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End-to-End Open-Domain Question Answering with BERTserini

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arxiv 1902.01718 v2 pith:A2QKRC7X submitted 2019-02-05 cs.CL cs.IR

Wei Yang , Yuqing Xie , Aileen Lin , Xingyu Li , Luchen Tan , Kun Xiong , Ming Li , Jimmy Lin This is my paper · ORCID
classification cs.CLcs.IR
keywords answeringend-to-endquestionbertintegrateslargesystemaccuracy
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We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit. In contrast to most question answering and reading comprehension models today, which operate over small amounts of input text, our system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles in an end-to-end fashion. We report large improvements over previous results on a standard benchmark test collection, showing that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identifying answer spans.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing

    cs.CL 2019-08 conditional novelty 6.0 of 10

    GRANNO labels real user utterances by having crowd workers detect matching grammar-generated paraphrases, improving semantic parsing accuracy from 51.3 to 70.6 on GEOQUERY and SCHOLAR.

  2. Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering

    cs.CL 2019-08 accept novelty 6.0 of 10

    Applying global normalization across passages, 100-word sliding windows, and a passage ranker to BERT yields state-of-the-art open-domain QA results on four benchmarks.

  3. SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models

    cs.CR 2025-02 conditional novelty 4.0 of 10

    SecPE uses a log-depth max tree to compute encrypted argmax, cutting the cost of private prompt ensembling by up to 35x with negligible accuracy loss on benchmarks.

  4. A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints

    cs.IR 2019-08 conditional novelty 4.0 of 10

    BERT fine-tuning substantially improves non-factoid passage re-ranking over prior baselines, with a 256-token input window performing best and chunking providing a workaround for longer passages.

  5. CFO: A Framework for Building Production NLP Systems

    cs.CL 2019-08 conditional novelty 4.0 of 10

    The paper presents CFO, a containerized orchestration framework for building production NLP systems, and GAAMA, a question answering system that combines BM25 retrieval with BERT, with empirical results on NQ and SQuAD 2.0.

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