Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Given that rich information is hidden behind ubiquitous numbers in text, numerical reasoning
over text should be an essential skill of AI systems. To derive precise equations to solve …

Task-oriented parsing (TOP) aims to convert natural language into machine-readable
representations of specific tasks, such as setting an alarm. A popular approach to TOP is to …

While large pre-trained language models accumulate a lot of knowledge in their parameters,
it has been demonstrated that augmenting it with non-parametric retrieval-based memory …

We propose a novel deliberation-based approach to end-to-end (E2E) spoken language
understanding (SLU), where a streaming automatic speech recognition (ASR) model …

Abstract We introduce a Recursive INsertion-based Encoder (RINE), a novel approach for
semantic parsing in task-oriented dialog. Our model consists of an encoder network that …

Recent task-oriented dialog systems have had great success in building English-based
personal assistants, but extending these systems to a global audience is challenging due to …

Task-oriented semantic parsing models have achieved strong results in recent years, but
unfortunately do not strike an appealing balance between model size, runtime latency, and …

We have investigated methods utilizing hierarchical structure information representation in
the semantic parsing task and have devised a method that reinforces the semantic …