51 citations · 106 across the 9 of their papers we have counts for
13 papers
Training Naturalized Semantic Parsers with Very Little Data
Subendhu Rongali, Konstantine Arkoudas, Melanie Rubino +1
Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Google Assistant. State-of-the-art (SOTA) semantic parsers are seq2seq architectur…
Instilling Type Knowledge in Language Models via Multi-Task QA
Shuyang Li, Mukund Sridhar, Chandana Satya Prakash +3
Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge -- their types. Previous methods to learn entity types rely on tra…
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
Shuyang Li, Jin Cao, Mukund Sridhar +4
Dialog State Tracking (DST), an integral part of modern dialog systems, aims to track user preferences and constraints (slots) in task-oriented dialogs. In real-world settings with…
Exploring Transfer Learning For End-to-End Spoken Language Understanding
Subendhu Rongali, Beiye Liu, Liwei Cai +3
Voice Assistants such as Alexa, Siri, and Google Assistant typically use a two-stage Spoken Language Understanding pipeline; first, an Automatic Speech Recognition (ASR) component…
Delexicalized Paraphrase Generation
Boya Yu, Konstantine Arkoudas, Wael Hamza
We present a neural model for paraphrasing and train it to generate delexicalized sentences. We achieve this by creating training data in which each input is paired with a number o…
Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding
Jin Cao, Jun Wang, Wael Hamza +2
Neural models have yielded state-of-the-art results in deciphering spoken language understanding (SLU) problems; however, these models require a significant amount of domain-specif…