Dialogue Generation
88 papers with code • 9 benchmarks • 12 datasets
Dialogue Generation is a fundamental component for real-world virtual assistants such as Siri and Alexa. It is the text generation task that automatically generate a response given a post by the user.
Source: Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey
Benchmarks
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Datasets
Reddit
PERSONA-CHAT
OpenSubtitles
UDC
CMU DoG
OpenDialKG
PersonalDialog
OpenViDial
OTTers
Greatest papers with code
Neural Machine Translation by Jointly Learning to Align and Translate
Neural machine translation is a recently proposed approach to machine translation.
Ranked #3 on
Dialogue Generation
on Persona-Chat
Controlling Style in Generated Dialogue
Open-domain conversation models have become good at generating natural-sounding dialogue, using very large architectures with billions of trainable parameters.
Wizard of Wikipedia: Knowledge-Powered Conversational agents
In open-domain dialogue intelligent agents should exhibit the use of knowledge, however there are few convincing demonstrations of this to date.
Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
facebookresearch/ParlAI
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ACL 2019
One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill.
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.
Ranked #4 on
Dialogue Generation
on Persona-Chat
ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.
Ranked #1 on
Text Summarization
on GigaWord-10k
(using extra training data)
Teaching Machines to Converse
facebook/MemNN
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The ability of a machine to communicate with humans has long been associated with the general success of AI.
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
PaddlePaddle/PaddleNLP
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ACL 2020
Pre-training models have been proved effective for a wide range of natural language processing tasks.
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
huggingface/transfer-learning-conv-ai
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We introduce a new approach to generative data-driven dialogue systems (e. g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model.
Ranked #2 on
Dialogue Generation
on Persona-Chat
Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation
However, previous work in dialogue response generation has shown that these metrics do not correlate strongly with human judgment in the non task-oriented dialogue setting.