In this paper, we present our solution to the TextGraphs-17 Shared Task on Text-Graph Representations for KGQA. GPT-4 alone, with chain-of-thought reasoning and a given set of answers, achieves an F1 score of 0.78. By employing subgraph size as a feature, Wikidata answer description as an additional context, and question rephrasing technique, we further strengthen this result. These tricks help to answer questions that were not initially answered and to eliminate irrelevant, identical answers. We have managed to achieve an F1 score of 0.83 and took 2nd place, improving the score by 0.05 over the baseline. An open implementation of our method is available on GitHub.

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.