A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling

By Yu Wang, Yilin Shen, Hongxia Jin. 2018

216 citations recorded by the source corpus.

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 these tasks . The most effective algorithms are based on the structures of sequence to sequence models (or “encoder-decoder” models), and generate the intents and semantic tags either using separate models. Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. None of the approaches consider the cross-impact between the intent detection task and the slot filling task. In this paper, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a decoder achieves state-of-art result on the benchmark ATIS data, with about 0.5% intent accuracy improvement and 0.9 % slot filling improvement.

Subfields: Artificial Intelligence

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Citations in the corpus

A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019, background): “…(Yao et al., 2014; Peng et al., 2015; Kurata et al., 2016; Hakkani-Tür et al., 2016; Liu and Lane, 2016; Deng et al., 2019; Ray et al., 2019) or jointly with the other intent detection task in SLU (Guo et al., 2014; Liu and Lane, 2016; Zhang and Wang, 2016; Wang et al., 2018; Goo et al., 2018).”
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding (2019, methodology): “• ATIS: It is used in many SLU researches (Liu and Lane, 2016; Wang et al., 2018; Kim et al., 2018), including those on utterances for flight reservations.”
Iterative Delexicalization for Improved Spoken Language Understanding (2019, background): “Recently, joint intent classification and slot labeling SLU models based on recurrent neural networks has been shown to achieve state-of-the-art performance on benchmark datasets [2, 3, 4, 5, 6].”
Deep Unknown Intent Detection with Margin Loss (2019, background): “DNN such as BiLSTM (Goo et al., 2018; Wang et al., 2018c) has demonstrated the ability to learn high-level semantic features of intents.”
A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding (2019, background): “Wang et al. (2018) proposed the Bi-model to consider the intent and slot ﬁll-ing cross-impact to each other.”