학회 정보

NeTTT 2024: International Conference New Trends in Translation and Technology

학회 웹사이트를 보려면 로그인해 주세요
무료 가입으로 공식 사이트 조회, 마감 추적, 이메일 리마인더를 이용할 수 있습니다.
회원가입 로그인
마감 카운트다운 배지 삽입
NeTTT
이 데이터를 API로 가져오기
검색과 순위 목록은 자격 증명이 전혀 필요 없습니다. 이 페이지의 상세 데이터에는 무료 API 키가 필요합니다. 자세한 내용은 개발자 안내 페이지를 참고하세요.
투고 마감일:
2024-03-31
통보일:
2024-06-05
개최일:
2024-07-04
개최지:
Varna, Bulgaria
조회: 8339   팔로우: 0   참가: 0

회반 지수 (CP-I)

42.1 / 100
전체 5,680개 중 4,126위 · 상위 73%
근거가 제한적입니다: 이 학회는 CCF / ICORE / QUALIS 어디에도 수록되어 있지 않고 게재율 데이터도 없어, 점수의 대부분이 중립 기준값으로 되돌아갑니다.
학술적 인정 (35%) 데이터 없음 — 중립 기준값 50점으로 계산 —
투고 선별성 (20%) 데이터 없음 — 중립 기준값 50점으로 계산 —
개최 횟수 (20%) 데이터 없음 — 중립 기준값 50점으로 계산 —
커뮤니티 관심도 (10%)
8
공개 자료 충실도 (15%)
25

사용한 입력: 지난 24개월 동안 이 페이지를 연 연구자: 2명

공개 자료에서 빠진 항목: 역대 게재율 (+4.5) · 역대 회차 (+3.0) · 최우수 논문 기록 (+2.3)
주최자는 학회를 인증 신청한 뒤 이 페이지에서 바로 추가할 수 있습니다. 점수는 매일 밤 다시 계산됩니다. 이 점수를 올리는 방법

신뢰도 25% — 점수 중 중립 기준값이 아니라 실제 관측된 데이터에 근거한 비율. 이 점수는 어떻게 계산되나 · 전체 순위 보기 · 알고리즘 버전 1.1 · 산출일 2026-09-12

논문 모집

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.
최종 수정: Dou Sun ()

관련 학회

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
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

댓글 0

아직 댓글이 없습니다.

댓글을 작성하려면 로그인해 주세요