MARMOT: A Toolkit for Translation Quality Estimation at the Word Level

By Varvara Logacheva, Chris Hokamp, Lucia Specia. 2016

21 citations recorded by the source corpus.

We present Marmot~― a new toolkit for quality estimation (QE) of machine translation output. Marmot contains utilities targeted at quality estimation at the word and phrase level. However, due to its flexibility and modularity, it can also be extended to work at the sentence level. In addition, it can be used as a framework for extracting features and learning models for many common natural language processing tasks. The tool has a set of state-of-the-art features for QE, and new features can easily be added. The tool is open-source and can be downloaded from https://github.com/qe-team/marmot/

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Findings of the 2016 Conference on Machine Translation (2016, methodology): “Word-level baseline system: For Tasks 2 and 2p, the baseline features were extracted with the Marmot tool (Logacheva et al., 2016b).”
Findings of the 2017 Conference on Machine Translation (WMT17) (2017, methodology): “Word-level baseline system: For Task 2, the baseline features were extracted with the MARMOT tool (Logacheva et al., 2016).”
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation (2017, background): “…standard tools of structured prediction to solve it, i.e. structured predictors such as CRFs or structured SVMs, which take advantage of sparse representations and very large feature sets, as well as dependencies between labels in the output sequence (Logacheva et al., 2016; Martins et al., 2016).”