Diego Bartolome


Language I/O: Our Solution for Multilingual Customer Support
Diego Bartolome | Silke Dodel | Chris Jacob
Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)

In this presentation, we will highlight the key technological innovations provided by Language I/O, see below. Dynamic MT engine selection based on the customer, content type, language pair, the content itself, as well as other metadata. Our proprietary MT quality estimation mechanism that allows customers to control their human review budget. The Self-Improving Glossary technology to continuously learn new keywords and key phrases based on the actual content processed in the platform.

Language I/O Solution for Multilingual Customer Support
Diego Bartolome | Chris Jacob
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

We describe the multilingual customer solution by Language I/O in this paper. With data security and confidentiality ensured by the ISO 27001 certification, global corporations can turn monolingual customer support agents into efficient multilingual brand ambassadors in less than 24 hours. Our solution supports more than 100 languages.


QE Viewer: an Open-Source Tool for Visualization of Machine Translation Quality Estimation Results
Felipe Soares | Anna Zaretskaya | Diego Bartolome
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

QE Viewer is a web-based tool for visualizing results of a Machine Translation Quality Estimation (QE) system. It allows users to see information on the predicted post-editing distance (PED) for a given file or sentence, and highlighted words that were predicted to contain MT errors. The tool can be used in a variety of academic, educational and commercial scenarios.

ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
Felipe Soares | Mark Stevenson | Diego Bartolome | Anna Zaretskaya
Proceedings of the Twelfth Language Resources and Evaluation Conference

The Google Patents is one of the main important sources of patents information. A striking characteristic is that many of its abstracts are presented in more than one language, thus making it a potential source of parallel corpora. This article presents the development of a parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned. We demonstrate the capabilities of our corpus by training Neural Machine Translation (NMT) models for the main 9 language pairs, with a total of 18 models. Our parallel corpus is freely available in TSV format and with a SQLite database, with complementary information regarding patent metadata.