The Danger of Foreignization: Ling Shuhua’s Ancient Melodies

Lawrence Venuti’s foreignization theory, with its link of translation strategy with power struggle, is one of the most influential theories in translation studies since the 1990s. At the same time, his theory has also been subject to heated debate due to its loosely defined terms, prescriptive approach, binary thinking, elitist tendency, and other issues. One issue stands out in particular: contrary to its goal of resistance against Anglo- American hegemony, foreignization can lead to its opposite—exoticism or Orientalism—under certain circumstances. In this paper, I examine the validity and application of Venuti’s foregnization theory in Ling Shuhua’s English autobiographical work Ancient Melodies. In Ling’s creative writing that embodies several forms of translation, foreignization is the dominant writing and translating strategy. By analysis, I argue that while Ling unwittingly breaks several binaries in translation studies, she deliberately creates the foreignizing effect with her careful maneuver of domestication. Ling’s highlighting of foreignizing strategy reveals her binary thinking, which displays deep roots in the power hierarchy of the West. In this way, it can be seen that foreignization strategy functions as a double-edged sword; in its open resistance against power, it is also deeply involved with and assists the power structure

Bridging SMT and TM with translation recommendation

We propose a translation recommendation framework to integrate Statistical Machine Translation (SMT) output with Translation Memory (TM) systems. The framework recommends SMT outputs to a TM user when it predicts that SMT outputs are more suitable for post-editing than the hits provided by the TM. We describe an implementation of this framework using an SVM binary classifier. We exploit methods to fine-tune the classifier and investigate a variety of features of different types. We rely on automatic MT evaluation metrics to approximate human judgements in our experiments. Experimental results show that our system can achieve 0.85 precision at 0.89 recall, excluding exact matches. futhermore, it is possible for the end-user to achieve a desired balance between precision and recall by adjusting confidence levels

Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs

Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a rich area focused on processing text of various natural languages. We notice that binary code analysis and NLP share a lot of analogical topics, such as semantics extraction, summarization, and classification. This work utilizes these ideas to address two important code similarity comparison problems. (I) Given a pair of basic blocks for different instruction set architectures (ISAs), determining whether their semantics is similar or not; and (II) given a piece of code of interest, determining if it is contained in another piece of assembly code for a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. And the case studies utilizing the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to large-scale binary code analysis.Comment: Accepted by Network and Distributed Systems Security (NDSS) Symposium 201