This paper presents a statistical machine translation trained on normalized corpora. The automatic paraphrasing is carried out by inducing paraphrasing expressions from a bilingual corpus. Then, the normalization is treated as a specific paraphrase of a given input determined by the frequency in a corpus. The experimental results on Japanese-to-English translation with normalized English corpus...

B is the de facto standard for evaluation and development of statistical machine translation systems. We describe three real-world situations involving comparisons between different versions of the same systems where one can obtain improvements in B scores that are questionable or even absurd. These situations arise because B lacks the property of decomposability, a property which is a...

This paper introduces a first outline of a typology of distributed knowledge co-creation in virtual communities based on Porter‘s typology of virtual communities. The typology is based on empirical results from the analyses of social media, and a discussion of case study results from India proves the adaptability as well as usefulness of the typology. At the same time, the case study serves as ...

Terms extensively exist in specific domains, and term translation plays a critical role in domain-specific statistical machine translation (SMT) tasks. However, it’s a challenging task to extract term translation knowledge from parallel sentences because of the error propagation in the SMT training pipeline. In this paper, we propose a simple, straightforward and effective model to mitigate the...

Automatic error detection is desired in the post-processing to improve machine translation quality. The previous work is largely based on confidence estimation using system-based features, such as word posterior probabilities calculated from N best lists or word lattices. We propose to incorporate two groups of linguistic features, which convey information from outside machine translation syste...

We propose an approach to N -best list reranking using neural sequence-labelling models. We train a compositional model for error detection that calculates the probability of each token in a sentence being correct or incorrect, utilising the full sentence as context. Using the error detection model, we then re-rank the N best hypotheses generated by statistical machine translation systems. Our ...

Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain? We address these questions with a challenge set approach to translation evaluation and error analysis. A challenge set consists of a small set of sentences, each hand-designed to probe a system’s capacity to bridge a particular structural div...

This paper presents a comparative study of two alternative approaches to statistical machine translation (SMT) and their application to a task of English-to-Latvian translation. Furthermore, a novel feature intending to reflect the relatively free word order scheme of the Latvian language is proposed and successfully applied on the n-best list rescoring step. Moving beyond classical automatic s...