Shushen Manakhimova


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Linguistically Motivated Evaluation of the 2023 State-of-the-art Machine Translation: Can ChatGPT Outperform NMT?
Shushen Manakhimova | Eleftherios Avramidis | Vivien Macketanz | Ekaterina Lapshinova-Koltunski | Sergei Bagdasarov | Sebastian Möller
Proceedings of the Eighth Conference on Machine Translation

This paper offers a fine-grained analysis of the machine translation outputs in the context of the Shared Task at the 8th Conference of Machine Translation (WMT23). Building on the foundation of previous test suite efforts, our analysis includes Large Language Models and an updated test set featuring new linguistic phenomena. To our knowledge, this is the first fine-grained linguistic analysis for the GPT-4 translation outputs. Our evaluation spans German-English, English-German, and English-Russian language directions. Some of the phenomena with the lowest accuracies for German-English are idioms and resultative predicates. For English-German, these include mediopassive voice, and noun formation(er). As for English-Russian, these included idioms and semantic roles. GPT-4 performs equally or comparably to the best systems in German-English and English-German but falls in the second significance cluster for English-Russian.

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Challenging the State-of-the-art Machine Translation Metrics from a Linguistic Perspective
Eleftherios Avramidis | Shushen Manakhimova | Vivien Macketanz | Sebastian Möller
Proceedings of the Eighth Conference on Machine Translation

We employ a linguistically motivated challenge set in order to evaluate the state-of-the-art machine translation metrics submitted to the Metrics Shared Task of the 8th Conference for Machine Translation. The challenge set includes about 21,000 items extracted from 155 machine translation systems for three language directions, covering more than 100 linguistically-motivated phenomena organized in 14 categories. The metrics that have the best performance with regard to our linguistically motivated analysis are the Cometoid22-wmt23 (a trained metric based on distillation) for German-English and MetricX-23-c (based on a fine-tuned mT5 encoder-decoder language model) for English-German and English-Russian. Some of the most difficult phenomena are passive voice for German-English, named entities, terminology and measurement units for English-German, and focus particles, adverbial clause and stripping for English-Russian.


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Linguistically Motivated Evaluation of the 2022 State-of-the-art Machine Translation Systems for Three Language Directions
Vivien Macketanz | Shushen Manakhimova | Eleftherios Avramidis | Ekaterina Lapshinova-koltunski | Sergei Bagdasarov | Sebastian Möller
Proceedings of the Seventh Conference on Machine Translation (WMT)

This document describes a fine-grained linguistically motivated analysis of 29 machine translation systems submitted at the Shared Task of the 7th Conference of Machine Translation (WMT22). This submission expands the test suite work of previous years by adding the language direction of English–Russian. As a result, evaluation takes place for the language directions of German–English, English–German, and English–Russian. We find that the German–English systems suffer in translating idioms, some tenses of modal verbs, and resultative predicates, the English–German ones in idioms, transitive-past progressive, and middle voice, whereas the English–Russian ones in pseudogapping and idioms.

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A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output
Vivien Macketanz | Eleftherios Avramidis | Aljoscha Burchardt | He Wang | Renlong Ai | Shushen Manakhimova | Ursula Strohriegel | Sebastian Möller | Hans Uszkoreit
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper presents a fine-grained test suite for the language pair German–English. The test suite is based on a number of linguistically motivated categories and phenomena and the semi-automatic evaluation is carried out with regular expressions. We describe the creation and implementation of the test suite in detail, providing a full list of all categories and phenomena. Furthermore, we present various exemplary applications of our test suite that have been implemented in the past years, like contributions to the Conference of Machine Translation, the usage of the test suite and MT outputs for quality estimation, and the expansion of the test suite to the language pair Portuguese–English. We describe how we tracked the development of the performance of various systems MT systems over the years with the help of the test suite and which categories and phenomena are prone to resulting in MT errors. For the first time, we also make a large part of our test suite publicly available to the research community.


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Linguistic Evaluation for the 2021 State-of-the-art Machine Translation Systems for German to English and English to German
Vivien Macketanz | Eleftherios Avramidis | Shushen Manakhimova | Sebastian Möller
Proceedings of the Sixth Conference on Machine Translation

We are using a semi-automated test suite in order to provide a fine-grained linguistic evaluation for state-of-the-art machine translation systems. The evaluation includes 18 German to English and 18 English to German systems, submitted to the Translation Shared Task of the 2021 Conference on Machine Translation. Our submission adds up to the submissions of the previous years by creating and applying a wide-range test suite for English to German as a new language pair. The fine-grained evaluation allows spotting significant differences between systems that cannot be distinguished by the direct assessment of the human evaluation campaign. We find that most of the systems achieve good accuracies in the majority of linguistic phenomena but there are few phenomena with lower accuracy, such as the idioms, the modal pluperfect and the German resultative predicates. Two systems have significantly better test suite accuracy in macro-average in every language direction, Online-W and Facebook-AI for German to English and VolcTrans and Online-W for English to German. The systems show a steady improvement as compared to previous years.