SOTAVerified

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

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Showing 1–10 of 46 papers

TitleStatusHype
Efficient Dynamic Hard Negative Sampling for Dialogue SelectionCode0
P5: Plug-and-Play Persona Prompting for Personalized Response SelectionCode0
Knowledge-aware response selection with semantics underlying multi-turn open-domain conversationsCode0
Dial-MAE: ConTextual Masked Auto-Encoder for Retrieval-based Dialogue SystemsCode0
Learning Dialogue Representations from Consecutive UtterancesCode1
One Agent To Rule Them All: Towards Multi-agent Conversational AICode0
Two-Level Supervised Contrastive Learning for Response Selection in Multi-Turn Dialogue—0
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning—0
Exploring Dense Retrieval for Dialogue Response SelectionCode1
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based DialoguesCode0
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All datasetsUbuntu Dialogue (v1, Ranking)DoubanE-commerceRRSDSTC7 UbuntuPolyAI RedditUbuntu IRCRRS Ranking TestPersona-ChatPolyAI AmazonQAAdvising Corpuspersonachat

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ConveRT1-of-100 Accuracy84.3—Unverified
2PolyAI Encoder1-of-100 Accuracy71.3—Unverified