Error syntax aware augmentation of feedback comment generation dataset

29 Dec 2022 · Nikolay Babakov, Maria Lysyuk, Alexander Shvets, Lilya Kazakova, Alexander Panchenko ·

This paper presents a solution to the GenChal 2022 shared task dedicated to feedback comment generation for writing learning. In terms of this task given a text with an error and a span of the error, a system generates an explanatory note that helps the writer (language learner) to improve their writing skills. Our solution is based on fine-tuning the T5 model on the initial dataset augmented according to syntactical dependencies of the words located within indicated error span. The solution of our team "nigula" obtained second place according to manual evaluation by the organizers.

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Comment Generation

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Adafactor • Attention Dropout • BPE • Dense Connections • Dropout • GELU • GLU • Inverse Square Root Schedule • Layer Normalization • Linear Layer • Multi-Head Attention • Residual Connection • Scaled Dot-Product Attention • SentencePiece • Softmax • T5

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Error syntax aware augmentation of feedback comment generation dataset

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