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Machine Translation Quality Estimation Across Domains
Machine Translation (MT) Quality Estimation (QE) aims to automatically measure the quality of MT system output without reference translations. In spite of the progress achieved in re- cent years, current …...
TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling
We present a system for taxonomy construc- tion that reached the first place in all sub- tasks of the SemEval 2016 challenge on Tax- onomy Extraction Evaluation. Our simple yet …...
The Reference Corpus of the Contemporary Romanian Language (CoRoLa)
We present here the largest publicly available corpus of Romanian. Its written component contains 1,257,752,812 tokens, distributed, in an unbalanced way, in several language styles (legal, administrative, scientific, journalistic, imaginative, …...
Dynamically Composing Domain-Data Selection with Clean-Data Selection by “Co-Curricular Learning” for Neural Machine Translation
AbstractNoise and domain are important aspects of data quality for neural machine translation. Existing research focus separately on domain-data selection, clean-data selection, or their static combination, leaving the dynamic interaction …...
SParC: Cross-Domain Semantic Parsing in Context
AbstractWe present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries). It is obtained from controlled user interactions with …...
Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation
AbstractNeural machine translation (NMT) models usually suffer from catastrophic forgetting during continual training where the models tend to gradually forget previously learned knowledge and swing to fit the newly added …...