Papers with dialogue generation systems

2 papers
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation (2022.coling-1)

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Challenge: Existing zero-shot dialogue generation systems rely on large-scale pre-trained language models.
Approach: They propose a multilingual learning framework for zero-shot dialogue generation that can transfer knowledge from an English corpus to a non-English corpus with zero samples.
Outcome: The proposed framework can transfer knowledge from an English corpus to a non-English corpus with zero samples.
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling (2020.coling-main)

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Challenge: Existing models for machine translation and dialogue response generation require a large number of handcrafted features.
Approach: They propose to interpret a general neural model comparatively by using the seq2seq model in two mainstream NLP tasks.
Outcome: The proposed model is used in two mainstream NLP tasks and is compared with a standard model.

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