87 citations · 106 across the 10 of their papers we have counts for
11 papers
A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Models
Rahul Gupta, Abhinav Mohanty, Payal Motwani +8
As frontier language models advance, policymakers and model developers need methods for assessing whether model access materially increases a non-expert actor's ability to plan hig…
Towards Data-efficient Modeling for Wake Word Spotting
Yixin Gao, Yuriy Mishchenko, Anish Shah +2
Wake word (WW) spotting is challenging in far-field not only because of the interference in signal transmission but also the complexity in acoustic environments. Traditional WW mod…
A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems
Sunghyun Park, Han Li, Ameen Patel +5
Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding…
Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training
Bowen Shi, Ming Sun, Chieh-Chi Kao +3
In this paper, we present a compression approach based on the combination of low-rank matrix factorization and quantization training, to reduce complexity for neural network based…
Semi-supervised Acoustic Event Detection based on tri-training
Bowen Shi, Ming Sun, Chieh-Chi Kao +3
This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset. Recent acoustic event detectors are based on large-scale neural networks, wh…
Parsing Coordination for Spoken Language Understanding
Sanchit Agarwal, Rahul Goel, Tagyoung Chung +3
Typical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology. The parses contain intents and slots that are directly consumed by do…
Active Learning for New Domains in Natural Language Understanding
Stanislav Peshterliev, John Kearney, Abhyuday Jagannatha +2
We explore active learning (AL) for improving the accuracy of new domains in a natural language understanding (NLU) system. We propose an algorithm called Majority-CRF that uses an…
A Re-ranker Scheme for Integrating Large Scale NLU models
Chengwei Su, Rahul Gupta, Shankar Ananthakrishnan +1
Large scale Natural Language Understanding (NLU) systems are typically trained on large quantities of data, requiring a fast and scalable training strategy. A typical design for NL…
Device-directed Utterance Detection
Sri Harish Mallidi, Roland Maas, Kyle Goehner +3
In this work, we propose a classifier for distinguishing device-directed queries from background speech in the context of interactions with voice assistants. Applications include r…
Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents
Anuj Goyal, Angeliki Metallinou, Spyros Matsoukas
Fast expansion of natural language functionality of intelligent virtual agents is critical for achieving engaging and informative interactions. However, developing accurate models…
Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting
Ming Sun, Anirudh Raju, George Tucker +6
We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requi…