Exploration on Advanced Intelligent Algorithms of Artificial Intelligence for Verb Recognition in Machine Translation

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qinghua Ai, Qingyan Ai, Jun Wang
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引用次数: 0

Abstract

This article aimed to address the problems of word order confusion, context dependency, and ambiguity in traditional machine translation (MT) methods for verb recognition. By applying advanced intelligent algorithms of artificial intelligence, verb recognition can be better processed and the quality and accuracy of MT can be improved. Based on Neural machine translation (NMT), basic attention mechanisms, historical attention information, dynamically obtain information related to the generated words, and constraint mechanisms were introduced to embed semantic information, represent polysemy, and annotate semantic roles of verbs. This article used the Workshop on machine translation (WMT), British National Corpus (BNC), Gutenberg, Reuters Corpus, OpenSubtitles corpus, and enhanced the data in the corpus. The improved NMT model was compared with traditional NMT models, Rule Based machine translation (RBMT), and Statistical machine translation (SMT). The experimental results showed that the average verb semantic matching degree of the improved NMT model in 5 corpora was 0.85, and the average Bilingual Evaluation Understudy (BLEU) score in 5 corpora was 0.90. The improved NMT model in this article can effectively improve the accuracy of verb recognition in MT, providing new methods for verb recognition in MT.

机器翻译中动词识别的人工智能高级算法探讨
本文旨在解决传统机器翻译(MT)方法在动词识别中存在的词序混乱、语境依赖和歧义等问题。通过应用人工智能的先进智能算法,可以更好地处理动词识别,提高 MT 的质量和准确性。在神经机器翻译(NMT)的基础上,引入了基本关注机制、历史关注信息、动态获取生成词相关信息和约束机制,以嵌入语义信息、表示多义词和注释动词的语义角色。本文使用了机器翻译研讨会(WMT)、英国国家语料库(BNC)、古腾堡语料库、路透社语料库、OpenSubtitles 语料库,并对语料库中的数据进行了增强。改进后的 NMT 模型与传统 NMT 模型、基于规则的机器翻译(RBMT)和统计机器翻译(SMT)进行了比较。实验结果表明,改进后的 NMT 模型在 5 个语料库中的平均动词语义匹配度为 0.85,在 5 个语料库中的平均双语评估(BLEU)得分为 0.90。本文改进的 NMT 模型能有效提高 MT 中动词识别的准确率,为 MT 中的动词识别提供了新的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.60
自引率
15.00%
发文量
241
期刊介绍: The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.
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