Marion Weller-Di Marco

Also published as: Marion Di Marco


2024

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Analyzing the Understanding of Morphologically Complex Words in Large Language Models
Marion Weller-Di Marco | Alexander Fraser
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

We empirically study the ability of a Large Language Model (gpt-3.5-turbo-instruct) to understand morphologically complex words. In our experiments, we looked at a variety of tasks to analyse German compounds with regard to compositional word formation and derivation, such as identifying the head noun of existing and novel compounds, identifying the shared verb stem between two words, or recognizing words constructed with inappropriately used derivation morphemes as invalid. Our results show that the language model is generally capable of solving most tasks, except for the task of identifying ill-formed word forms. While the model demonstrated a good overall understanding of complex words and their word-internal structure, the results also suggest that there is no formal knowledge of derivational rules, but rather an interpretation of the observed word parts to derive the meaning of a word.

2023

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A Study on Accessing Linguistic Information in Pre-Trained Language Models by Using Prompts
Marion Di Marco | Katharina Hämmerl | Alexander Fraser
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

We study whether linguistic information in pre-trained multilingual language models can be accessed by human language: So far, there is no easy method to directly obtain linguistic information and gain insights into the linguistic principles encoded in such models. We use the technique of prompting and formulate linguistic tasks to test the LM’s access to explicit grammatical principles and study how effective this method is at providing access to linguistic features. Our experiments on German, Icelandic and Spanish show that some linguistic properties can in fact be accessed through prompting, whereas others are harder to capture.

2022

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Test Suite Evaluation: Morphological Challenges and Pronoun Translation
Marion Weller-Di Marco | Alexander Fraser
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper summarizes the results of our test suite evaluation with a main focus on morphology for the language pairs English to/from German. We look at the translation of morphologically complex words (DE–EN), and evaluatewhether English noun phrases are translated as compounds vs. phrases into German. Furthermore, we investigate the preservation of morphological features (gender in EN–DE pronoun translation and number in morpho-syntacticallycomplex structures for DE–EN). Our results indicate that systems are able to interpret linguistic structures to obtain relevant information, but also that translation becomes more challenging with increasing complexity, as seen, for example, when translating words with negation or non-concatenative properties, and for the morecomplex cases of the pronoun translation task.

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Findings of the WMT 2022 Shared Tasks in Unsupervised MT and Very Low Resource Supervised MT
Marion Weller-Di Marco | Alexander Fraser
Proceedings of the Seventh Conference on Machine Translation (WMT)

We present the findings of the WMT2022Shared Tasks in Unsupervised MT and VeryLow Resource Supervised MT with experiments on the language pairs German to/fromUpper Sorbian, German to/from Lower Sorbian and Lower Sorbian to/from Upper Sorbian. Upper and Lower Sorbian are minoritylanguages spoken in the Eastern parts of Germany. There are active language communitiesworking on the preservation of the languageswho also made the data used in this Shared Taskavailable.In total, four teams participated on this SharedTask, with submissions from three teams for theunsupervised sub task, and submissions fromall four teams for the supervised sub task. Inthis overview paper, we present and discuss theresults.

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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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

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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.

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