Gahgene Gweon


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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.

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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%.


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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


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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


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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


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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