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HIT&QMUL at SemEval-2022 Task 9: Label-Enclosed Generative Question Answering (LEG-QA)

Weihe Zhai, Mingqiang Feng, Arkaitz Zubiaga, Bingquan Liu

SemEval. 2022.

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This paper presents the second place system for the R2VQ: competence-based multimodal question answering shared task. The purpose of this task is to involve semantic&cooking roles and text-images objects when querying how well a system understands the procedure of a recipe. This task is approached with text-to-text generative model based on transformer architecture. As a result, the model can well generalise to soft constrained and other competence-based question answering problem. We propose label enclosed input method which help the model achieve significant improvement from 65.34 (baseline) to 91.3. In addition to describing the submitted system, the impact of model architecture and label selection are investigated along with remarks regarding error analysis. Finally, future works are presented.
  title={HIT\&QMUL at SemEval-2022 Task 9: Label-Enclosed Generative Question Answering (LEG-QA)},
  author={Zhai, Weihe and Feng, Mingqiang and Zubiaga, Arkaitz and Liu, Bingquan},
  booktitle={Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)},