23 citations · 24 across the 3 of their papers we have counts for
6 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…
Improved Latent Tree Induction with Distant Supervision via Span Constraints
Zhiyang Xu, Andrew Drozdov, Jay Yoon Lee +6
For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems stil…
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…
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings
Prafull Prakash, Saurabh Kumar Shashidhar, Wenlong Zhao +3
The current state-of-the-art task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders; these models have huge memory footprints. This poses a challenge to t…
Continual Domain-Tuning for Pretrained Language Models
Subendhu Rongali, Abhyuday Jagannatha, Bhanu Pratap Singh Rawat +1
Pre-trained language models (LM) such as BERT, DistilBERT, and RoBERTa can be tuned for different domains (domain-tuning) by continuing the pre-training phase on a new target domai…
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…