35 citations · 201 across the 39 of their papers we have counts for
6 papers · 1 filter
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 New Concept of Deep Reinforcement Learning based Augmented General Sequence Tagging System
Yu Wang, Abhishek Patel, Hongxia Jin
In this paper, a new deep reinforcement learning based augmented general sequence tagging system is proposed. The new system contains two parts: a deep neural network (DNN) based s…
A Neural Transition-based Model for Nested Mention Recognition
Bailin Wang, Wei Lu, Yu Wang +1
It is common that entity mentions can contain other mentions recursively. This paper introduces a scalable transition-based method to model the nested structure of mentions. We fir…
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…
Beyond Word Embeddings: Learning Entity and Concept Representations from Large Scale Knowledge Bases
Walid Shalaby, Wlodek Zadrozny, Hongxia Jin
Text representations using neural word embeddings have proven effective in many NLP applications. Recent researches adapt the traditional word embedding models to learn vectors of…