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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 – 6 of 6 1 Multilingual Event Linking to Wikidata ... Pratapa, Adithya; Gupta, Rishubh; Mitamura, Teruko. - : arXiv, 2022 BASE Show details 2 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... Pratapa, Adithya; Anastasopoulos, Antonios; Rijhwani, Shruti. - : arXiv, 2021 BASE Show details 3 Cross-document Event Identity via Dense Annotation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Hasegawa, Kimihiro; Li, Linwei. - : Underline Science Inc., 2021 BASE Show details 4 Evaluating the Morphosyntactic Well-formedness of Generated Texts ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Anastasopoulos, Antonios; Chaudhary, Aditi. - : Underline Science Inc., 2021 BASE Show details 5 A Study of Morphological Robustness of Neural Machine Translation ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Jayanthi, Sai Muralidhar; Pratapa, Adithya. - : Underline Science Inc., 2021 Abstract: In this work, we analyze the robustness of neural machine translation systems towards grammatical perturbations in the source. In particular, we focus on morphological inflection related perturbations. While this has been recently studied for English->French translation (Morpheus, Tan et al. 2020), it is unclear how this extends to Any->English translation systems. We propose Morpheus-Multilingual that utilizes UniMorph dictionaries to identify morphological perturbations to source that adversely affect the translation models. Along with an analysis of state-of-the-art pretrained MT systems, we train and analyze systems for 11 language pairs using the multilingual TED corpus. We also compare this to actual errors of non-native speakers using Grammatical Error Correction datasets. Finally, we present a qualitative and quantitative analysis of the robustness of Any->English translation systems. ... URL: https://underline.io/lecture/30088-a-study-of-morphological-robustness-of-neural-machine-translation https://dx.doi.org/10.48448/1yca-n456 BASE Hide details 6 Automatic Extraction of Rules Governing Morphological Agreement ... Chaudhary, Aditi; Anastasopoulos, Antonios; Pratapa, Adithya. - : arXiv, 2020 BASE Show details

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