Sparsify reports state-of-the-art accuracy on Music AVQA benchmarks by borrowing three existing sparsification techniques, cutting training time by 28% and retaining 70-80% of accuracy on a 25% data subset.
A Novel DeBERTa-based Model for Financial Question Answering Task
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abstract
As a rising star in the field of natural language processing, question answering systems (Q&A Systems) are widely used in all walks of life. Compared with other scenarios, the applicationin financial scenario has strong requirements in the traceability and interpretability of the Q&A systems. In addition, since the demand for artificial intelligence technology has gradually shifted from the initial computational intelligence to cognitive intelligence, this research mainly focuses on the financial numerical reasoning dataset - FinQA. In the shared task, the objective is to generate the reasoning program and the final answer according to the given financial report containing text and tables. We use the method based on DeBERTa pre-trained language model, with additional optimization methods including multi-model fusion, training set combination on this basis. We finally obtain an execution accuracy of 68.99 and a program accuracy of 64.53, ranking No. 4 in the 2022 FinQA Challenge.
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Learning Sparsity for Effective and Efficient Music Performance Question Answering
Sparsify reports state-of-the-art accuracy on Music AVQA benchmarks by borrowing three existing sparsification techniques, cutting training time by 28% and retaining 70-80% of accuracy on a 25% data subset.