Gahgene Gweon


Automatic Gloss-level Data Augmentation for Sign Language Translation
Jin Yea Jang | Han-Mu Park | Saim Shin | Suna Shin | Byungcheon Yoon | Gahgene Gweon
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Securing sufficient data to enable automatic sign language translation modeling is challenging. The data insufficiency issue exists in both video and text modalities; however, fewer studies have been performed on text data augmentation compared to video data. In this study, we present three methods of augmenting sign language text modality data, comprising 3,052 Gloss-level Korean Sign Language (GKSL) and Word-level Korean Language (WKL) sentence pairs. Using each of the three methods, the following number of sentence pairs were created: blank replacement 10,654, sentence paraphrasing 1,494, and synonym replacement 899. Translation experiment results using the augmented data showed that when translating from GKSL to WKL and from WKL to GKSL, Bi-Lingual Evaluation Understudy (BLEU) scores improved by 0.204 and 0.170 respectively, compared to when only the original data was used. The three contributions of this study are as follows. First, we demonstrated that three different augmentation techniques used in existing Natural Language Processing (NLP) can be applied to sign language. Second, we propose an automatic data augmentation method which generates quality data by utilizing the Korean sign language gloss dictionary. Lastly, we publish the Gloss-level Korean Sign Language 13k dataset (GKSL13k), which has verified data quality through expert reviews.

EPT-X: An Expression-Pointer Transformer model that generates eXplanations for numbers
Bugeun Kim | Kyung Seo Ki | Sangkyu Rhim | Gahgene Gweon
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper, we propose a neural model EPT-X (Expression-Pointer Transformer with Explanations), which utilizes natural language explanations to solve an algebraic word problem. To enhance the explainability of the encoding process of a neural model, EPT-X adopts the concepts of plausibility and faithfulness which are drawn from math word problem solving strategies by humans. A plausible explanation is one that includes contextual information for the numbers and variables that appear in a given math word problem. A faithful explanation is one that accurately represents the reasoning process behind the model’s solution equation. The EPT-X model yields an average baseline performance of 69.59% on our PEN dataset and produces explanations with quality that is comparable to human output. The contribution of this work is two-fold. (1) EPT-X model: An explainable neural model that sets a baseline for algebraic word problem solving task, in terms of model’s correctness, plausibility, and faithfulness. (2) New dataset: We release a novel dataset PEN (Problems with Explanations for Numbers), which expands the existing datasets by attaching explanations to each number/variable.


BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning
Jin Yea Jang | San Kim | Minyoung Jung | Saim Shin | Gahgene Gweon
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Backchannel (BC), a short reaction signal of a listener to a speaker’s utterances, helps to improve the quality of the conversation. Several studies have been conducted to predict BC in conversation; however, the utilization of advanced natural language processing techniques using lexical information presented in the utterances of a speaker has been less considered. To address this limitation, we present a BC prediction model called BPM_MT (Backchannel prediction model with multitask learning), which utilizes KoBERT, a pre-trained language model. The BPM_MT simultaneously carries out two tasks at learning: 1) BC category prediction using acoustic and lexical features, and 2) sentiment score prediction based on sentiment cues. BPM_MT exhibited 14.24% performance improvement compared to the existing baseline in the four BC categories: continuer, understanding, empathic response, and No BC. In particular, for empathic response category, a performance improvement of 17.14% was achieved.


Generating Equation by Utilizing Operators : GEO model
Kyung Seo Ki | Donggeon Lee | Bugeun Kim | Gahgene Gweon
Proceedings of the 28th International Conference on Computational Linguistics

Math word problem solving is an emerging research topic in Natural Language Processing. Recently, to address the math word problem-solving task, researchers have applied the encoder-decoder architecture, which is mainly used in machine translation tasks. The state-of-the-art neural models use hand-crafted features and are based on generation methods. In this paper, we propose the GEO (Generation of Equations by utilizing Operators) model that does not use hand-crafted features and addresses two issues that are present in existing neural models: 1. missing domain-specific knowledge features and 2. losing encoder-level knowledge. To address missing domain-specific feature issue, we designed two auxiliary tasks: operation group difference prediction and implicit pair prediction. To address losing encoder-level knowledge issue, we added an Operation Feature Feed Forward (OP3F) layer. Experimental results showed that the GEO model outperformed existing state-of-the-art models on two datasets, 85.1% in MAWPS, and 62.5% in DRAW-1K, and reached comparable performance of 82.1% in ALG514 dataset.

Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model
Bugeun Kim | Kyung Seo Ki | Donggeon Lee | Gahgene Gweon
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Solving algebraic word problems has recently emerged as an important natural language processing task. To solve algebraic word problems, recent studies suggested neural models that generate solution equations by using ‘Op (operator/operand)’ tokens as a unit of input/output. However, such a neural model suffered two issues: expression fragmentation and operand-context separation. To address each of these two issues, we propose a pure neural model, Expression-Pointer Transformer (EPT), which uses (1) ‘Expression’ token and (2) operand-context pointers when generating solution equations. The performance of the EPT model is tested on three datasets: ALG514, DRAW-1K, and MAWPS. Compared to the state-of-the-art (SoTA) models, the EPT model achieved a comparable performance accuracy in each of the three datasets; 81.3% on ALG514, 59.5% on DRAW-1K, and 84.5% on MAWPS. The contribution of this paper is two-fold; (1) We propose a pure neural model, EPT, which can address the expression fragmentation and the operand-context separation. (2) The fully automatic EPT model, which does not use hand-crafted features, yields comparable performance to existing models using hand-crafted features, and achieves better performance than existing pure neural models by at most 40%.


Measuring Popularity of Machine-Generated Sentences Using Term Count, Document Frequency, and Dependency Language Model
Jong Myoung Kim | Hancheol Park | Young-Seob Jeong | Ho-Jin Choi | Gahgene Gweon | Jeong Hur
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters


Sentential Paraphrase Generation for Agglutinative Languages Using SVM with a String Kernel
Hancheol Park | Gahgene Gweon | Ho-Jin Choi | Jeong Heo | Pum-Mo Ryu
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing


An Unsupervised Dynamic Bayesian Network Approach to Measuring Speech Style Accommodation
Mahaveer Jain | John McDonough | Gahgene Gweon | Bhiksha Raj | Carolyn Penstein Rosé
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics


Towards a Prototyping Tool for Behavior Oriented Authoring of Conversational Agents for Educational Applications
Gahgene Gweon | Jaime Arguello | Carol Pai | Regan Carey | Zachary Zaiss | Carolyn Rosé
Proceedings of the Second Workshop on Building Educational Applications Using NLP