35 citations · 98 across the 17 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…