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Found 13 papers, 4 papers with code
MTee: Open Machine Translation Platform for Estonian Government
We present the MTee project - a research initiative funded via an Estonian public procurement to develop machine translation technology that is open-source and free of charge.
From Zero to Production: Baltic-Ukrainian Machine Translation Systems to Aid Refugees
In this paper, we examine the development and usage of six low-resource machine translation systems translating between the Ukrainian language and each of the official languages of the Baltic states.
Open Terminology Management and Sharing Toolkit for Federation of Terminology Databases
Consolidated access to current and reliable terms from different subject fields and languages is necessary for content creators and translators.
Dynamic Terminology Integration for COVID-19 and other Emerging Domains
The majority of language domains require prudent use of terminology to ensure clarity and adequacy of information conveyed.
Facilitating Terminology Translation with Target Lemma Annotations
Most of the recent work on terminology integration in machine translation has assumed that terminology translations are given already inflected in forms that are suitable for the target language sentence.
Mitigating Gender Bias in Machine Translation with Target Gender Annotations
to a language with grammatical gender, it might be necessary to determine the gender of the subject "secretary".
Robust Neural Machine Translation: Modeling Orthographic and Interpunctual Variation
Neural machine translation systems typically are trained on curated corpora and break when faced with non-standard orthography or punctuation.
Training Data Augmentation for Context-Sensitive Neural Lemmatizer Using Inflection Tables and Raw Text
Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form.
Training Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text
Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form.
Context Sensitive Neural Lemmatization with Lematus
The main motivation for developing contextsensitive lemmatizers is to improve performance on unseen and ambiguous words.
From Segmentation to Analyses: a Probabilistic Model for Unsupervised Morphology Induction
A major motivation for unsupervised morphological analysis is to reduce the sparse data problem in under-resourced languages.
