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
Sequential Attention-based Network for Noetic End-to-End Response Selection
alibaba/esim-response-selection
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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.
Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
JasonForJoy/IMN
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In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task.
Building Sequential Inference Models for End-to-End Response Selection
JasonForJoy/DSTC7-ResponseSelection
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This paper presents an end-to-end response selection model for Track 1 of the 7th Dialogue System Technology Challenges (DSTC7).
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
huggingface/transformers
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network
Human generates responses relying on semantic and functional dependencies, including coreference relation, among dialogue elements and their context.
Modeling Multi-turn Conversation with Deep Utterance Aggregation
In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation.
Universal Sentence Encoder
facebookresearch/InferSent
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For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.
Deep contextualized word representations
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
Personalizing Dialogue Agents: I have a dog, do you have pets too?
facebookresearch/ParlAI
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ACL 2018
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating.
Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
MarkWuNLP/MultiTurnResponseSelection
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ACL 2017
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.
