Short-Text Conversation
8 papers with code • 0 benchmarks • 1 datasets
Given a short text, finding an appropriate response (Source: http://staff.ustc.edu.cn/~cheneh/paper_pdf/2013/HaoWang.pdf)
Benchmarks
These leaderboards are used to track progress in Short-Text Conversation
Libraries
Use these libraries to find Short-Text Conversation models and implementations
LCCC
Latest papers with no code
DCH-2: A Parallel Customer-Helpdesk Dialogue Corpus with Distributions of Annotators' Labels
We introduce a data set called DCH-2, which contains 4, 390 real customer-helpdesk dialogues in Chinese and their English translations.
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation
Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts.
Relevance-Promoting Language Model for Short-Text Conversation
In this paper, we propose to formulate the STC task as a language modeling problem and tailor-make a training strategy to adapt a language model for response generation.
A Discrete CVAE for Response Generation on Short-Text Conversation
In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation.
Fine-Grained Sentence Functions for Short-Text Conversation
Classification models are trained on this dataset to (i) recognize the sentence function of new data in a large corpus of short-text conversations; (ii) estimate a proper sentence function of the response given a test query.
Generating Multiple Diverse Responses for Short-Text Conversation
In this paper, we propose a novel response generation model, which considers a set of responses jointly and generates multiple diverse responses simultaneously.
MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation
Neural encoder-decoder models have been widely applied to conversational response generation, which is a research hot spot in recent years.
Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems
The study on human-computer conversation systems is a hot research topic nowadays.
Learning to Start for Sequence to Sequence Architecture
An obvious drawback of these work is that there is not a learnable relationship between words and the start symbol.
