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Found 14 papers, 3 papers with code
Linguistic Evaluation for the 2021 State-of-the-art Machine Translation Systems for German to English and English to German
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.
Perceptual Quality Dimensions of Machine-Generated Text with a Focus on Machine Translation
The quality of machine-generated text is a complex construct consisting of various aspects and dimensions.
A Linguistically Motivated Test Suite to Semi-Automatically Evaluate German–English Machine Translation Output
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.
Observing the Learning Curve of NMT Systems With Regard to Linguistic Phenomena
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.
Fine-grained linguistic evaluation for state-of-the-art 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).
Linguistic evaluation of German-English Machine Translation using a Test Suite
We present the results of the application of a grammatical test suite for German$\rightarrow$English MT on the systems submitted at WMT19, with a detailed analysis for 107 phenomena organized in 14 categories.
Fine-grained evaluation of German-English Machine Translation based on a Test Suite
We present an analysis of 16 state-of-the-art MT systems on German-English based on a linguistically-motivated test suite.
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically-motivated Test Suite
We present an alternative method of evaluating Quality Estimation systems, which is based on a linguistically-motivated Test Suite.
Train, Sort, Explain: Learning to Diagnose Translation Models
Evaluating translation models is a trade-off between effort and detail.
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.
