16 citations · 48 across the 11 of their papers we have counts for
14 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…
AutoNLU: Detecting, root-causing, and fixing NLU model errors
Pooja Sethi, Denis Savenkov, Forough Arabshahi +6
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task. In this work…
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…
Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization
David Eriksson, Pierce I-Jen Chuang, Samuel Daulton +7
When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device l…
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…
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog
Arun Babu, Akshat Shrivastava, Armen Aghajanyan +3
Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespr…
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…
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…
Optimizing Deep Neural Networks with Multiple Search Neuroevolution
Ahmed Aly, David Weikersdorfer, Claire Delaunay
This paper presents an evolutionary metaheuristic called Multiple Search Neuroevolution (MSN) to optimize deep neural networks. The algorithm attempts to search multiple promising…
PyText: A Seamless Path from NLP research to production
Ahmed Aly, Kushal Lakhotia, Shicong Zhao +7
We introduce PyText - a deep learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of se…
Efficient Single-Shot Multibox Detector for Construction Site Monitoring
Viral Thakar, Himani Saini, Walid Ahmed +3
Asset monitoring in construction sites is an intricate, manually intensive task, that can highly benefit from automated solutions engineered using deep neural networks. We use Sing…
Experiential Robot Learning with Accelerated Neuroevolution
Ahmed Aly, Joanne B. Dugan
Derivative-based optimization techniques such as Stochastic Gradient Descent has been wildly successful in training deep neural networks. However, it has constraints such as end-to…