Prompsit Language Engineering is launching an updated API and CLI for its open-source, planet-friendly machine translation services. Operating on a freemium model, the tools offer free limited access alongside tiered pricing for advanced features like MT evaluation, quality estimation, corpus scoring, and multilingual dataset annotation.

Language identification is a crucial component in the automated production of language resources, particularly in multilingual and big data contexts. However, commonly used language identifiers struggle to differentiate between similar or closely-related languages. This paper introduces FastSpell, a language identifier that combines fastText (a pre-trained language identifier tool) and Hunspell (a spell checker) with the aim of having a refined second-opinion before deciding which language should be assigned to a text. We provide a description of the FastSpell algorithm along with an explanation on how to use and configure it. To that end, we motivate the need of such a tool and present a benchmark including some popular language identifiers evaluated during the development of FastSpell. We show how FastSpell is useful not only to improve identification of similar languages, but also to identify new ones ignored by other tools.

SmartBiC, an 18-month innovation project funded by the Spanish Government, aims at improving the full process of collecting, filtering and selecting in-domain parallel content to be used for machine translation and language model tuning purposes in industrial settings. Based on state-of-the-art technology in the free/open-source parallel web corpora harvester Bitextor, SmartBic develops a web-based application around it including novel components such as a language- and domain-focused crawler and a domain-specific corpora selector. SmartBic also addresses specific industrial use cases for individual components of the Bitextor pipeline, such as parallel data cleaning. Relevant improvements to the current Bitextor pipeline will be publicly released.

This paper describes the experiments carried out during the development of the latest version of Bicleaner, named Bicleaner AI, a tool that aims at detecting noisy sentences in parallel corpora. The tool, which now implements a new neural classifier, uses state-of-the-art techniques based on pre-trained transformer-based language models fine-tuned on a binary classification task. After that, parallel corpus filtering is performed, discarding the sentences that have lower probability of being mutual translations. Our experiments, based on the training of neural machine translation (NMT) with corpora filtered using Bicleaner AI for two different scenarios, show significant improvements in translation quality compared to the previous version of the tool which implemented a classifier based on Extremely Randomized Trees.

Marta Bañón, Pinzhen Chen, Barry Haddow, Kenneth Heafield, Hieu Hoang, Miquel Esplà-Gomis, Mikel L. Forcada, Amir Kamran, Faheem Kirefu, Philipp Koehn, Sergio Ortiz Rojas, Leopoldo Pla Sempere, Gema Ramírez-Sánchez, Elsa Sarrías, Marek Strelec, Brian Thompson, William Waites, Dion Wiggins, Jaume Zaragoza. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.

This paper shows the utility of two open-source tools designed for parallel data cleaning: Bifixer and Bicleaner. Already used to clean highly noisy parallel content from crawled multilingual websites, we evaluate their performance in a different scenario: cleaning publicly available corpora commonly used to train machine translation systems. We choose four English–Portuguese corpora which we plan to use internally to compute paraphrases at a later stage. We clean the four corpora using both tools, which are described in detail, and analyse the effect of some of the cleaning steps on them. We then compare machine translation training times and quality before and after cleaning these corpora, showing a positive impact particularly for the noisiest ones.

This paper describes interNOSTRUM, a Spanish3Catalan machine translation system currently under development that achieves great speed through the use of finite-state technologies (so that it may be integrated with internet browsing) and a reasonable accuracy using an advanced morphological transfer strategy (to produce fast translation drafts ready for light postedition).