会议信息

NeTTT 2024: International Conference New Trends in Translation and Technology

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截稿日期:
2024-03-31
通知日期:
2024-06-05
会议日期:
2024-07-04
会议地点:
Varna, Bulgaria
浏览: 8338   关注: 0   参加: 0

会伴指数 (CP-I)

42.1 / 100
全站第 4,126 名 / 共 5,680 个会议 · 前 73%
证据有限:这个会议不在 CCF / ICORE / QUALIS 任何一份榜单里,也没有录用率数据,因此分数的大部分回落到了中性基准。
学术认可 (35%) 无数据 —— 按中性基准 50 分计入
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入
会议传承 (20%) 无数据 —— 按中性基准 50 分计入
社区关注 (10%)
8
资料公开度 (15%)
25

用到的输入: 过去 24 个月打开过本页的研究者:2 人

公开资料里还缺: 历年录用率 (+4.5) · 历届信息 (+3.0) · 最佳论文记录 (+2.3)
主办方认领本会议后,可直接在这一页补上;分数每晚重算。如何提升这个分数

置信度 25% —— 分数中有多大比例来自实际观测到的数据,而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.1 · 算于 2026-09-12

征稿

NeTTT 2024 (International Conference New Trends in Translation and Technology) is an academic conference held in Varna, Bulgaria on 2024-07-04. The paper submission deadline is 2024-03-31. Acceptance notifications are sent on 2024-06-05.

Contributions are invited on any topic related to latest technology and practices in machine translation, translation, subtitling, localisation and interpreting. NeTTT’2024 will feature a Special Theme Track “Future of Translation Technology in the Era of LLMs and Generative AI”. The conference topics include but are not limited to: CAT tools Translation Memory (TM) systems NLP and MT for translation memory systems Terminology extraction tools Localisation tools Machine Translation Latest developments in Neural Machine Translation MT for under-resourced languages MT with low computing resources Multimodal MT Integration of MT in TM systems Resources for MT Technologies for MT deployment MT evaluation techniques, metrics and evaluation results Human evaluations of MT output Evaluating MT in a real-world setting Quality estimation for MT Domain adaptation Translation Studies Corpus-based studies applied to translation Corpora and resources for translation Translationese Cognitive effort and eye-tracking experiments in translation Interpreting studies Corpus-based studies applied to interpreting Corpora and resources for interpreting Interpretationese Resources for interpreting and interpreting technology applications Cognitive effort and eye-tracking experiments in interpreting Interpreting technology Machine interpreting Computer-aided interpreting NLP for dialogue interpreting Development of NLP based applications for communication in public service settings (healthcare, education, law, emergency services) Emerging Areas in Translation and Interpreting MT and translation tools for literary texts and creative texts MT for social media and real-time conversations Sign language recognition and translation Subtitling NLP and MT for subtitling Latest technology for subtitling User needs Analysis of translators’ and interpreters’ needs in terms of translation and interpreting technology User requirements for interpreting and translation tools Incorporating human knowledge into translation and interpreting technology What existing translators’ (including subtitlers’) and interpreters’ tools do not offer User requirements for electronic resources for translators and interpreters Translation and interpreting workflows in larger organisations and the tools for translation and interpreting employed The business of translation and interpreting Translation workflow and management Technology adoption by translators and industry Setting up translation /interpreting / language provider company Teaching translation and interpreting Teaching Machine Translation Teaching translation technology Teaching interpreting technology Latest AI developments in the syllabi of translation and interpreting curricula Ethical issues in translation and technology Bias and fairness in MT Privacy and security in cloud MT systems Transparency and explainability of MT systems Environmental impact on MT systems Special Theme Track – Future of Translation Technology in the Era of LLMs and Generative AI We are excited to share that NeTTT’2024 will have a special theme with the goal of stimulating discussion around Large Language Models, Generative AI and the Future of Translation and Interpreting Technology. While the new generation of Large Language Models such as CHATGPT and LLAMA showcase remarkable advancements in language generation and understanding, we find ourselves in uncharted territory when it comes to their performance on various Translation and Interpreting Technology tasks with regards to fairness, interpretability, ethics and transparency. The theme track invites studies on how LLMs perform on Translation and Interpreting Technology tasks and applications, and what this means for the future of the field. The possible topics of discussion include (but are not limited to) the following: Changes in the translators and interpreters’ professions in the new AI era especially as a result of the latest developments in LLMs and Generative AI Generative AI and translation Generative AI and interpreting Augmenting machine translation systems with generative AI Domain and terminology adaptation with Large Language Models Literary translation with Large Language Models Improving Machine Translation Quality with Contextual Prompts in Large Language Models Prompt engineering for translation Generative AI for professional translation Generative AI for professional interpreting We anticipate having a special session on this theme at the conference.
Dou Sun 最后更新于

相关期刊

CCF全称影响因子出版商ISSN
CMachine TranslationSpringer0922-6567
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536

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