SEQUENCE-TO-SEQUENCE LEARNING FOR MOTION-AWARE CLAIM GENERATION
Keywords:seq2seq, argumentation mining, deep learning, negative log likelihood, BLEU
The goal of this research is to generate a motion-aware claim using a deep neural network approach: sequence-to-sequence learning method. A motion-aware claim is a sentence that is logically correlated to the motion while preserving its grammatical structure. Our proposed model generates a motion-aware claim in a form of one sentence and takes motion as the input also in a form of one sentence. We use a publicly available argumentation mining dataset that contains annotated motion and claim data. In this research, we propose a novel approach for argument generation by employing a scheduled sampling strategy to make the model converge faster. The BLEU scores and questionnaire are used to quantitatively assess the model. Our best model achieves 0.175 ± 0.088 BLEU-4 score. Based on the questionnaire results, we can also derive a conclusion that it is hard for the respondents to differentiate between the human-made and the model-generated arguments.
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