Pre-trained encoder-only and sequence-to-sequence (seq2seq) models each ...
Scaling up weakly-supervised datasets has shown to be highly effective i...
Semantic parsing plays a key role in digital voice assistants such as Al...
A bottleneck to developing Semantic Parsing (SP) models is the need for ...
We present LINGUIST, a method for generating annotated data for Intent
C...
In this work, we demonstrate that multilingual large-scale
sequence-to-s...
We present results from a large-scale experiment on pretraining encoders...
Semantic parsing is an important NLP problem, particularly for voice
ass...
Understanding human language often necessitates understanding entities a...
Dialog State Tracking (DST), an integral part of modern dialog systems, ...
Voice Assistants such as Alexa, Siri, and Google Assistant typically use...
We present a neural model for paraphrasing and train it to generate
dele...
Neural models have yielded state-of-the-art results in deciphering spoke...
Semantic parsing is one of the key components of natural language
unders...
Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistan...
In a modern spoken language understanding (SLU) system, the natural lang...
We propose an entity-centric neural cross-lingual coreference model that...
A major challenge in Entity Linking (EL) is making effective use of
cont...
We propose a query-based generative model for solving both tasks of ques...
In recent years, many deep-learning based models are proposed for text
c...
This paper describes an application of reinforcement learning to the men...
Natural language sentence matching is a fundamental technology for a var...
Previous machine comprehension (MC) datasets are either too small to tra...