mlsanityData to 7 Oct 2026

A Novel DeBERTa-based Model for Financial Question Answering Task

arXiv 2207.05875 ↗First posted 12 Jul 2022cs.CLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 12 Jul 2022.

Builds on

9 of 22 references.

References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  3. 2021FinQA: A Dataset of Numerical Reasoning over Financial Datacited by 552
  4. 2021TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Financecited by 402
  5. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 386
  6. 2017Variational Reasoning for Question Answering with Knowledge Graphcited by 170
  7. 2021Hurdles to Progress in Long-form Question Answeringcited by 166
  8. 2020SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Basescited by 47
  9. 2019Bidirectional Attentive Memory Networks for Question Answering over Knowledge Basescited by 30

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2; 2026: 1.

5 papers.

  1. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 134
  2. 2022APOLLO: An Optimized Training Approach for Long-form Numerical Reasoningcited by 8
  3. 2025Learning Sparsity for Effective and Efficient Music Performance Question Answeringcited by 4
  4. 2025Program of Thoughts for Financial Reasoning: Leveraging Dynamic In-Context Examples and Generative Retrievalcited by 2
  5. 2026From Documents to Reasoning: A Validated Synthetic Data Pipeline and Semantic-Aware Fine-Tuning for Financial Numerical Reasoningcited by 0