12 citations · 21 across the 7 of their papers we have counts for
13 papers
Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing
Akshat Shrivastava, Shrey Desai, Anchit Gupta +4
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 cr…
Assessing Data Efficiency in Task-Oriented Semantic Parsing
Shrey Desai, Akshat Shrivastava, Justin Rill +4
Data efficiency, despite being an attractive characteristic, is often challenging to measure and optimize for in task-oriented semantic parsing; unlike exact match, it can require…
Diagnosing Transformers in Task-Oriented Semantic Parsing
Shrey Desai, Ahmed Aly
Modern task-oriented semantic parsing approaches typically use seq2seq transformers to map textual utterances to semantic frames comprised of intents and slots. While these models…
Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling
Shrey Desai, Akshat Shrivastava, Alexander Zotov +1
Task-oriented semantic parsing models typically have high resource requirements: to support new ontologies (i.e., intents and slots), practitioners crowdsource thousands of samples…
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing
Akshat Shrivastava, Pierce Chuang, Arun Babu +4
An effective recipe for building seq2seq, non-autoregressive, task-oriented parsers to map utterances to semantic frames proceeds in three steps: encoding an utterance , predict…
Compressive Summarization with Plausibility and Salience Modeling
Shrey Desai, Jiacheng Xu, Greg Durrett
Compressive summarization systems typically rely on a crafted set of syntactic rules to determine what spans of possible summary sentences can be deleted, then learn a model of wha…
Understanding Neural Abstractive Summarization Models via Uncertainty
Jiacheng Xu, Shrey Desai, Greg Durrett
An advantage of seq2seq abstractive summarization models is that they generate text in a free-form manner, but this flexibility makes it difficult to interpret model behavior. In t…
Accelerating Natural Language Understanding in Task-Oriented Dialog
Ojas Ahuja, Shrey Desai
Task-oriented dialog models typically leverage complex neural architectures and large-scale, pre-trained Transformers to achieve state-of-the-art performance on popular natural lan…
Detecting Perceived Emotions in Hurricane Disasters
Shrey Desai, Cornelia Caragea, Junyi Jessy Li
Natural disasters (e.g., hurricanes) affect millions of people each year, causing widespread destruction in their wake. People have recently taken to social media websites (e.g., T…
Calibration of Pre-trained Transformers
Shrey Desai, Greg Durrett
Pre-trained Transformers are now ubiquitous in natural language processing, but despite their high end-task performance, little is known empirically about whether they are calibrat…
Lightweight Convolutional Representations for On-Device Natural Language Processing
Shrey Desai, Geoffrey Goh, Arun Babu +1
The increasing computational and memory complexities of deep neural networks have made it difficult to deploy them on low-resource electronic devices (e.g., mobile phones, tablets,…
Evaluating Lottery Tickets Under Distributional Shifts
Shrey Desai, Hongyuan Zhan, Ahmed Aly
The Lottery Ticket Hypothesis suggests large, over-parameterized neural networks consist of small, sparse subnetworks that can be trained in isolation to reach a similar (or better…
Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis
Shrey Desai, Barea Sinno, Alex Rosenfeld +1
Insightful findings in political science often require researchers to analyze documents of a certain subject or type, yet these documents are usually contained in large corpora tha…