Abstract: A large number of machine translation approaches have recently been developed to facilitate the fluid migration of content across languages. However, the literature suggests that many obstacles must still be dealt with to achieve better automatic translations. One of these obstacles is lexical and syntactic ambiguity. A promising way of overcoming this problem is using Semantic Web technologies. This article is an extended abstract of our systematic review on machine translation approaches that rely on Semantic Web technologies for improving the translation of texts. Overall, we present the challenges and opportunities in the use of Semantic Web technologies in Machine Translation. Moreover, our research suggests that while Semantic Web technologies can enhance the quality of machine translation outputs for various problems, the combination of both is still in its infancy.

Introduction: Alongside increasing globalization comes a greater need for readers to understand texts in languages foreign to them. For example, approximately 48% of the pages on the Web are not available in English. The technological progress of recent decades has made both the distribution and access to content in different languages ever simpler. Translation aims to support users who need to access content in a language in which they are not fluent BIBREF0 . However, translation is a difficult task due to the complexity of natural languages and their structure BIBREF0 . In addition, manual translation does not scale to the magnitude of the Web. One remedy for this problem is MT. The main goal of MT is to enable people to assess content in languages other than the languages in which they are fluent BIBREF1 . From a formal point of view, this means that the goal of MT is to transfer the semantics of text from an input language to an output language BIBREF2 . Although MT systems are now popular on the Web, they still generate a large number of incorrect translations. Recently, Popović BIBREF3 has classified five types of errors that still remain in MT systems. According to research, the two main faults that are responsible for 40% and 30% of problems respectively, are reordering errors and lexical and syntactic ambiguity. Thus, addressing these barriers is a key challenge for modern translation systems. A large number of MT approaches have been developed over the years that could potentially serve as a remedy. For instance, translators began by using methodologies based on linguistics which led to the family of RBMT. However, RBMT systems have a critical drawback in their reliance on manually crafted rules, thus making the development of new translation modules for different languages even more difficult. SMT and EBMT were developed to deal with the scalability issue in RBMT BIBREF4 , a necessary characteristic of MT systems that must deal with data at Web scale. Presently, these approaches have begun to address the drawbacks of rule-based approaches. However, some problems that had already been solved for linguistics based methods reappeared. The majority of these problems are connected to the issue of ambiguity, including syntactic and semantic variations BIBREF0 . Nowadays, a novel SMT paradigm has arisen called NMT which relies on NN algorithms. NMT has been achieving impressive results and is now the state-of-the-art in MT approaches. However, NMT is still a statistical approach sharing some semantic drawbacks from other well-defined SMT approaches BIBREF5 . One possible solution to address the remaining issues of MT lies in the use of SWT, which have emerged over recent decades as a paradigm to make the semantics of content explicit so that it can be used by machines. It is believed that explicit semantic knowledge made available through these technologies can empower MT systems to supply translations with significantly better quality while remaining scalable. In particular, the disambiguated knowledge about real-world entities, their properties and their relationships made available on the LD Web can potentially be used to infer the right meaning of ambiguous sentences or words. According to our survey BIBREF6 , the obvious opportunity of using SWT for MT has already been studied by a number of approaches, especially w.r.t. the issue of ambiguity. In this paper, we present the challenges and opportunities in the use of SWT in MT for translating texts.

Question: What are the challenges associated with the use of Semantic Web technologies in Machine Translation?
Answer: SWT are hard to implement