Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents

Malandrakis, Nikolaos; Shen, Minmin; Goyal, Anuj; Gao, Shuyang; Sethi, Abhishek; Metallinou, Angeliki

doi:10.18653/v1/d19-5609

Cited by 36 publications

(21 citation statements)

References 18 publications

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“…In our work, we make no assumption about the availability of labeled data in other languages, user feedback or unlabeled data, as none of them is necessarily available when bootstrapping a new feature. Therefore, the closest existing work to ours is and (Malandrakis et al, 2019), who both deal with the setup where only seed examples are available. Malandrakis et al (2019) propose using conditional variational auto-encoders to generate paraphrases for the seed data and show that the paraphrases increase intent classification performance in their experiment.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the closest existing work to ours is and (Malandrakis et al, 2019), who both deal with the setup where only seed examples are available. Malandrakis et al (2019) propose using conditional variational auto-encoders to generate paraphrases for the seed data and show that the paraphrases increase intent classification performance in their experiment. In contrast to our work, they do not evaluate on slot labeling and do not suggest a technique to add slot labels to the paraphrases.…”

Section: Related Workmentioning

confidence: 99%

“…However, manually collecting annotated data is expensive and time-consuming, slowing down the expansion of the system's functionality. We therefore aim to reduce that time and effort by automatically augmenting the seed training data and follow the idea of recent work to leverage paraphrase generation (Malandrakis et al, 2019;. Through paraphrasing, we can automatically increase the amount of training data, but for the data to be useful, we have to ensure that the new utterances are diverse and different from what we already have.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems

Jolly

Falke

Tırkaz³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

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Recent progress through advanced neural models pushed the performance of task-oriented dialog systems to almost perfect accuracy on existing benchmark datasets for intent classification and slot labeling. However, in evolving real-world dialog systems, where new functionality is regularly added, a major additional challenge is the lack of annotated training data for such new functionality, as the necessary data collection efforts are laborious and time-consuming. A potential solution to reduce the effort is to augment initial seed data by paraphrasing existing utterances automatically. In this paper, we propose a new, data-efficient approach following this idea. Using an interpretation-to-text model for paraphrase generation, we are able to rely on existing dialog system training data, and, in combination with shuffling-based sampling techniques, we can obtain diverse and novel paraphrases from small amounts of seed data. In experiments on a public dataset and with a real-world dialog system, we observe improvements for both intent classification and slot labeling, demonstrating the usefulness of our approach.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems

Jolly

Falke

Tırkaz³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

View full text Add to dashboard Cite

show abstract

“…However, these approaches suffer from high training variance and mode collapse. Controlled text generation techniques were also explored to perform data augmentation (Malandrakis et al, 2019). For instance, Variational Autoencoder (VAE) was applied in text generation (Bowman et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection

Cao¹,

Lee²

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Academia and industry have developed machine learning and natural language processing models to detect online hate speech automatically. However, most of these existing methods adopt a supervised approach that heavily depends on labeled datasets for training. This results in the methods' poor detection performance of the hate speech class as the training datasets are highly imbalanced. In this paper, we propose HateGAN, a deep generative reinforcement learning model, which addresses the challenge of imbalance class by augmenting the dataset with hateful tweets. We conduct extensive experiments to augment two commonly-used hate speech detection datasets with the HateGAN generated tweets. Our experiment results show that HateGAN improves the detection performance of the hate speech class regardless of the classifiers and datasets used in the detection task. Specifically, we observe an average 5% improvement for the hate class F1 scores across all state-of-the-art hate speech classifiers. We also conduct case studies to empirically examine the HateGAN generated hate speeches and show that the generated tweets are diverse, coherent, and relevant to hate speech detection.

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“…For example, Wei and Zou (2019) investigated language transformations like insertion, deletion and swap. Several works (Malandrakis et al, 2019;Yoo et al, 2019;Xia et al, 2020b;Xia et al, 2020a) utilized variational autoencoders (VAEs) (Kingma and Welling, 2013) to generate more raw inputs. Nevertheless, these methods often rely on some extra knowledge to guarantee the quality of new inputs, and they have to be working in a pipeline.…”

Section: Introductionmentioning

confidence: 99%

Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks

Sun¹,

Xia

Yin³

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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Mixup (Zhang et al., 2017) is a latest data augmentation technique that linearly interpolates input examples and the corresponding labels. It has shown strong effectiveness in image classification by interpolating images at the pixel level. Inspired by this line of research, in this paper, we explore: i) how to apply mixup to natural language processing tasks since text data can hardly be mixed in the raw format; ii) if mixup is still effective in transformer-based learning models, e.g., BERT. To achieve the goal, we incorporate mixup to transformer-based pre-trained architecture, named "mixup-transformer", for a wide range of NLP tasks while keeping the whole end-to-end training system. We evaluate the proposed framework by running extensive experiments on the GLUE benchmark. Furthermore, we also examine the performance of mixup-transformer in low-resource scenarios by reducing the training data with a certain ratio. Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.

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Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents

Cited by 36 publications

References 18 publications

Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems

Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems

HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection

Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks

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