GAAMA 2.0: An Integrated System that Answers Boolean and Extractive Questions
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Abstract (arXiv)
Recent machine reading comprehension datasets include extractive and boolean questions but current approaches do not offer integrated support for answering both question types. We present a multilingual machine reading comprehension system and front-end demo that handles boolean questions by providing both a YES/NO answer and highlighting supporting evidence, and handles extractive questions by highlighting the answer in the passage. Our system, GAAMA 2.0, is ranked first on the Tydi QA leaderboard at the time of this writing. We contrast two different implementations of our approach. The first includes several independent stacks of transformers allowing easy deployment of each component. The second is a single stack of transformers utilizing adapters to reduce GPU memory footprint in a resource-constrained environment.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 16 Jun 2022.
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18 of 25 references.
References · 18
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Parameter-Efficient Transfer Learning for NLPcited by 4,195
- 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2019BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questionscited by 2,140
- 2016MS MARCO: A Human Generated MAchine Reading COmprehension Datasetcited by 1,591
- 2020AdapterFusion: Non-Destructive Task Composition for Transfer Learningcited by 780
- 2020TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languagescited by 486
- 2020MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfercited by 443
- 2020AdapterHub: A Framework for Adapting Transformerscited by 441
- 2019End-to-End Open-Domain Question Answering with BERTserinicited by 152
- 2021Poolingformer: Long Document Modeling with Pooling Attentioncited by 60
6 more
- 2019A BERT Baseline for the Natural Questionscited by 33
- 2020Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehensioncited by 17
- 2020Talk to Papers: Bringing Neural Question Answering to Academic Searchcited by 5
- 2021Do Answers to Boolean Questions Need Explanations? Yescited by 5
- 2020Improved Synthetic Training for Reading Comprehensioncited by 4
- 2019CFO: A Framework for Building Production NLP Systemscited by 2
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