Zobrazeno 1 - 10
of 71
pro vyhledávání: '"ORĂSAN, CONSTANTIN"'
This paper compares the accuracy of the terms extracted using SketchEngine, TBXTools and ChatGPT. In addition, it evaluates the quality of the definitions produced by ChatGPT for these terms. The research is carried out on a comparable corpus of fash
Externí odkaz:
http://arxiv.org/abs/2412.03242
Autor:
Saadany, Hadeel, Bhosale, Swapnil, Agrawal, Samarth, Kanojia, Diptesh, Orasan, Constantin, Wu, Zhe
This paper addresses the challenge of improving user experience on e-commerce platforms by enhancing product ranking relevant to users' search queries. Ambiguity and complexity of user queries often lead to a mismatch between the user's intent and re
Externí odkaz:
http://arxiv.org/abs/2410.15930
This paper investigates whether large language models (LLMs) are state-of-the-art quality estimators for machine translation of user-generated content (UGC) that contains emotional expressions, without the use of reference translations. To achieve th
Externí odkaz:
http://arxiv.org/abs/2410.06338
Machine translation (MT) of user-generated content (UGC) poses unique challenges, including handling slang, emotion, and literary devices like irony and sarcasm. Evaluating the quality of these translations is challenging as current metrics do not fo
Externí odkaz:
http://arxiv.org/abs/2410.03277
Autor:
Qian, Shenbin, Sindhujan, Archchana, Kabra, Minnie, Kanojia, Diptesh, Orăsan, Constantin, Ranasinghe, Tharindu, Blain, Frédéric
Leveraging large language models (LLMs) for various natural language processing tasks has led to superlative claims about their performance. For the evaluation of machine translation (MT), existing research shows that LLMs are able to achieve results
Externí odkaz:
http://arxiv.org/abs/2410.03278
With the advent of Neural Machine Translation (NMT) systems, the MT output has reached unprecedented accuracy levels which resulted in the ubiquity of MT tools on almost all online platforms with multilingual content. However, NMT systems, like other
Externí odkaz:
http://arxiv.org/abs/2405.11668
Autor:
Delfani, Jaleh, Orasan, Constantin, Saadany, Hadeel, Temizoz, Ozlem, Taylor-Stilgoe, Eleanor, Kanojia, Diptesh, Braun, Sabine, Schouten, Barbara
This study explores the use of Google Translate (GT) for translating mental healthcare (MHealth) information and evaluates its accuracy, comprehensibility, and implications for multilingual healthcare communication through analysing GT output in the
Externí odkaz:
http://arxiv.org/abs/2402.04023
Quality Estimation (QE) systems are important in situations where it is necessary to assess the quality of translations, but there is no reference available. This paper describes the approach adopted by the SurreyAI team for addressing the Sentence-L
Externí odkaz:
http://arxiv.org/abs/2312.00525
In this paper, we focus on how current Machine Translation (MT) tools perform on the translation of emotion-loaded texts by evaluating outputs from Google Translate according to a framework proposed in this paper. We propose this evaluation framework
Externí odkaz:
http://arxiv.org/abs/2306.11900
Transcription of legal proceedings is very important to enable access to justice. However, speech transcription is an expensive and slow process. In this paper we describe part of a combined research and industrial project for building an automated t
Externí odkaz:
http://arxiv.org/abs/2211.17094