116 citations · 515 across the 22 of their papers we have counts for
6 papers · 1 filter
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…
Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding
Qile Zhu, Haidar Khan, Saleh Soltan +2
Semantic parsing is one of the key components of natural language understanding systems. A successful parse transforms an input utterance to an action that is easily understood by…
Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
Subendhu Rongali, Luca Soldaini, Emilio Monti +1
Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant often rely on a semantic parsing component to understand which action(s) to execute for an utterance spoke…
Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses
Mingda Li, Weitong Ruan, Xinyue Liu +3
In a modern spoken language understanding (SLU) system, the natural language understanding (NLU) module takes interpretations of a speech from the automatic speech recognition (ASR…