View publication

Natural language understanding (NLU) and natural language generation (NLG) are two fundamental and related tasks in building task-oriented dialogue systems with opposite objectives: NLU tackles the transformation from natural language to formal representations, whereas NLG does the reverse. A key to success in either task is parallel training data which is expensive to obtain at a large scale. In this work, we propose a generative model which couples NLU and NLG through a shared latent variable. This approach allows us to explore both spaces of natural language and formal representations, and facilitates information sharing through the latent space to eventually benefit NLU and NLG. Our model achieves state-of-the-art performance on two dialogue datasets with both flat and tree-structured formal representations. We also show that the model can be trained in a semi-supervised fashion by utilising unlabelled data to boost its performance.

Related readings and updates.

Using Pause Information for More Accurate Entity Recognition

This paper was showcased in the 3rd Workshop on NLP for Conversational AI at the EMNLP 2021 conference. Entity tags in human-machine dialog are integral to natural language understanding (NLU) tasks in conversational assistants. However, current systems struggle to accurately parse spoken queries with the typical use of text input alone, and often fail to understand the user intent. Previous work in linguistics has identified a cross-language…
See paper details

Apple at ACL 2020

Apple sponsored the 58th Annual Meeting of the Association for Computational Linguistics (ACL) from July 5 - 10. ACL is the premier conference of the field of computational linguistics, covering a broad spectrum of research areas regarding computational approaches to natural language.

See event details