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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 – 5 of 5 1 Improving Span Representation for Domain-adapted Coreference Resolution ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Field, Anjalie; Gandhi, Nupoor. - : Underline Science Inc., 2021 BASE Show details 2 Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Han, Xiaochuang; Tsvetkov, Yulia. - : Underline Science Inc., 2021 BASE Show details 3 SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Balachandran, Vidhisha; Hovy, Eduard. - : Underline Science Inc., 2021 BASE Show details 4 Efficient Test Time Adapter Ensembling for Low-resource Language Varieties ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Sebastian; Neubig, Graham. - : Underline Science Inc., 2021 BASE Show details 5 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Chaudhary, Aditi; Mortensen, David R.; Neubig, Graham; Pratapa, Adithya; Rijhwani, Shruti; Tsvetkov, Yulia. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.570/ Abstract: Text generation systems are ubiquitous in natural language processing applications. However, evaluation of these systems remains a challenge, especially in multilingual settings. In this paper, we propose L'AMBRE -- a metric to evaluate the morphosyntactic well-formedness of text using its dependency parse and morphosyntactic rules of the language. We present a way to automatically extract various rules governing morphosyntax directly from dependency treebanks. To tackle the noisy outputs from text generation systems, we propose a simple methodology to train robust parsers. We show the effectiveness of our metric on the task of machine translation through a diachronic study of systems translating into morphologically-rich languages. ... Keyword: Data Management System; Machine Learning; Machine translation; Natural Language Processing URL: https://dx.doi.org/10.48448/dzpt-d487 https://underline.io/lecture/37414-evaluating-the-morphosyntactic-well-formedness-of-generated-texts BASE Hide details

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