Detect, Retrieve, Comprehend: A Flexible Framework for Zero-Shot Document-Level Question Answering

• URL: http://arxiv.org/abs/2210.01959v3
• Date: Mon, 11 Dec 2023 22:20:47 GMT
• Title: Detect, Retrieve, Comprehend: A Flexible Framework for Zero-Shot Document-Level Question Answering
• Authors: Tavish McDonald, Brian Tsan, Amar Saini, Juanita Ordonez, Luis Gutierrez, Phan Nguyen, Blake Mason, Brenda Ng
• Abstract summary: Researchers produce thousands of scholarly documents containing valuable technical knowledge. Document-level question answering (QA) offers a flexible framework where human-posed questions can be adapted to extract diverse knowledge. We present a three-stage document QA approach: text extraction from PDF; evidence retrieval from extracted texts to form well-posed contexts; and QA to extract knowledge from contexts to return high-quality answers.
• Score: 6.224211330728391
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Researchers produce thousands of scholarly documents containing valuable technical knowledge. The community faces the laborious task of reading these documents to identify, extract, and synthesize information. To automate information gathering, document-level question answering (QA) offers a flexible framework where human-posed questions can be adapted to extract diverse knowledge. Finetuning QA systems requires access to labeled data (tuples of context, question and answer). However, data curation for document QA is uniquely challenging because the context (i.e. answer evidence passage) needs to be retrieved from potentially long, ill-formatted documents. Existing QA datasets sidestep this challenge by providing short, well-defined contexts that are unrealistic in real-world applications. We present a three-stage document QA approach: (1) text extraction from PDF; (2) evidence retrieval from extracted texts to form well-posed contexts; (3) QA to extract knowledge from contexts to return high-quality answers -- extractive, abstractive, or Boolean. Using QASPER for evaluation, our detect-retrieve-comprehend (DRC) system achieves a +7.19 improvement in Answer-F1 over existing baselines while delivering superior context selection. Our results demonstrate that DRC holds tremendous promise as a flexible framework for practical scientific document QA.

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