Conversational Response Selection
30 papers with code • 13 benchmarks • 10 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
Douban
UDC
DSTC7 Task 1
Reddit Corpus
Advising Corpus
Most implemented papers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
google-research/bert
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NAACL 2019
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
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).
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.
The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
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.
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.
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
The use of deep pre-trained bidirectional transformers has led to remarkable progress in a number of applications (Devlin et al., 2018).
ConveRT: Efficient and Accurate Conversational Representations from Transformers
golsun/dialogrpt
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Findings of the Association for Computational Linguistics 2020
General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train.
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.
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.
A Repository of Conversational Datasets
PolyAI-LDN/conversational-datasets
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WS 2019
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.
