@inproceedings{ki-etal-2020-generating,
    title = "Generating Equation by Utilizing Operators : {GEO} model",
    author = "Ki, Kyung Seo  and
      Lee, Donggeon  and
      Kim, Bugeun  and
      Gweon, Gahgene",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.coling-main.38/",
    doi = "10.18653/v1/2020.coling-main.38",
    pages = "426--436",
    abstract = "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."
}