# Artificial intelligence contribution to translation industry: looking back and forward

arXiv: https://arxiv.org/abs/2411.19855 · HTML: https://mlsanity.com/paper/2411.19855/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 89 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [Colorless green recurrent networks dream hierarchically](https://mlsanity.com/paper/1803.11138/) (2018)
- [Is ChatGPT A Good Translator? Yes With GPT-4 As The Engine](https://mlsanity.com/paper/2301.08745/) (2023)
- [End-to-End Attention-based Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1508.04395/) (2015)
- [Syntactic Structure from Deep Learning](https://mlsanity.com/paper/2004.10827/) (2020)
- [A Survey of Deep Learning Techniques for Neural Machine Translation](https://mlsanity.com/paper/2002.07526/) (2020)
- [Semantic Neural Machine Translation using AMR](https://mlsanity.com/paper/1902.07282/) (2019)
- [On the Linguistic Representational Power of Neural Machine Translation Models](https://mlsanity.com/paper/1911.00317/) (2019)
- [Machine Translation from Signed to Spoken Languages: State of the Art and Challenges](https://mlsanity.com/paper/2202.03086/) (2022)
- [Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions](https://mlsanity.com/paper/2307.14107/) (2023)
- [Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models](https://mlsanity.com/paper/2401.08350/) (2024)
- [Neural machine translation for low-resource languages](https://mlsanity.com/paper/1708.05729/) (2017)
- [Generative linguistics contribution to artificial intelligence: Where this contribution lies?](https://mlsanity.com/paper/2410.20221/) (2024)
- [A Review of Theory and Practice in Scientometrics](https://mlsanity.com/paper/1501.05462/) (2015)
- [Visualizing a Field of Research: A Methodology of Systematic Scientometric Reviews](https://mlsanity.com/paper/1906.04800/) (2019)
- [Improvement in Machine Translation with Generative Adversarial Networks](https://mlsanity.com/paper/2111.15166/) (2021)

## Built on by (most cited first, 2 of 2)

- [Current LLMs still cannot 'talk much' about grammar modules: Evidence from syntax](https://mlsanity.com/paper/2603.20114/) (2026)
- [The impact of postediting on AI generative translation in Yemeni context: Translating literary prose by ChatGPT](https://mlsanity.com/paper/2604.16704/) (2026)

## Abstract (arXiv)

This study provides a comprehensive analysis of artificial intelligence (AI) contribution to research in the translation industry (ACTI), synthesizing it over forty-five years from 1980-2024. 13220 articles were retrieved from three sources, namely WoS, Scopus, and Lens; 9836 were unique records, which were used for the analysis. We provided two types of analysis, viz., scientometric and thematic, focusing on Cluster, Subject categories, Keywords, Bursts, Centrality and Research Centers as for the former. For the latter, we provided a thematic review for 18 articles, selected purposefully from the articles involved, centering on purpose, approach, findings, and contribution to ACTI future directions. This study is significant for its valuable contribution to ACTI knowledge production over 45 years, emphasizing several trending issues and hotspots including Machine translation, Statistical machine translation, Low-resource language, Large language model, Arabic dialects, Translation quality, and Neural machine translation. The findings reveal that the more AI develops, the more it contributes to translation industry, as Neural Networking Algorithms have been incorporated and Deep Language Learning Models like ChatGPT have been launched. However, much rigorous research is still needed to overcome several problems encountering translation industry, specifically concerning low-resource, multi-dialectical and free word order languages, and cultural and religious registers.