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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 – 7 of 7 1 Bi-directional Domain Adaptation Using Weighted MTL ... 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; Dakota, Daniel; Kübler, Sandra; Sayyed, Zeeshan. - : Underline Science Inc., 2021 Abstract: Domain adaption in syntactic parsing is still a significant challenge. We address the issue of data imbalance between the in-domain and out-of-domain treebank typically used for the problem. We define domain adaptation as a Multi-task learning (MTL) problem, which allows us to train two parsers, one for each domain. Our results show that the MTL approach is beneficial for the smaller treebank. For the larger treebank, we need to use loss weighting in order to avoid a decrease in performance below the single task. In order to determine to what degree the data imbalance between two domains and the domain differences affect results, we also carry out an experiment with two imbalanced in-domain treebanks and show that loss weighting also improves performance in an in-domain setting. Given loss weighting in MTL, we can improve results for both parsers. ... Keyword: Computational Linguistics; Condensed Matter Physics; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/5jt1-hh42 https://underline.io/lecture/29935-bi-directional-domain-adaptation-using-weighted-mtl BASE Hide details 2 What's in a Span? Evaluating the Creativity of a Span-Based Neural Constituency Parser Dakota, Daniel; Kübler, Sandra In: Proceedings of the Society for Computation in Linguistics (2021) BASE Show details 3 “My Curiosity was Satisfied, but not in a Good Way”: Predicting User Ratings for Online Recipes [Online resource] Liu, Can; Guo, Chun; Dakota, Daniel. IDS-Repository Show details 4 Parsing German: How Much Morphology Do We Need? [Online resource] Maier, Wolfgang; Kübler, Sandra; Dakota, Daniel. IDS-Repository Show details 5 From Discourse Representation Structure to Event Semantics: A Simple Conversion? [Online resource] Dakota, Daniel; Kübler, Sandra IDS-Repository Show details 6 IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter [Online resource] Liu, Can; Li, Wen; Demarest, Bradford. IDS-Repository Show details 7 Do FreeWord Order Languages Need More Treebank Data? Investigating Dative Alternation in German, English, and Russian [Online resource] Dakota, Daniel; Gilmanov, Timur; Li, Wen. IDS-Repository Show details

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