35 citations · 201 across the 39 of their papers we have counts for
39 papers
Numerical Optimizations for Weighted Low-rank Estimation on Language Model
Ting Hua, Yen-Chang Hsu, Felicity Wang +3
Singular value decomposition (SVD) is one of the most popular compression methods that approximate a target matrix with smaller matrices. However, standard SVD treats the parameter…
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling
Kalpa Gunaratna, Vijay Srinivasan, Akhila Yerukola +1
Joint intent detection and slot filling is a key research topic in natural language understanding (NLU). Existing joint intent and slot filling systems analyze and compute features…
Language model compression with weighted low-rank factorization
Yen-Chang Hsu, Ting Hua, Sungen Chang +3
Factorizing a large matrix into small matrices is a popular strategy for model compression. Singular value decomposition (SVD) plays a vital role in this compression strategy, appr…
A Closer Look at Knowledge Distillation with Features, Logits, and Gradients
Yen-Chang Hsu, James Smith, Yilin Shen +2
Knowledge distillation (KD) is a substantial strategy for transferring learned knowledge from one neural network model to another. A vast number of methods have been developed for…
MGA-VQA: Multi-Granularity Alignment for Visual Question Answering
Peixi Xiong, Yilin Shen, Hongxia Jin
Learning to answer visual questions is a challenging task since the multi-modal inputs are within two feature spaces. Moreover, reasoning in visual question answering requires the…
Hyperparameter-free Continuous Learning for Domain Classification in Natural Language Understanding
Ting Hua, Yilin Shen, Changsheng Zhao +2
Domain classification is the fundamental task in natural language understanding (NLU), which often requires fast accommodation to new emerging domains. This constraint makes it imp…
Automatic Mixed-Precision Quantization Search of BERT
Changsheng Zhao, Ting Hua, Yilin Shen +2
Pre-trained language models such as BERT have shown remarkable effectiveness in various natural language processing tasks. However, these models usually contain millions of paramet…
ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs
Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan +1
Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and sati…
Exploring Covariate and Concept Shift for Detection and Calibration of Out-of-Distribution Data
Junjiao Tian, Yen-Change Hsu, Yilin Shen +2
Moving beyond testing on in-distribution data works on Out-of-Distribution (OOD) detection have recently increased in popularity. A recent attempt to categorize OOD data introduces…
Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile Phones
Kalpa Gunaratna, Vijay Srinivasan, Sandeep Nama +1
Information Extraction from visual documents enables convenient and intelligent assistance to end users. We present a Neighborhood-based Information Extraction (NIE) approach that…
Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU
Yilin Shen, Yen-Chang Hsu, Avik Ray +1
Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to d…
An Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction
Yu Wang, Yilin Shen, Hongxia Jin
Most of the existing spoken language understanding systems can perform only semantic frame parsing based on a single-round user query. They cannot take users' feedback to update/ad…
Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning
James Smith, Yen-Chang Hsu, Jonathan Balloch +3
Modern computer vision applications suffer from catastrophic forgetting when incrementally learning new concepts over time. The most successful approaches to alleviate this forgett…
A Coarse to Fine Question Answering System based on Reinforcement Learning
Yu Wang, Hongxia Jin
In this paper, we present a coarse to fine question answering (CFQA) system based on reinforcement learning which can efficiently processes documents with different lengths by choo…
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web
Kalpa Gunaratna, Yu Wang, Hongxia Jin
Knowledge is captured in the form of entities and their relationships and stored in knowledge graphs. Knowledge graphs enhance the capabilities of applications in many different ar…
Negative Data Augmentation
Abhishek Sinha, Kumar Ayush, Jiaming Song +3
Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations,…
Modeling Token-level Uncertainty to Learn Unknown Concepts in SLU via Calibrated Dirichlet Prior RNN
Yilin Shen, Wenhu Chen, Hongxia Jin
One major task of spoken language understanding (SLU) in modern personal assistants is to extract semantic concepts from an utterance, called slot filling. Although existing slot f…
Generating Dialogue Responses from a Semantic Latent Space
Wei-Jen Ko, Avik Ray, Yilin Shen +1
Existing open-domain dialogue generation models are usually trained to mimic the gold response in the training set using cross-entropy loss on the vocabulary. However, a good respo…
A Complex KBQA System using Multiple Reasoning Paths
Kechen Qin, Yu Wang, Cheng Li +4
Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding. Many KBQA approaches have been proposed in recent years, and most of them…
Reward Constrained Interactive Recommendation with Natural Language Feedback
Ruiyi Zhang, Tong Yu, Yilin Shen +3
Text-based interactive recommendation provides richer user feedback and has demonstrated advantages over traditional interactive recommender systems. However, recommendations can e…
PGLP: Customizable and Rigorous Location Privacy through Policy Graph
Yang Cao, Yonghui Xiao, Shun Takagi +6
Location privacy has been extensively studied in the literature. However, existing location privacy models are either not rigorous or not customizable, which limits the trade-off b…
Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data
Yen-Chang Hsu, Yilin Shen, Hongxia Jin +1
Deep neural networks have attained remarkable performance when applied to data that comes from the same distribution as that of the training set, but can significantly degrade othe…
Iterative Delexicalization for Improved Spoken Language Understanding
Avik Ray, Yilin Shen, Hongxia Jin
Recurrent neural network (RNN) based joint intent classification and slot tagging models have achieved tremendous success in recent years for building spoken language understanding…
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded
Ramprasaath R. Selvaraju, Stefan Lee, Yilin Shen +5
Many vision and language models suffer from poor visual grounding - often falling back on easy-to-learn language priors rather than basing their decisions on visual concepts in the…