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.
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One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill.
Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.
Ranked #1 on Generative Question Answering on CoQA (using extra training data)
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.
This paper presents a new adversarial learning method for generative conversational agents (GCA) besides a new model of GCA.
We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations.
Ranked #7 on Emotion Recognition in Conversation on MELD
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.
Ranked #1 on Dialogue Generation on Ubuntu Dialogue (Activity)
While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses.
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.
Ranked #1 on Dialogue Generation on Amazon-5
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.