Papers by Vivien Macketanz
Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena (2021.acl-srw)
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| Challenge: | Using a semi-automatic process, we observe the linguistic performance of various neural machine translation models. |
| Approach: | They observe the linguistic performance of a neural machine translation model on several steps on the training process. |
| Outcome: | The proposed system performs well on training of English-to-German models. |
Train, Sort, Explain: Learning to Diagnose Translation Models (N19-4)
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| Challenge: | Evaluating translation models is a trade-off between effort and detail. |
| Approach: | They propose to use a neural text classifier to automatically expose systematic differences between human and machine translations to human experts. |
| Outcome: | The proposed method exposes systematic differences between human and machine translations to human experts. |
TQ-AutoTest – An Automated Test Suite for (Machine) Translation Quality (L18-1)
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| Challenge: | Especially the trend towards neural MT has renewed peoples' interest in better and more analytical diagnostic methods for MT quality. |
| Approach: | They propose a framework that supports a linguistic evaluation of machine translations using test suites. |
| Outcome: | The proposed framework supports linguistic evaluation of (machine) translations using test suites. |
A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output (2022.lrec-1)
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Vivien Macketanz, Eleftherios Avramidis, Aljoscha Burchardt, He Wang, Renlong Ai, Shushen Manakhimova, Ursula Strohriegel, Sebastian Möller, Hans Uszkoreit
| Challenge: | Using fine-grained evaluation techniques, translation outputs have become better and more fluent. |
| Approach: | They propose a fine-grained test suite for the language pair German–English . they describe the creation and implementation of the test suite in detail . |
| Outcome: | The proposed test suite is based on linguistically motivated categories and phenomena and semi-automatic evaluation is carried out with regular expressions. |