摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器翻译的新数据在一份新的报告中呈现。根据NewsRx编辑在《国家科技期刊》上的新闻报道，研究表明："在语言多样的印度landscap e中，多模态机器翻译(MMT)是一项具有挑战性的任务。机器翻译指的是在没有人类参与的情况下，将一种语言的内容自动转换为另一种语言的任务。"新闻记者从国家理工学院的研究中获得一句话:“在自然语言处理领域,人类语言中固有的歧义产生了一个重要的挑战。翻译歧义是一种跨语言现象,其表现形式有词汇歧义、偶然需要填补缺失词、世代歧义的存在等。”这些因素会导致翻译准确性的下降。除了文本之外，图像、视频和音频等多种模式的整合对于提高翻译系统的鲁棒性和准确性起着关键作用。在过去的五年里，大量的研究致力于将第二种模式与文本结合起来，以提高语言的翻译和理解。在这项综合研究中，我们的目标是确定和探索有前途的MT方法、可用语料库、评估指标、研究挑战以及未来专门针对印度语言的研究方向。我们评估了D 81篇论文，包括MT模型、印度语言的MT数据集、MT方法调查以及机器翻译中多种模式的影响。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on machine translation are presented in a new report. According to news reporting out of the National Insti tute of Technology by NewsRx editors, research stated, "Multimodal machine trans lation (MMT) is a challenging task in the linguistically diverse Indian landscap e. Machine translation refers to the task of automatically converting content fr om one language to another without human involvement." The news journalists obtained a quote from the research from National Institute of Technology: "Within the realm of natural language processing, a significant c hallenge arises from the inherent ambiguity present in human language. Translati on ambiguity is a cross-lingual phenomenon that can manifest itself for various reasons, including lexical ambiguity, the occasional need to impute missing word s, the presence of gen-der ambiguity, and word-sense ambiguities. These factors can lead to a decrease in translation accuracy. The integration of multiple moda lities, such as images, videos, and audio, in addition to text, plays a pivotal role in improving the robustness and precision of translation systems. Over the past five years, extensive research has been dedicated to incorporating secondar y modalities alongside text to improve language translation and comprehension. I n this comprehensive study, our objective was to identify and explore promising MMT approaches, available corpora, eval-uation metrics, research challenges, and the future direction of research specifically for Indian languages. We evaluate d 81 papers, including MMT models, MMT dataset in Indian languages, survey on MM T approach, and the effects of multiple modalities in machine translation."
NETL
NSTL