首页|New Data from Nanjing University Illuminate Research in Machine Translation (Mac hine Translation Testing via Syntactic Tree Pruning)
New Data from Nanjing University Illuminate Research in Machine Translation (Mac hine Translation Testing via Syntactic Tree Pruning)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on machine translati on are discussed in a new report. According to news reporting from Nanjing, Peop le's Republic of China, by NewsRx journalists, research stated, "Machine transla tion systems have been widely adopted in our daily life, making life easier and more convenient. Unfortunately, erroneous translations may result in severe cons equences, such as financial losses." Our news reporters obtained a quote from the research from Nanjing University: " This requires to improve the accuracy and the reliability of machine translation systems. However, it is challenging to test machine translation systems because of the complexity and intractability of the underlying neural models. To tackle these challenges, we propose a novel metamorphic testing approach by syntactic tree pruning (STP) to validate machine translation systems. Our key insight is t hat a pruned sentence should have similar crucial semantics compared with the or iginal sentence. Specifically, STP (1) proposes a core semantics-preserving prun ing strategy by basic sentence structures and dependency relations on the level of syntactic tree representation, (2) generates source sentence pairs based on t he metamorphic relation, and (3) reports suspicious issues whose translations br eak the consistency property by a bag-of-words model. We further evaluate STP on two state-of-the-art machine translation systems (i.e., Google Translate and Bi ng Microsoft Translator) with 1,200 source sentences as inputs. The results show that STP accurately finds 5,073 unique erroneous translations in Google Transla te and 5,100 unique erroneous translations in Bing Microsoft Translator (400% more than state-of-the-art techniques), with 64.5% and 65.4% precision, respectively."
Nanjing UniversityNanjingPeople's Re public of ChinaAsiaEmerging TechnologiesMachine LearningMachine Translat ion
NETL
NSTL
