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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 Multilingual Neural Semantic Parsing for Low-Resourced Languages ... Xia, Menglin; Monti, Emilio. - : arXiv, 2021 BASE Show details 2 One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets ... 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; Damonte, Marco; Monti, Emilio. - : Underline Science Inc., 2021 BASE Show details 3 In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering ... 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; Aletras, Nikolaos; Barrault, Loïc. - : Underline Science Inc., 2021 BASE Show details 4 Multilingual Neural Semantic Parsing for Low-Resourced Languages ... 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; Monti, Emilio; Xia, Menglin. - : Underline Science Inc., 2021 Abstract: Multilingual semantic parsing is a cost-effective method that allows a single model to understand different languages. However, researchers face a great imbalance of availability of training data, with English being resource rich, and other languages having much less data. To tackle the data limitation problem, we propose using machine translation to bootstrap multilingual training data from the more abundant English data. To compensate for the data quality of machine translated training data, we utilize transfer learning from pretrained multilingual encoders to further improve the model. To evaluate our multilingual models on human-written sentences as opposed to machine translated ones, we introduce a new multilingual semantic parsing dataset in English, Italian and Japanese based on the Facebook Task Oriented Parsing (TOP) dataset. We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset and outperforms the state-of-the-art model on the ... Keyword: Computational Linguistics; Data Management System; FOS Languages and literature; Linguistics; Natural Language Processing; Semantics; Translation Studies URL: https://dx.doi.org/10.48448/pz0n-0053 https://underline.io/lecture/29775-multilingual-neural-semantic-parsing-for-low-resourced-languages BASE Hide details

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