response selection refers to the task of identifying the most relevant response to a given input sentence from a collection of sentences.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 31–40 of 46 papers

Title	Date	Tasks	Status	Hype	Score
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues	Aug 17, 2021	Conversational Response SelectionRetrieval	CodeCode Available	0	5
Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots	Dec 6, 2016	Conversational Response SelectionRetrieval	CodeCode Available	0	5
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems	Jun 30, 2015	Answer SelectionConversational Response Selection	CodeCode Available	0	5
TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots	Sep 24, 2019	Conversational Response SelectionRetrieval	CodeCode Available	0	5
A Repository of Conversational Datasets	Apr 13, 2019	BIG-bench Machine LearningConversational Response Selection	CodeCode Available	0	5
Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots	Nov 1, 2019	Conversational Response SelectionRetrieval	CodeCode Available	0	5
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network	Jul 1, 2018	ChatbotConversational Response Selection	CodeCode Available	0	5
Multi-Granularity Representations of Dialog	Aug 26, 2019	Conversational Response SelectionRetrieval	—Unverified	0	0
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue	Mar 1, 2022	Contrastive LearningConversational Response Selection	—Unverified	0	0
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems	Nov 1, 2019	Conversational Response SelectionRetrieval	—Unverified	0	0

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All datasets Ubuntu Dialogue (v1, Ranking)Douban E-commerce RRS DSTC7 Ubuntu PolyAI Reddit Ubuntu IRC RRS Ranking Test Persona-Chat PolyAI AmazonQA Advising Corpus personachat

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Dial-MAE	R10@1	0.92	—	Unverified
2	BERT-FP+EDHNS	R10@1	0.92	—	Unverified
3	Uni-Enc+BERT-FP	R10@1	0.92	—	Unverified
4	BERT-FP	R10@1	0.91	—	Unverified
5	Uni-Encoder	R10@1	0.89	—	Unverified
6	BERT-UMS+FGC	R10@1	0.89	—	Unverified
7	BERT-SL	R10@1	0.88	—	Unverified
8	Poly-encoder	R10@1	0.88	—	Unverified
9	UMS_BERT+	R10@1	0.88	—	Unverified
10	SA-BERT	R10@1	0.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SEMSOL(W/o utterances)	MAP	0.65	—	Unverified
2	Uni-Enc+BERT-FP	MAP	0.65	—	Unverified
3	BERT-FP	MAP	0.64	—	Unverified
4	SEMSOL	MAP	0.64	—	Unverified
5	SA-BERT+HCL	MAP	0.64	—	Unverified
6	UMS_BERT+	MAP	0.63	—	Unverified
7	Uni-Encoder	MAP	0.62	—	Unverified
8	SA-BERT	MAP	0.62	—	Unverified
9	Poly-encoder	MAP	0.61	—	Unverified
10	BERT	MAP	0.59	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT-FP+EDHNS	R10@1	0.96	—	Unverified
2	DialMAE	R10@1	0.93	—	Unverified
3	BERT-TL	R10@1	0.93	—	Unverified
4	BERT-FP	R10@1	0.87	—	Unverified
5	BERT-SL	R10@1	0.78	—	Unverified
6	UMS_BERT+	R10@1	0.76	—	Unverified
7	SA-BERT+HCL	R10@1	0.72	—	Unverified
8	SA-BERT	R10@1	0.7	—	Unverified
9	IMN	R10@1	0.62	—	Unverified
10	U2U-IMN	R10@1	0.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT-FP	MAP	0.7	—	Unverified
2	SA-BERT+BERT-FP	MAP	0.7	—	Unverified
3	SA-BERT+HCL	MAP	0.67	—	Unverified
4	BERT	MAP	0.63	—	Unverified
5	MSN	MAP	0.55	—	Unverified
6	DAM	MAP	0.51	—	Unverified
7	SMN	MAP	0.49	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Multi-context ConveRT	1-of-100 Accuracy	71.2	—	Unverified
2	Bi-encoder (v2)	1-of-100 Accuracy	70.9	—	Unverified
3	Bi-encoder	1-of-100 Accuracy	66.3	—	Unverified
4	Sequential Attention-based Network	1-of-100 Accuracy	64.5	—	Unverified
5	Sequential Inference Models	1-of-100 Accuracy	60.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Multi-context ConveRT	1-of-100 Accuracy	71.8	—	Unverified
2	ConveRT	1-of-100 Accuracy	68.3	—	Unverified
3	PolyAI Encoder	1-of-100 Accuracy	61.3	—	Unverified
4	USE	1-of-100 Accuracy	47.7	—	Unverified
5	ELMO	1-of-100 Accuracy	19.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MPC-BERT	Accuracy	63.64	—	Unverified
2	SA-BERT	Accuracy	60.42	—	Unverified
3	SIRNN	Accuracy	40.83	—	Unverified
4	DRNN	Accuracy	36.93	—	Unverified
5	Dynamic Modeling (n=10)	Accuracy	36.93	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Poly-encoder	NDCG@3	0.68	—	Unverified
2	SA-BERT+BERT-FP	NDCG@3	0.67	—	Unverified
3	BERT	NDCG@3	0.63	—	Unverified
4	BERT-FP	NDCG@3	0.61	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Uni-Encoder	MRR	0.92	—	Unverified
2	P5	R20@1	0.88	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ConveRT	1-of-100 Accuracy	84.3	—	Unverified
2	PolyAI Encoder	1-of-100 Accuracy	71.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CtxDec & -Rev	R@1	31	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	P5	R20@1	87.45	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PolyAI Encoder	1-of-100 Accuracy	30.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Uni-Encoder	R10@1	0.86	—	Unverified