natural-language-processing-9

Conversational Response Selection

3260 papers • 126 benchmarks • 313 datasets

Conversational response selection refers to the task of identifying the most relevant response to a given input sentence from a collection of sentences.

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Benchmarks

These leaderboards are used to track progress in conversational-response-selection-9

Trend

Dataset

Best Model

Actions

Ubuntu Dialogue (v1, Ranking)

Douban

E-commerce

Libraries

i

Use these libraries to find conversational-response-selection-9 models and implementations

facebookresearch/ParlAI

2 papers 10,421

Datasets

Douban

UDC

E-commerce

Douban Conversation Corpus

DSTC7 Task 1

Subtasks

No subtasks available.

Most implemented papers

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Jacob Devlin, Kenton Lee, Kristina Toutanova, Ming-Wei Chang•Mon Dec 31 2018

A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

109344

Content

E-commerce

RRS

PolyAI Reddit

DSTC7 Ubuntu

RRS Ranking Test

PolyAI AmazonQA

PolyAI OpenSubtitles

Advising Corpus

Douban Conversation Corpus

Ubuntu Dialogue (v2, Ranking)

Persona-Chat

dmlc/gluon-nlp

2 papers 2,548

helboukkouri/character-bert

2 papers 194

yangliuy/NeuralResponseRanking

2 papers 73

RRS

0

Deep Contextualized Word Representations

Christopher Clark, Mohit Iyyer, Luke Zettlemoyer, Kenton Lee, Matt Gardner, Matthew E. Peters, Mark Neumann•Wed Feb 14 2018

A new type of deep contextualized word representation is introduced that models both complex characteristics of word use and how these uses vary across linguistic contexts, allowing downstream models to mix different types of semi-supervision signals.

12011 0

Paper Graph

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

Joelle Pineau, Ryan Lowe, Nissan Pow, Iulian Serban•Mon Jun 29 2015

The Ubuntu Dialogue Corpus is introduced, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words, that provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data.

969 0

Paper Graph

Universal Sentence Encoder

Daniel Matthew Cer, Yinfei Yang, C. Tar, Noah Constant, Nan Hua, Yun-Hsuan Sung, Sheng-yi Kong, Nicole Limtiaco, Rhomni St. John, Mario Guajardo-Cespedes, Steve Yuan, B. Strope, R. Kurzweil•Wed Mar 28 2018

It is found that transfer learning using sentence embeddings tends to outperform word level transfer with surprisingly good performance with minimal amounts of supervised training data for a transfer task.

2044 0

Paper Graph

Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring

J. Weston, Kurt Shuster, Samuel Humeau, M. Lachaux•Sun Apr 21 2019

This work develops a new transformer architecture, the Poly-encoder, that learns global rather than token level self-attention features and achieves state-of-the-art results on three existing tasks.

235 0

Paper Graph

ConveRT: Efficient and Accurate Conversational Representations from Transformers

Ivan Vulic, I. Casanueva, Matthew Henderson, Pei-hao Su, Nikola Mrkvsi'c, Tsung-Hsien•Fri Nov 08 2019

The proposed ConveRT (Conversational Representations from Transformers), a pretraining framework for conversational tasks satisfying all the following requirements: it is effective, affordable, and quick to train, and promises wider portability and scalability for Conversational AI applications.

207 0

Paper Graph

Personalizing Dialogue Agents: I have a dog, do you have pets too?

Arthur Szlam, Douwe Kiela, Saizheng Zhang, J. Weston, Emily Dinan, Jack Urbanek•Sun Jan 21 2018

This work collects data and train models tocondition on their given profile information; and information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction.

1631 0

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

Qian Chen, Wen Wang•Tue Jan 08 2019

This paper investigates a sequential matching model based only on chain sequence for multi-turn response selection, which outperforms all previous models, including state-of-the-art hierarchy-based models, and achieves new state of the art performances on two large-scale public multi- turn response selection benchmark datasets.

41 0

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A Repository of Conversational Datasets

N. Mrksic, Tsung-Hsien Wen, Ivan Vulic, Paweł Budzianowski, I. Casanueva, D. Gerz, Matthew Henderson, Sam Coope, Georgios P. Spithourakis, Pei-hao Su•Fri Apr 12 2019

A repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using '1-of-100 accuracy' is presented.

93 0

Paper Graph

Adding a benchmark result helps the community track progress.