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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 4 of 4 1 Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence Annotation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Boyd-Graber, Jordan; Daumé III, Hal; Xiong, Chenyan; Zhao, Chen. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.756/ Abstract: Open-domain question answering answers a question based on evidence retrieved from a large corpus. State-of-the-art neural approaches require intermediate evidence annotations for training. However, such intermediate annotations are expensive, and methods that rely on them cannot transfer to the more common setting, where only question–answer pairs are available. This paper investigates whether models can learn to find evidence from a large corpus, with only distant supervision from answer labels for model training, thereby generating no additional annotation cost. We introduce a novel approach (DistDR) that iteratively improves over a weak retriever by alternately finding evidence from the up-to-date model and encouraging the model to learn the most likely evidence. Without using any evidence labels, DistDR is on par with fully-supervised state-of-the-art methods on both multi-hop and single-hop QA benchmarks. Our analysis ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Question-Answering Systems URL: https://underline.io/lecture/37542-distantly-supervised-dense-retrieval-enables-open-domain-question-answering-without-evidence-annotation https://dx.doi.org/10.48448/4nba-ab53 BASE Hide details 2 What's in a Name? Answer Equivalence For Open-Domain Question Answering ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Jordan; Si, Chenglei. - : Underline Science Inc., 2021 BASE Show details 3 On the Potential of Lexico-logical Alignments for Semantic Parsing to SQL Queries ... Shi, Tianze; Zhao, Chen; Boyd-Graber, Jordan. - : arXiv, 2020 BASE Show details 4 Non-native homonym processing : an ERP measurement Hu, Jiehui; Zhang, Wenpeng; Zhao, Chen. - : International Society for Bioelectromagnetism, 2011 BASE Show details

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