Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained Models
Spoken Language Understanding (SLU) is one essential step in building a dialogue system. Due to the expensive cost of obtaining the labeled data, SLU suffers from the data scarcity problem. Therefore, in this paper, we focus on data augmentation for slot filling task in SLU. To achieve that, we aim at generating more diverse data based on existing data. Specifically, we try to exploit the latent language knowledge from pretrained language models by finetuning them. We propose two strategies for finetuning process: value-based and context-based augmentation. Experimental results on two public SLU datasets have shown that compared with existing data augmentation methods, our proposed method can generate more diverse sentences and significantly improve the performance on SLU.
Code (1)
Tasks
Data Augmentationslot-fillingSlot FillingSpoken Language UnderstandingSimilar Papers 제목 키워드 기반
SpokenUS: A Spoken User Simulator for Task-Oriented Dialogue
Robust task-oriented spoken dialogue agents require exposure to the full diversity of how people interact through speech. Building spoken user simulators that address this requires large-scale spoken task-oriented dialog…
A Hierarchical Decoding Model For Spoken Language Understanding From Unaligned Data
Spoken language understanding (SLU) systems can be trained on two types of labelled data: aligned or unaligned. Unaligned data do not require word by word annotation and is easier to be obtained. In the paper, we focus o…
Spoken Language UnderstandingIterative Delexicalization for Improved Spoken Language Understanding
Recurrent neural network (RNN) based joint intent classification and slot tagging models have achieved tremendous success in recent years for building spoken language understanding and dialog systems. However, these mode…
intent-classificationIntent ClassificationSpoken Language UnderstandingAn Adversarial Learning based Multi-Step Spoken Language Understanding System through Human-Computer Interaction
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/add/remove slot values through multiround …
Dialogue State TrackingSemantic Frame ParsingSentenceSpoken Language UnderstandingImproving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention
We present a generative neural network model for slot filling based on a sequence-to-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in …
Sentenceslot-fillingSlot FillingSpeech Recognition+1