Hyungtak Choi


pdf bib
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation
Pawel Bujnowski | Kseniia Ryzhova | Hyungtak Choi | Katarzyna Witkowska | Jaroslaw Piersa | Tymoteusz Krumholc | Katarzyna Beksa
Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

The topic of this paper is neural multi-task training for text style transfer. We present an efficient method for neutral-to-style transformation using the transformer framework. We demonstrate how to prepare a robust model utilizing large paraphrases corpora together with a small parallel style transfer corpus. We study how much style transfer data is needed for a model on the example of two transformations: neutral-to-cute on internal corpus and modern-to-antique on publicly available Bible corpora. Additionally, we propose a synthetic measure for the automatic evaluation of style transfer models. We hope our research is a step towards replacing common but limited rule-based style transfer systems by more flexible machine learning models for both public and commercial usage.


pdf bib
VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization
Hyungtak Choi | Lohith Ravuru | Tomasz Dryjański | Sunghan Rye | Donghyun Lee | Hojung Lee | Inchul Hwang
Proceedings of the 12th International Conference on Natural Language Generation

This paper describes our submission to the TL;DR challenge. Neural abstractive summarization models have been successful in generating fluent and consistent summaries with advancements like the copy (Pointer-generator) and coverage mechanisms. However, these models suffer from their extractive nature as they learn to copy words from the source text. In this paper, we propose a novel abstractive model based on Variational Autoencoder (VAE) to address this issue. We also propose a Unified Summarization Framework for the generation of summaries. Our model eliminates non-critical information at a sentence-level with an extractive summarization module and generates the summary word by word using an abstractive summarization module. To implement our framework, we combine submodules with state-of-the-art techniques including Pointer-Generator Network (PGN) and BERT while also using our new VAE-PGN abstractive model. We evaluate our model on the benchmark Reddit corpus as part of the TL;DR challenge and show that our model outperforms the baseline in ROUGE score while generating diverse summaries.


pdf bib
Self-Learning Architecture for Natural Language Generation
Hyungtak Choi | Siddarth K.M. | Haehun Yang | Heesik Jeon | Inchul Hwang | Jihie Kim
Proceedings of the 11th International Conference on Natural Language Generation

In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants. Generating templates to cover all the combinations of slots in an intent is time consuming and labor-intensive. We examine three different models based on our proposed architecture - Rule-based model, Sequence-to-Sequence (Seq2Seq) model and Semantically Conditioned LSTM (SC-LSTM) model for the IoT domain - to reduce the human labor required for template generation. We demonstrate the feasibility of template generation for the IoT domain using our self-learning architecture. In both automatic and human evaluation, the self-learning architecture outperforms previous works trained with a fully human-labeled dataset. This is promising for commercial conversational assistant solutions.