Modelling word translation entropy and syntactic equivalence with machine learning
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningTranslationWord TranslationSimilar Papers 제목 키워드 기반
Modelling and Optimizing on Syntactic N-Grams for Statistical Machine Translation
The role of language models in SMT is to promote fluent translation output, but traditional n-gram language models are unable to capture fluency phenomena between distant words, such as some morphological agreement pheno…
Language ModelingLanguage ModellingMachine TranslationTranslationModelling Source- and Target- Language Syntactic Information as Conditional Context in Interactive Neural Machine Translation
In interactive machine translation (MT), human translators correct errors in automatic translations in collaboration with the MT systems, which is seen as an effective way to improve the productivity gain in translation.…
Machine TranslationNMTTranslationA Dataset of Translational Equivalents Built on the Basis of plWordNet-Princeton WordNet Synset Mapping
The paper presents a dataset of 11,000 Polish-English translational equivalents in the form of pairs of plWordNet and Princeton WordNet lexical units linked by three types of equivalence links: strong equivalence, regula…
TranslationWord Sense DisambiguationWhich Tokens Need Context? A Reference-Based Analysis of Translation Responsibility Using Fertility and Entropy
When humans translate, not every word depends equally on the surrounding context. Some tokens, particularly function words like pronouns and auxiliaries, rely heavily on preceding or following sentences, while others, su…
Machine TranslationA Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation
It is difficult for non-autoregressive translation (NAT) models to capture the multi-modal distribution of target translations due to their conditional independence assumption, which is known as the "multi-modality probl…
Machine TranslationTranslation