Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding

By Yutai Hou, Yijia Liu, Wanxiang Che, Ting-Wei Liu. 2018

160 citations recorded by the source corpus.

In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system. In contrast to previous work which augments an utterance without considering its relation with other utterances, we propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data. A novel diversity rank is incorporated into the utterance representation to make the model produce diverse utterances and these diversely augmented utterances help to improve the language understanding module. Experimental results on the Airline Travel Information System dataset and a newly created semantic frame annotation on Stanford Multi-turn, Multi-domain Dialogue Dataset show that our framework achieves significant improvements of 6.38 and 10.04 F-scores respectively when only a training set of hundreds utterances is represented. Case studies also confirm that our method generates diverse utterances.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, background): “Hou et al. (2018), Kim et al. (2019), Zhao et al. (2019), and Yoo et al. (2019) investigate DA methods for dialogue and spoken language understanding (SLU), including generative latent variable models.”
Automatically Learning Data Augmentation Policies for Dialogue Tasks (2019, neutral): “…Lemley et al., 2017) and NLP (F¨urstenau and Lapata, 2009; Sen-nrich et al., 2016; Wang and Yang, 2015; Zhang et al., 2015; Jia and Liang, 2016; Kim and Rush, 2016; Hu et al., 2017; Xu et al., 2017; Xia et al., 2017; Silfverberg et al., 2017; Kaﬂe et al., 2017; Hou et al., 2018; Wang et al., 2018).”
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding (2019, background): “Hou et al. (2018) generated diverse utterances of existing training data through a data augmentation framework based on sequence-to-sequence generation.”
Dialog State Tracking with Reinforced Data Augmentation (2019, methodology): “There is prior work on data augmentation in NLP (Zhang et al., 2015; Kang et al., 2018; Kobayashi, 2018; Hou et al., 2018; Ray et al., 2018; Yoo et al., 2018).”
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021, background): “Some other researchers have taken the direction of perturbing the latent spaces, optionally by introducing variational inference in the architecture (Xia et al., 2020b,a; Hou et al., 2018; Yoo et al., 2019).”
Data Augmentation with Atomic Templates for Spoken Language Understanding (2019, methodology): “Data augmentation for SLU Hou et al. (2018) use a sequence-to-sequence model to translate between sentence pairs with the same meaning.”
Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight (2020, methodology): “, 2018; Hu et al., 2019) have been proposed to manage the problem of training set biases or label noises. Lison and Bibauw (2017) propose to enhance the retrieval-based dialog system with a weighting model.”
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking (2021, background): “There are also many works (Hou et al., 2018; Yoo et al., 2020; Yin et al., 2020) on dialog augmentation.”
A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification (2019, background): “For text classification, several text generation based data augmentation techniques have also been explored (Hou et al., 2018; Zhao et al., 2019; Guu et al., 2018; Yoo et al., 2018; Cho et al., 2019a).”
Data Augmentation Approaches in Natural Language Processing: A Survey (2021, background): “[16] propose a Seq2Seq data augmentation model for the language understanding module of task-based dialogue systems.”
An empirical survey of data augmentation for time series classification with neural networks (2020, background): “[83] tackle language understanding and demonstrate the effectiveness of an LSTM-based input-feeding neural machine translation (NMT) model with attention in generating sentences for data augmentation.”
CorefQA: Coreference Resolution as Query-based Span Prediction (2019, background): “Data augmentation techniques have been explored in various fields such as question answering (Talmor and Berant, 2019), text classification (Kobayashi, 2018) and dialogue language understanding (Hou et al., 2018).”
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020, methodology): “This scenario may reﬂect a trade-off between diversity and validity of augmented instances (Hou et al., 2018; Xie et al., 2019).”
Substructure Substitution: Structured Data Augmentation for NLP (2021, methodology): “, 2020; Dehouck and Gómez-Rodrı́guez, 2020), semantic role labeling (Fürstenau and Lapata, 2009), and dialogue understanding (Hou et al., 2018; Niu and Bansal, 2019).”
Paraphrase Augmented Task-Oriented Dialog Generation (2020, methodology): “We use the BLEU (Pap-ineni et al., 2002) score and the diversity score proposed in Hou et al. (2018) to evaluate the para-phrase quality.”
Data Augmentation for Spoken Language Understanding via Joint Variational Generation (2018, methodology): “…for textual generation (Serban et al. 2017; Yu et al. 2017; Hu et al. 2017; Li et al. 2017), prompting investigations into the possibility of improving model performances through generative data augmentation (Kaﬂe, Yousefhussien, and Kanan 2017; Kurata, Xiang, and Zhou 2016; Hou et al. 2018).”
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue (2019, background): “[25] propose a data-augmentation framework to model relations between utterances of the same semantic frame in the training data.”
Simulated Chats for Task-oriented Dialog: Learning to Generate Conversations from Instructions (2020, methodology): “Such methods include, generating paraphrased utterances of existing conversations using sequence-to-sequence generative approaches (Hou et al. 2018; Anaby-Tavor et al. 2020), generating annotations including intent-slots and dialog acts (Yoo, Shin, and Lee 2019; Yoo et al.”
Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network (2020, methodology): “For data augmentation of slot tagging, sentence generation based methods are explored to create additional labeled samples (Hou et al., 2018; Shin et al., 2019; Yoo et al., 2019).”