Improved Arabic–Chinese Machine Translation with Linguistic Input Features
نویسندگان
چکیده
منابع مشابه
Linguistic Input Features Improve Neural Machine Translation
Neural machine translation has recently achieved impressive results, while using little in the way of external linguistic information. In this paper we show that the strong learning capability of neural MT models does not make linguistic features redundant; they can be easily incorporated to provide further improvements in performance. We generalize the embedding layer of the encoder in the att...
متن کاملThe Contribution of Linguistic Features to Automatic Machine Translation Evaluation
A number of approaches to Automatic MT Evaluation based on deep linguistic knowledge have been suggested. However, n-gram based metrics are still today the dominant approach. The main reason is that the advantages of employing deeper linguistic information have not been clarified yet. In this work, we propose a novel approach for meta-evaluation of MT evaluation metrics, since correlation coffi...
متن کاملError Detection for Statistical Machine Translation Using Linguistic Features
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...
متن کاملImproving statistical machine translation with linguistic information
Statistical machine translation (SMT) should benefit from linguistic information to improve performance but current state-of-the-art models rely purely on data-driven models. There are several reasons why prior efforts to build linguistically annotated models have failed or not even been attempted. Firstly, the practical implementation often requires too much work to be cost effective. Where ad...
متن کاملLinguistic Bases for Machine Translation
Researchers in MT do not work with linguistic theories which are 'on vogue' today. The two special issues on MT of the journal Computational Linguistics (CL 1985) contain eight contributions of the leading teams. In the bibliography of these articles you don't find names like Chomsky, Montague, Bresnan, Gazdar, Kamp, Barwise, Perry etc.[2] Syntactic theories like GB, GPSG, LFG are not mentioned...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Future Internet
سال: 2019
ISSN: 1999-5903
DOI: 10.3390/fi11010022