Chinese–Spanish neural machine translation enhanced with character and word bitmap fonts

Recently, machine translation systems based on neural networks have reached state-of-the-art results for some pairs of languages (e.g., German–English). In this paper, we are investigating the performance of neural machine translation in Chinese–Spanish, which is a challenging language pair. Given that the meaning of a Chinese word can be related to its graphical representation, this work aims to enhance neural machine translation by using as input a combination of: words or characters and their corresponding bitmap fonts. The fact of performing the interpretation of every word or character as a bitmap font generates more informed vectorial representations. Best results are obtained when using words plus their bitmap fonts obtaining an improvement (over a competitive neural MT baseline system) of almost six BLEU, five METEOR points and ranked coherently better in the human evaluation.Peer ReviewedPostprint (published version

Findings of the WMT'22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages

We present the results of the WMT'22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages. The shared task included both a data and a systems track, along with additional innovations, such as a focus on African languages and extensive human evaluation of submitted systems. We received 14 system submissions from 8 teams, as well as 6 data track contributions. We report a large progress in the quality of translation for African languages since the last iteration of this shared task: there is an increase of about 7.5 BLEU points across 72 language pairs, and the average BLEU scores went from 15.09 to 22.60

Hybrid Machine Translation Oriented to Cross-Language Information Retrieval: English-Spanish Error Analysis

ABSTRACT: The main objective of this study focuses on analysing the automatic translation of questions (intended as query inputs to a Cross-Language Information Retrieval System) and on the creation of a taxonomy of translation errors present in hybrid machine translation (HMT) systems. An analysis of translations by HMT systems was carried out. From these, there is a proposal of a type 1, 2 or 3 error taxonomy weighted according to their level of importance. Results indicate that post-editing is an essential task in the automatic translation process