Effective Spoken Language Labeling with Deep Recurrent Neural Networks
Understanding spoken language is a highly complex problem, which can be decomposed into several simpler tasks. In this paper, we focus on Spoken Language Understanding (SLU), the module of spoken dialog systems responsible for extracting a semantic interpretation from the user utterance. The task is treated as a labeling problem. In the past, SLU has been performed with a wide variety of probabilistic models. The rise of neural networks, in the last couple of years, has opened new interesting research directions in this domain. Recurrent Neural Networks (RNNs) in particular are able not only to represent several pieces of information as embeddings but also, thanks to their recurrent architecture, to encode as embeddings relatively long contexts. Such long contexts are in general out of reach for models previously used for SLU. In this paper we propose novel RNNs architectures for SLU which outperform previous ones. Starting from a published idea as base block, we design new deep RNNs achieving state-of-the-art results on two widely used corpora for SLU: ATIS (Air Traveling Information System), in English, and MEDIA (Hotel information and reservation in France), in French.
Code (0)
등록된 구현이 없습니다.
Tasks
Spoken Language UnderstandingSimilar Papers 제목 키워드 기반
Improving Recurrent Neural Networks For Sequence Labelling
In this paper we study different types of Recurrent Neural Networks (RNN) for sequence labeling tasks. We propose two new variants of RNNs integrating improvements for sequence labeling, and we compare them to the more t…
POSPOS TaggingSpoken Language UnderstandingSequential Convolutional Neural Networks for Slot Filling in Spoken Language Understanding
We investigate the usage of convolutional neural networks (CNNs) for the slot filling task in spoken language understanding. We propose a novel CNN architecture for sequence labeling which takes into account the previous…
General Classificationslot-fillingSlot FillingSpoken Language UnderstandingLabel-Dependencies Aware Recurrent Neural Networks
In the last few years, Recurrent Neural Networks (RNNs) have proved effective on several NLP tasks. Despite such great success, their ability to model \emph{sequence labeling} is still limited. This lead research toward …
Spoken Language UnderstandingSimple, Fast, Accurate Intent Classification and Slot Labeling for Goal-Oriented Dialogue Systems
With the advent of conversational assistants, like Amazon Alexa, Google Now, etc., dialogue systems are gaining a lot of traction, especially in industrial setting. These systems typically consist of Spoken Language unde…
General ClassificationGoal-Oriented Dialogue Systemsintent-classificationIntent Classification+1Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model
Intent Detection and Slot Filling are two pillar tasks in Spoken Natural Language Understanding. Common approaches adopt joint Deep Learning architectures in attention-based recurrent frameworks. In this work, we aim at …
Intent DetectionNatural Language Understandingslot-fillingSlot Filling+2