200,365 research outputs found
Rethinking Global-Regulation: world’s law meets artificial intelligence
This article takes a critical look at Machine Translation of legal text, especially global legislation, through the discussion of Global-Regulation, a state of the art online search engine of the world’s legislation in English. Part 2 explains the rationale for an online platform such as Global-Regulation. Part 3 provides a brief account of the history of the development of machine translation, and it describes some of the limits of the use of statistical machine translation for translating legal texts. Part 4 describes Neural Machine Translation (NMT), which is a new generation of machine translation systems. Finally, Parts 5 and 6 outline the ‘big sky’ thoughts on future directions for Global-Regulation
New Trends in Machine Translation using Large Language Models: Case Examples with ChatGPT
Machine Translation (MT) has made significant progress in recent years using
deep learning, especially after the emergence of large language models (LLMs)
such as GPT-3 and ChatGPT. This brings new challenges and opportunities for MT
using LLMs. In this paper, we brainstorm some interesting directions for MT
using LLMs, including stylized MT, interactive MT, and Translation Memory-based
MT, as well as a new evaluation paradigm using LLMs. We also discuss the
privacy concerns in MT using LLMs and a basic privacy-preserving method to
mitigate such risks. To illustrate the potential of our proposed directions, we
present several examples for the new directions mentioned above, demonstrating
the feasibility of the proposed directions and highlight the opportunities and
challenges for future research in MT using LLMs
Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019
We reassess the claims of human parity and super-human performance made at
the news shared task of WMT 2019 for three translation directions:
English-to-German, English-to-Russian and German-to-English. First we identify
three potential issues in the human evaluation of that shared task: (i) the
limited amount of intersentential context available, (ii) the limited
translation proficiency of the evaluators and (iii) the use of a reference
translation. We then conduct a modified evaluation taking these issues into
account. Our results indicate that all the claims of human parity and
super-human performance made at WMT 2019 should be refuted, except the claim of
human parity for English-to-German. Based on our findings, we put forward a set
of recommendations and open questions for future assessments of human parity in
machine translation.Comment: Accepted at the 22nd Annual Conference of the European Association
for Machine Translation (EAMT 2020
Perspektywy rozwoju tłumaczenia maszynowego (na przykładzie angielsko-rosyjskich relacji przekładowych)
Machine translation (MT) is a relatively new field of science. MT systems are evolving in certain directions. The article discusses the possibilities and the future of systems currently offered to public by the biggest technological companies focusing on English-Russian translation relations
Low Resource Neural Machine Translation: A Benchmark for Five African Languages
Recent advents in Neural Machine Translation (NMT) have shown improvements in
low-resource language (LRL) translation tasks. In this work, we benchmark NMT
between English and five African LRL pairs (Swahili, Amharic, Tigrigna, Oromo,
Somali [SATOS]). We collected the available resources on the SATOS languages to
evaluate the current state of NMT for LRLs. Our evaluation, comparing a
baseline single language pair NMT model against semi-supervised learning,
transfer learning, and multilingual modeling, shows significant performance
improvements both in the En-LRL and LRL-En directions. In terms of averaged
BLEU score, the multilingual approach shows the largest gains, up to +5 points,
in six out of ten translation directions. To demonstrate the generalization
capability of each model, we also report results on multi-domain test sets. We
release the standardized experimental data and the test sets for future works
addressing the challenges of NMT in under-resourced settings, in particular for
the SATOS languages.Comment: Accepted for AfricaNLP workshop at ICLR 202
Towards a Better Understanding of Variations in Zero-Shot Neural Machine Translation Performance
Multilingual Neural Machine Translation (MNMT) facilitates knowledge sharing
but often suffers from poor zero-shot (ZS) translation qualities. While prior
work has explored the causes of overall low ZS performance, our work introduces
a fresh perspective: the presence of high variations in ZS performance. This
suggests that MNMT does not uniformly exhibit poor ZS capability; instead,
certain translation directions yield reasonable results. Through systematic
experimentation involving 1,560 language directions spanning 40 languages, we
identify three key factors contributing to high variations in ZS NMT
performance: 1) target side translation capability 2) vocabulary overlap 3)
linguistic properties. Our findings highlight that the target side translation
quality is the most influential factor, with vocabulary overlap consistently
impacting ZS performance. Additionally, linguistic properties, such as language
family and writing system, play a role, particularly with smaller models.
Furthermore, we suggest that the off-target issue is a symptom of inadequate ZS
performance, emphasizing that zero-shot translation challenges extend beyond
addressing the off-target problem. We release the data and models serving as a
benchmark to study zero-shot for future research at
https://github.com/Smu-Tan/ZS-NMT-VariationsComment: This paper is accepted by the EMNLP 2023 Main Conferenc
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