TY - GEN
T1 - Italian-Chinese Neural Machine Translation
T2 - 3rd ACM Conference on Information Technology for Social Good, GoodIT 2023
AU - Delnevo, Giovanni
AU - Im, Marcus
AU - Tse, Rita
AU - Lam, Chan Tong
AU - Tang, Su Kit
AU - Salomoni, Paola
AU - Pau, Giovanni
AU - Ghini, Vittorio
AU - Mirri, Silvia
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/9/6
Y1 - 2023/9/6
N2 - Today, access to the Internet provides access to various forms of knowledge like free online lecture series offered by prestigious universities, massive open online courses, films and books, and Wikipedia. In addition, it is possible to join online communities on any topic of interest, get to know people with common interests, exchange thoughts and participate in debates. To enable access to these unprecedented knowledge bases, it is crucial to be able to translate texts into any language known by users. For this reason, Machine Translation has been a very active research field for the last thirty years. In this paper, we investigate the task of Chinese-Italian translations by exploiting Neural Machine Translation approaches. We trained several deep neural networks starting from two already available datasets containing Chinese-Italian parallel corpora. Then, we compared their performance against some of the most common machine translation services freely available online. In particular, we take advantage of Microsoft Translator, Google Translate, DeepL, and ModernMT.
AB - Today, access to the Internet provides access to various forms of knowledge like free online lecture series offered by prestigious universities, massive open online courses, films and books, and Wikipedia. In addition, it is possible to join online communities on any topic of interest, get to know people with common interests, exchange thoughts and participate in debates. To enable access to these unprecedented knowledge bases, it is crucial to be able to translate texts into any language known by users. For this reason, Machine Translation has been a very active research field for the last thirty years. In this paper, we investigate the task of Chinese-Italian translations by exploiting Neural Machine Translation approaches. We trained several deep neural networks starting from two already available datasets containing Chinese-Italian parallel corpora. Then, we compared their performance against some of the most common machine translation services freely available online. In particular, we take advantage of Microsoft Translator, Google Translate, DeepL, and ModernMT.
KW - Big Data
KW - Italian Chinese Translation
KW - Neural Machine Translation
KW - Transformer
UR - http://www.scopus.com/inward/record.url?scp=85174288403&partnerID=8YFLogxK
U2 - 10.1145/3582515.3609567
DO - 10.1145/3582515.3609567
M3 - Conference contribution
AN - SCOPUS:85174288403
T3 - ACM International Conference Proceeding Series
SP - 455
EP - 461
BT - GoodIT 2023 - Proceedings of the 2023 ACM Conference on Information Technology for Social Good
PB - Association for Computing Machinery
Y2 - 6 September 2023 through 8 September 2023
ER -