Quarel: A dataset and models for answering questions about qualitative relationships. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 30 October–2 November 2018 pp. Coqa: A conversational question answering challenge. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 9–11 September 2017 pp. RACE: Large-scale ReAding Comprehension Dataset From Examinations. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, WA, USA, 18–21 October 2013 pp. Mctest: A challenge dataset for the open-domain machine comprehension of text. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), Melbourne, Australia, 15–20 July 2018 Volume 1 (Long Papers), pp. Looking beyond the surface: A challenge set for reading comprehension over multiple sentences. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada, 12–15 July 2017 Volume 1: Long Papers, pp. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension. Natural questions: A benchmark for question answering research. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX, USA, 1–5 November 2016 pp. SQuAD: 100,000+ Questions for Machine Comprehension of Text. Building Watson: An overview of the DeepQA project. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), Philadelphia, PA, USA, 6–7 July 2002 pp. An analysis of the AskMSR question-answering system. In Proceedings of the 38th annual meeting of the Association for Computational Linguistics, Hong Kong, China, 10–12 October 2000 pp. The structure and performance of an open-domain question answering system. The TREC-8 question answering track report. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. We were able to achieve 42% F1 and 39% exact match score (EM) end-to-end with no domain-specific training. Our custom-built extractor model is created from a pretrained language model and fine-tuned on the the Stanford Question Answering Dataset-SQuAD and Natural Questions datasets. In this paper, we conducted experiments on several information retrieval systems and extractive language models, attempting to find the yes–no–none answers and text answers in the same pass. To test our solution, we are introducing a new dataset for open-book QA based on real customer questions on AWS technical documentation. We present a two-step, retriever–extractor architecture in which a retriever finds the right documents and an extractor finds the answers in the retrieved documents. These questions have a yes–no–none answer and a text answer which can be short (a few words) or long (a few sentences). This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). Open-book question answering is a subset of question answering (QA) tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic.
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