Dialogue Generation
147 papers with code • 12 benchmarks • 23 datasets
Dialogue generation is the task of "understanding" natural language inputs - within natural language processing in order to produce output. The systems are usually intended for conversing with humans, for instance back and forth dialogue with a conversation agent like a chatbot. Some example benchmarks for this task (see others such as Natural Language Understanding) include FusedChat and Ubuntu DIalogue Corpus (UDC). Models can be evaluated via metrics such as BLEU, ROUGE, and METEOR albeit with challenges in terms of weak correlation with human judgement, that may be addressed by new ones like UnSupervised and Reference-free (USR) and Metric for automatic Unreferenced dialog evaluation (MaUde).
Benchmarks
These leaderboards are used to track progress in Dialogue Generation
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Libraries
Use these libraries to find Dialogue Generation models and implementationsDatasets
WikiText-2
Reddit
WikiText-103
OpenSubtitles
UDC
Doc2Dial
CMU DoG
CPED
Most implemented papers
Neural Machine Translation by Jointly Learning to Align and Translate
Neural machine translation is a recently proposed approach to machine translation.
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.
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.
Deep Reinforcement Learning for Dialogue Generation
liuyuemaicha/Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow
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EMNLP 2016
Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes.
Adversarial Learning for Neural Dialogue Generation
liuyuemaicha/Adversarial-Learning-for-Neural-Dialogue-Generation-in-Tensorflow
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EMNLP 2017
In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances.
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
facebookresearch/EmpatheticDialogues
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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.
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens.
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.
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.
