Marion Weller-Di Marco


2020

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Modeling Word Formation in English–German Neural Machine Translation
Marion Weller-Di Marco | Alexander Fraser
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper studies strategies to model word formation in NMT using rich linguistic information, namely a word segmentation approach that goes beyond splitting into substrings by considering fusional morphology. Our linguistically sound segmentation is combined with a method for target-side inflection to accommodate modeling word formation. The best system variants employ source-side morphological analysis and model complex target-side words, improving over a standard system.

2017

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Addressing Problems across Linguistic Levels in SMT: Combining Approaches to Model Morphology, Syntax and Lexical Choice
Marion Weller-Di Marco | Alexander Fraser | Sabine Schulte im Walde
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Many errors in phrase-based SMT can be attributed to problems on three linguistic levels: morphological complexity in the target language, structural differences and lexical choice. We explore combinations of linguistically motivated approaches to address these problems in English-to-German SMT and show that they are complementary to one another, but also that the popular verbal pre-ordering can cause problems on the morphological and lexical level. A discriminative classifier can overcome these problems, in particular when enriching standard lexical features with features geared towards verbal inflection.

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Simple Compound Splitting for German
Marion Weller-Di Marco
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)

This paper presents a simple method for German compound splitting that combines a basic frequency-based approach with a form-to-lemma mapping to approximate morphological operations. With the exception of a small set of hand-crafted rules for modeling transitional elements, this approach is resource-poor. In our evaluation, the simple splitter outperforms a splitter relying on rich morphological resources.

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Modeling Target-Side Inflection in Neural Machine Translation
Aleš Tamchyna | Marion Weller-Di Marco | Alexander Fraser
Proceedings of the Second Conference on Machine Translation

2016

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Graph-based Clustering of Synonym Senses for German Particle Verbs
Moritz Wittmann | Marion Weller-Di Marco | Sabine Schulte im Walde
Proceedings of the 12th Workshop on Multiword Expressions

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Modeling Complement Types in Phrase-Based SMT
Marion Weller-Di Marco | Alexander Fraser | Sabine Schulte im Walde
Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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Phrase-Based SMT for Finnish with More Data, Better Models and Alternative Alignment and Translation Tools
Jörg Tiedemann | Fabienne Cap | Jenna Kanerva | Filip Ginter | Sara Stymne | Robert Östling | Marion Weller-Di Marco
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers