Translation Models

The purpose of this work is to investigate the use of machine learning approaches for confi- dence estimation within a statistical machine translation application. Specifically, we at- tempt to learn …...

Statistical MT has made great progress in the last few years, but current translation models are weak on re-ordering and target language fluency. Syn- tactic approaches seek to remedy these …...

Lexical gaps between queries and questions (documents) have been a major issue in ques- tion retrieval on large online question and answer (Q&A) collections. Previous stud- ies address the issue …...

We describe our experiments with phrase-based machine translation for the WMT 2013 Shared Task. We trained one system for 18 translation directions between English or Czech on one side and …...

AbstractRecent years have seen increased interest in adapting translation models to test domains that are known in advance as well as using latent topic representations to adapt to unknown test …...

AbstractResources of manual word alignments contain configurations that are beyond the alignment capacity of current translation models, hence the term complex alignment configuration. They have been the matter of some …...

Discussion forums have evolved into a de- pendable source of knowledge to solve common problems. However, only a mi- nority of the posts in discussion forums are solution posts. Identifying …...

AbstractThis paper presents a high-quality multilingual dataset for the documentation domain to advance research on localization of structured text. Unlike widely-used datasets for translation of plain text, we collect XML-structured …...

AbstractPredicting the quality of machine translation has traditionally been addressed with language-specific models, under the assumption that the quality label distribution or linguistic features exhibit traits that are not shared …...

AbstractThis paper describes the development of a new benchmark for machine translation that provides training and test data for thousands of language pairs covering over 500 languages and tools for …...

AbstractTraditional translation systems trained on written documents perform well for text-based translation but not as well for speech-based applications. We aim to adapt translation models to speech by introducing actual …...

AbstractRecent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many …...

AbstractThis paper addressed the problem of structured sentiment analysis using a bi-affine semantic dependency parser, large pre-trained language models, and publicly available translation models. For the monolingual setup, we considered: …...

AbstractNon-autoregressive approaches aim to improve the inference speed of translation models by only requiring a single forward pass to generate the output sequence instead of iteratively producing each predicted token. …...

AbstractData augmentation is an effective way to enhance the performance of neural machine translation models, especially for low-resource languages. Existing data augmentation methods are either at a token level or …...