Translation Template Learning Based on Hidden Markov Modeling
نویسندگان
چکیده
This paper addresses a novel translation method based on Hidden Markov Model using template rules after learning them from the bilingual corpus. The method can enhance the translation accuracy and ensure a low complexity in comparing with the pervious template learning translation method and draws a new perspective for applying statistical machine learning on example based translations. domain.
منابع مشابه
Example-Based Sentence Reduction Using Hidden Markov Model
Sentence reduction is the problem of removing redundant words or phrases from an input sentence by creating a new sentence, in which the gist of the meaning of the original sentence is unchanged. All most previous methods required a syntax parser before reducing sentence. However, these methods were difficult to apply to a language in which there was not a reliable parser. In this paper, we pro...
متن کاملTemplate Learning using Wavelet Domain Statistical Models
Wavelets have been used with great success in applications such as signal denoising, compression, estimation and feature extraction. This is because of their ability to capture singularities in the signal with a few coefficients. Applications that consider the statistical dependencies of wavelet coefficients have been shown to perform better than those which assume the wavelet coefficients as i...
متن کاملP − 20 01 Speech - to - Speech Translation Based on Finite - State Transducers
Nowadays, the most successful speech recognition systems are based on stochastic finite-state networks (hidden Markov models and n-grams). Speech translation can be accomplished in a similar way as speech recognition. Stochastic finite-state transducers, which are specific stochastic finitestate networks, have proved very adequate for translation modeling. In this work a speech-to-speech transl...
متن کاملText-Independent Speaker Identification
Speaker identification is a difficult task, and the task has several different approaches. The state of the art for speaker identification techniques include dynamic time warped(DTW) template matching, Hidden Markov Modeling(HMM), and codebook schemes based on vector quantization(VQ)[2]. In this project, the vector quantization approach will be used, due to ease of implementation and high accur...
متن کاملA novel learning method by structural reduction of DAGs for on-line OCR applications
This paper introduces a learning algorithm for a neural structure, Directed Acyclic Graphs (DAGs) that is structurally based, i.e. reduction and manipulation of internal structure are directly linked to learning. This paper extends the concepts in [1] for template matching to a neural structure with capabilities for generalization. DAG-Learning is derived from concepts in Finite State Transduce...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003