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Conversational Response Selection

13 papers with code · Natural Language Processing

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The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems

WS 2015 facebookresearch/ParlAI

This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words.

ANSWER SELECTION CONVERSATIONAL RESPONSE SELECTION

A Repository of Conversational Datasets

WS 2019 PolyAI-LDN/conversational-datasets

Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.

CONVERSATIONAL RESPONSE SELECTION DIALOGUE UNDERSTANDING

Sequential Attention-based Network for Noetic End-to-End Response Selection

9 Jan 2019alibaba/esim-response-selection

The noetic end-to-end response selection challenge as one track in Dialog System Technology Challenges 7 (DSTC7) aims to push the state of the art of utterance classification for real world goal-oriented dialog systems, for which participants need to select the correct next utterances from a set of candidates for the multi-turn context.

CONVERSATIONAL RESPONSE SELECTION GOAL-ORIENTED DIALOG

Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network

ACL 2018 baidu/Dialogue

Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context.

CHATBOT CONVERSATIONAL RESPONSE SELECTION

Modeling Multi-turn Conversation with Deep Utterance Aggregation

COLING 2018 cooelf/DeepUtteranceAggregation

In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation.

CONVERSATIONAL RESPONSE SELECTION

ConveRT: Efficient and Accurate Conversational Representations from Transformers

9 Nov 2019PolyAI-LDN/polyai-models

We pretrain using a retrieval-based response selection task, effectively leveraging quantization and subword-level parameterization in the dual encoder to build a lightweight memory- and energy-efficient model.

CONVERSATIONAL RESPONSE SELECTION INTENT CLASSIFICATION QUANTIZATION

Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots

ACL 2017 yangliuy/NeuralResponseRanking

Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which may lose relationships among utterances or important contextual information.

CONVERSATIONAL RESPONSE SELECTION

Multi-hop Selector Network for Multi-turn Response Selection in Retrieval-based Chatbots

IJCNLP 2019 chunyuanY/Dialogue

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.

CONVERSATIONAL RESPONSE SELECTION