Error syntax aware augmentation of feedback comment generation dataset
This work addresses feedback generation for language learners, but it is incremental as it builds on existing models and datasets.
The paper tackled feedback comment generation for writing learning by fine-tuning T5 on a dataset augmented with syntactical dependencies of error spans, achieving second place in manual evaluation in the GenChal 2022 shared task.
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.