958 research outputs found
Third-Party Aligner for Neural Word Alignments
Word alignment is to find translationally equivalent words between source and
target sentences. Previous work has demonstrated that self-training can achieve
competitive word alignment results. In this paper, we propose to use word
alignments generated by a third-party word aligner to supervise the neural word
alignment training. Specifically, source word and target word of each word pair
aligned by the third-party aligner are trained to be close neighbors to each
other in the contextualized embedding space when fine-tuning a pre-trained
cross-lingual language model. Experiments on the benchmarks of various language
pairs show that our approach can surprisingly do self-correction over the
third-party supervision by finding more accurate word alignments and deleting
wrong word alignments, leading to better performance than various third-party
word aligners, including the currently best one. When we integrate all
supervisions from various third-party aligners, we achieve state-of-the-art
word alignment performances, with averagely more than two points lower
alignment error rates than the best third-party aligner. We released our code
at https://github.com/sdongchuanqi/Third-Party-Supervised-Aligner.Comment: 12 pages, 4 figures, findings of emnlp 202
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
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
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