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20152022
most citedA Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling

12 citations · 75 across the 23 of their papers we have counts for

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11 papers · 1 filter

cs.CL20223 cited

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…

cs.CL2021

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…

cs.CL2021

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…

cs.CL2021

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…

cs.CL20202 cited

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…

cs.CL20205 cited

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…