20 citations · 39 across the 10 of their papers we have counts for
12 papers
Introducing Semantics into Speech Encoders
Derek Xu, Shuyan Dong, Changhan Wang +10
Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information. As a result, pipelined supervised automatic speech recogni…
Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models
Alon Albalak, Akshat Shrivastava, Chinnadhurai Sankar +2
Multi-task learning (MTL), instruction tuning, and prompting have recently been shown to improve the generalizability of large language models to new tasks. However, the benefits o…
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…
RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing
Vivek Gupta, Akshat Shrivastava, Adithya Sagar +2
While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory ha…
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…