Special Libraries, January 1962

Volume 53, Issue 1https://scholarworks.sjsu.edu/sla_sl_1962/1000/thumbnail.jp

Character-level Transformer-based Neural Machine Translation

Neural machine translation (NMT) is nowadays commonly applied at the subword level, using byte-pair encoding. A promising alternative approach focuses on character-level translation, which simplifies processing pipelines in NMT considerably. This approach, however, must consider relatively longer sequences, rendering the training process prohibitively expensive. In this paper, we discuss a novel, Transformer-based approach, that we compare, both in speed and in quality to the Transformer at subword and character levels, as well as previously developed character-level models. We evaluate our models on 4 language pairs from WMT'15: DE-EN, CS-EN, FI-EN and RU-EN. The proposed novel architecture can be trained on a single GPU and is 34% percent faster than the character-level Transformer; still, the obtained results are at least on par with it. In addition, our proposed model outperforms the subword-level model in FI-EN and shows close results in CS-EN. To stimulate further research in this area and close the gap with subword-level NMT, we make all our code and models publicly available

Automatic alignment of hieroglyphs and transliteration

Automatic alignment has important applications in philology, facilitating study of texts on the basis of electronic resources produced by different scholars. A simple technique is presented to realise such alignment for Ancient Egyptian hieroglyphic texts and transliteration. Preliminary experiments with the technique are reported, and plans for future work are discussed.Postprin