[PDF] Using Recurrent Neural Networks for Slot Filling in Spoken



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Using Recurrent Neural Networks for Slot Filling in Spoken

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AbstractSemantic slot filling is one of the most challenging

Index Terms

I. INTRODUCTION

he term spoken language understanding' recognition of user input, ܵ

Manuscript submitted for review on XXX.

The s Grégoire Mesnil, Yann Dauphin, Kaisheng Yao, Yoshua Bengio, Li Deng, Dilek Hakkani-Tur, Xiaodong He, Larry Heck, Gokhan Tur, Dong Yu, and Geoffrey Zweig T them on the standard ATIS (Airline Travel

SLOT FILLING IN SPOKEN LANGUAGE

UNDERSTANDING

achieve a goal in a human

Boston

New York

respectively. Sentence show flights from Boston To New York today

Slots/Concepts O O O B-dept O B-arr I-arr B-date

Named Entity O O O B-city O B-city I-city O

Intent Find_Flight

Domain Airline Travel

L, and the output

S, one for each

L, the goal of SLU is to find the

a posteriori ܲ S. sequenceܮ

݈௧, with a

-@Es.

DEEP LEARNINGREVIEW

words

RECURRENT NEURAL NETWORKS FOR

SLOT-ILLING

A. Word

The main input to a R

semantic

B. Context Word Window

Before considering any t

ȁ8 -@Es ݀ previous word followed by the word of interest

݈௜ :6BHECDP6;L

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