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An Overview of the SEBAMAT Project
SEBAMAT (semantics-based MT) is a Marie Curie project intended to con-tribute to the state of the art in machine translation (MT).
Using Semantic Role Labeling to Improve Neural Machine Translation
We then train a neural machine translation (NMT) system using the annotated corpus on the source language side, and the original unannotated corpus on the target language side.
Similar Language Translation for Catalan, Portuguese and Spanish Using Marian NMT
This paper describes the SEBAMAT contribution to the 2021 WMT Similar Language Translation shared task.
Overview of the Fourth BUCC Shared Task: Bilingual Dictionary Induction from Comparable Corpora
The shared task of the 13th Workshop on Building and Using Comparable Corpora was devoted to the induction of bilingual dictionaries from comparable rather than parallel corpora.
Overview of the Second BUCC Shared Task: Spotting Parallel Sentences in Comparable Corpora
We examined manually a small sample of the false negative sentence pairs for the most precise French-English runs and estimated the number of parallel sentence pairs not yet in the provided gold standard.
Using Word Familiarities and Word Associations to Measure Corpus Representativeness
As it is not practical to observe people{'}s language input over years, we suggest to utilize two types of experimental data capturing two forms of human intuitions: Word familiarity norms and word association norms.
A Graph-Based Approach for Computing Free Word Associations
We interpret these findings as evidence for the claim that human association acquisition must be based on the statistical analysis of perceived language and that when producing associations the detected statistical regularities are replicated.
Corpus-Based Computation of Reverse Associations
And during language production, these co-occurrence patterns are reproduced.
Identifying Word Translations from Comparable Documents Without a Seed Lexicon
The extraction of dictionaries from parallel text corpora is an established technique.
