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

Showing 110 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 Dialogue0
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning0
Exploring Dense Retrieval for Dialogue Response SelectionCode1
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based DialoguesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Multi-context ConveRT1-of-100 Accuracy71.2Unverified
2Bi-encoder (v2)1-of-100 Accuracy70.9Unverified
3Bi-encoder1-of-100 Accuracy66.3Unverified
4Sequential Attention-based Network1-of-100 Accuracy64.5Unverified
5Sequential Inference Models1-of-100 Accuracy60.8Unverified