Vivien Macketanz


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Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena
Patrick Stadler | Vivien Macketanz | Eleftherios Avramidis
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop

In this paper we present our observations and evaluations by observing the linguistic performance of the system on several steps on the training process of various English-to-German Neural Machine Translation models. The linguistic performance is measured through a semi-automatic process using a test suite. Among several linguistic observations, we find that the translation quality of some linguistic categories decreased within the recorded iterations. Additionally, we notice some drops of the translation quality of certain categories when using a larger corpus.


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Fine-grained linguistic evaluation for state-of-the-art Machine Translation
Eleftherios Avramidis | Vivien Macketanz | Ursula Strohriegel | Aljoscha Burchardt | Sebastian Möller
Proceedings of the Fifth Conference on Machine Translation

This paper describes a test suite submission providing detailed statistics of linguistic performance for the state-of-the-art German-English systems of the Fifth Conference of Machine Translation (WMT20). The analysis covers 107 phenomena organized in 14 categories based on about 5,500 test items, including a manual annotation effort of 45 person hours. Two systems (Tohoku and Huoshan) appear to have significantly better test suite accuracy than the others, although the best system of WMT20 is not significantly better than the one from WMT19 in a macro-average. Additionally, we identify some linguistic phenomena where all systems suffer (such as idioms, resultative predicates and pluperfect), but we are also able to identify particular weaknesses for individual systems (such as quotation marks, lexical ambiguity and sluicing). Most of the systems of WMT19 which submitted new versions this year show improvements.


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Train, Sort, Explain: Learning to Diagnose Translation Models
Robert Schwarzenberg | David Harbecke | Vivien Macketanz | Eleftherios Avramidis | Sebastian Möller
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)

Evaluating translation models is a trade-off between effort and detail. On the one end of the spectrum there are automatic count-based methods such as BLEU, on the other end linguistic evaluations by humans, which arguably are more informative but also require a disproportionately high effort. To narrow the spectrum, we propose a general approach on how to automatically expose systematic differences between human and machine translations to human experts. Inspired by adversarial settings, we train a neural text classifier to distinguish human from machine translations. A classifier that performs and generalizes well after training should recognize systematic differences between the two classes, which we uncover with neural explainability methods. Our proof-of-concept implementation, DiaMaT, is open source. Applied to a dataset translated by a state-of-the-art neural Transformer model, DiaMaT achieves a classification accuracy of 75% and exposes meaningful differences between humans and the Transformer, amidst the current discussion about human parity.

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Linguistic Evaluation of German-English Machine Translation Using a Test Suite
Eleftherios Avramidis | Vivien Macketanz | Ursula Strohriegel | Hans Uszkoreit
Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)

We present the results of the application of a grammatical test suite for German-to-English MT on the systems submitted at WMT19, with a detailed analysis for 107 phenomena organized in 14 categories. The systems still translate wrong one out of four test items in average. Low performance is indicated for idioms, modals, pseudo-clefts, multi-word expressions and verb valency. When compared to last year, there has been a improvement of function words, non verbal agreement and punctuation. More detailed conclusions about particular systems and phenomena are also presented.


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TQ-AutoTest – An Automated Test Suite for (Machine) Translation Quality
Vivien Macketanz | Renlong Ai | Aljoscha Burchardt | Hans Uszkoreit
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically motivated Test Suite
Eleftherios Avramidis | Vivien Macketanz | Arle Lommel | Hans Uszkoreit
Proceedings of the AMTA 2018 Workshop on Translation Quality Estimation and Automatic Post-Editing

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Fine-grained evaluation of German-English Machine Translation based on a Test Suite
Vivien Macketanz | Eleftherios Avramidis | Aljoscha Burchardt | Hans Uszkoreit
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We present an analysis of 16 state-of-the-art MT systems on German-English based on a linguistically-motivated test suite. The test suite has been devised manually by a team of language professionals in order to cover a broad variety of linguistic phenomena that MT often fails to translate properly. It contains 5,000 test sentences covering 106 linguistic phenomena in 14 categories, with an increased focus on verb tenses, aspects and moods. The MT outputs are evaluated in a semi-automatic way through regular expressions that focus only on the part of the sentence that is relevant to each phenomenon. Through our analysis, we are able to compare systems based on their performance on these categories. Additionally, we reveal strengths and weaknesses of particular systems and we identify grammatical phenomena where the overall performance of MT is relatively low.


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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Daniel Zeman | Martin Popel | Milan Straka | Jan Hajič | Joakim Nivre | Filip Ginter | Juhani Luotolahti | Sampo Pyysalo | Slav Petrov | Martin Potthast | Francis Tyers | Elena Badmaeva | Memduh Gokirmak | Anna Nedoluzhko | Silvie Cinková | Jan Hajič jr. | Jaroslava Hlaváčová | Václava Kettnerová | Zdeňka Urešová | Jenna Kanerva | Stina Ojala | Anna Missilä | Christopher D. Manning | Sebastian Schuster | Siva Reddy | Dima Taji | Nizar Habash | Herman Leung | Marie-Catherine de Marneffe | Manuela Sanguinetti | Maria Simi | Hiroshi Kanayama | Valeria de Paiva | Kira Droganova | Héctor Martínez Alonso | Çağrı Çöltekin | Umut Sulubacak | Hans Uszkoreit | Vivien Macketanz | Aljoscha Burchardt | Kim Harris | Katrin Marheinecke | Georg Rehm | Tolga Kayadelen | Mohammed Attia | Ali Elkahky | Zhuoran Yu | Emily Pitler | Saran Lertpradit | Michael Mandl | Jesse Kirchner | Hector Fernandez Alcalde | Jana Strnadová | Esha Banerjee | Ruli Manurung | Antonio Stella | Atsuko Shimada | Sookyoung Kwak | Gustavo Mendonça | Tatiana Lando | Rattima Nitisaroj | Josie Li
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.


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DFKI’s system for WMT16 IT-domain task, including analysis of systematic errors
Eleftherios Avramidis | Aljoscha Burchardt | Vivien Macketanz | Ankit Srivastava
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Deeper Machine Translation and Evaluation for German
Eleftherios Avramidis | Vivien Macketanz | Aljoscha Burchardt | Jindrich Helcl | Hans Uszkoreit
Proceedings of the 2nd Deep Machine Translation Workshop