35 citations · 98 across the 17 of their papers we have counts for
17 papers
Compositional Generalization in Spoken Language Understanding
Avik Ray, Yilin Shen, Hongxia Jin
State-of-the-art spoken language understanding (SLU) models have shown tremendous success in benchmark SLU datasets, yet they still fail in many practical scenario due to the lack…
CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss
Rakshith Sharma Srinivasa, Jaejin Cho, Chouchang Yang +4
This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pa…
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…
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…
How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection
Wenhu Chen, Yu Su, Yilin Shen +3
With the rapid development in deep learning, deep neural networks have been widely adopted in many real-life natural language applications. Under deep neural networks, a pre-define…
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…
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
Yu Wang, Yilin Shen, Hongxia Jin
Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on…
A Variational Dirichlet Framework for Out-of-Distribution Detection
Wenhu Chen, Yilin Shen, Hongxia Jin +1
With the recently rapid development in deep learning, deep neural networks have been widely adopted in many real-life applications. However, deep neural networks are also known to…
User Information Augmented Semantic Frame Parsing using Coarse-to-Fine Neural Networks
Yilin Shen, Xiangyu Zeng, Yu Wang +1
Semantic frame parsing is a crucial component in spoken language understanding (SLU) to build spoken dialog systems. It has two main tasks: intent detection and slot filling. Altho…
Robust Spoken Language Understanding via Paraphrasing
Avik Ray, Yilin Shen, Hongxia Jin
Learning intents and slot labels from user utterances is a fundamental step in all spoken language understanding (SLU) and dialog systems. State-of-the-art neural network based met…
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning
Xinlei Pan, Eshed Ohn-Bar, Nicholas Rhinehart +3
Humans are able to understand and perform complex tasks by strategically structuring the tasks into incremental steps or subgoals. For a robot attempting to learn to perform a sequ…
Secure Pick Up: Implicit Authentication When You Start Using the Smartphone
Wei-Han Lee, Xiaochen Liu, Yilin Shen +2
We propose Secure Pick Up (SPU), a convenient, lightweight, in-device, non-intrusive and automatic-learning system for smartphone user authentication. Operating in the background,…
DPHMM: Customizable Data Release with Differential Privacy via Hidden Markov Model
Yonghui Xiao, Yilin Shen, Jinfei Liu +3
Hidden Markov model (HMM) has been well studied and extensively used. In this paper, we present DPHMM ({Differentially Private Hidden Markov Model}), an HMM embedded with a private…