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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 Page: 1 2 3 Hits 41 – 45 of 45 41 MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Daiber, Joachim; Longpre, Shayne. - : Underline Science Inc., 2021 BASE Show details 42 Just Ask! Evaluating Machine Translation by Asking and Answering Questions ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Marie-Francine; Ghadery, Erfan. - : Underline Science Inc., 2021 BASE Show details 43 Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Svitlana; Kanoulas, Evangelos. - : Underline Science Inc., 2021 BASE Show details 44 Mitigating False-Negative Contexts in Multi-document Question Answering with Retrieval Marginalization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Dasigi, Pradeep; Gardner, Matt; Ni, Ansong. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.497/ Abstract: Question Answering (QA) tasks requiring information from multiple documents often rely on a retrieval model to identify relevant information for reasoning. The retrieval model is typically trained to maximize the likelihood of the labeled supporting evidence. However, when retrieving from large text corpora such as Wikipedia, the correct answer can often be obtained from multiple evidence candidates. Moreover, not all such candidates are labeled as positive during annotation, rendering the training signal weak and noisy. This problem is exacerbated when the questions are unanswerable or when the answers are Boolean, since the model cannot rely on lexical overlap to make a connection between the answer and supporting evidence. We develop a new parameterization of set-valued retrieval that handles unanswerable queries, and we show that marginalizing over this set during training allows a model to mitigate false negatives in ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Question-Answering Systems URL: https://dx.doi.org/10.48448/d96m-4f64 https://underline.io/lecture/38033-mitigating-false-negative-contexts-in-multi-document-question-answering-with-retrieval-marginalization BASE Hide details 45 SOM-NCSCM : An Efficient Neural Chinese Sentence Compression Model Enhanced with Self-Organizing Map ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cao, Yanan; Cao, Cungen. - : Underline Science Inc., 2021 BASE Show details Page: 1 2 3

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