[PDF] Top 20 Maximum Entropy based Rule Selection Model for Syntax based Statistical Machine Translation

... The MERS models combines rich contextual in- formation as features to help the decoder perform rule selection. Table 4 shows the effect of different feature sets. We test two classes of feature sets: the ... See full document

... Maximum Entropy based Rule Selection Model for Syntax-based Statistical Machine Translation Qun Liu, Zhongjun He, Yang Liu and Shouxun Lin.. Indirect-HMM-based Hypothesis Alignment for C[r] ... See full document

... the translation of the word. In this paper, we propose a sense-based transla- tion model to integrate word senses into statistical machine ...tagger based on a nonparametric ... See full document

... language model and those of the transla- tion table: Considering the pertinent phrases as single units will reduce the entropy of the language ...rent statistical machine translations are per- ... See full document

... simple model for re- ordering conditioned on the source side parse ...The model is learned using a parallel corpus of source-target sentence pairs, machine generated word alignments, and source side ... See full document

... language model to capture long- distance word ...a maximum entropy classifier to select proper translation rules during ...joint model for SCFG rule ...free model and ... See full document

... data selection part in the Figure 1 and transla- tion selection by the translation evaluation and se- lection part in the Figure 1, as the ja-en ... See full document

... that translation quality of English-Japanese PBMT is significantly improved using a transla- tion model learned by English sentences prepro- cessed by Head Finalization and Japanese sen- ...a ... See full document

... We carry out experiment on Chinese-English NIST evaluation tasks. We use FBIS corpus (250K sentence pairs) as training data with the source side parsed by a modified Charniak parser (Charniak 2000) which can output a ... See full document

... n-gram translation model to incorpo- rate lexical dependencies that span rule boundaries (Marino et ...sequence model) could help phrase-based translation models to over- come ... See full document

... for statistical machine translation of natural languages based on direct maximum entropy mod- els, which contains the widely used sour- ce-channel approach as a special ... See full document

... of maximum entropy has been applied to the problem of lexical selection before; in particular, Berger et ...lexical selection in statistical MT as a classification ...rate ... See full document

... cal machine translation (SMT) is to choose the appropriate translation rules based on the sentence ...space rule selection (CSRS) model for syntax-based SMT ... See full document

... information of corners when extracting blocks. Line 4 and 5 are similar to the phrase extraction algorithm by Och (2003b). Different from Och, we just extend one word which is aligned to null on the boundary of target ... See full document

... the rule contributions and the redundances issues using the presented rule classification based on some real translation systems, we plan to implement some syn- chronous grammar based ... See full document

... human translation for example, is its partly repetitive ...same machine translation system is handled by multiple post-editors, then the opportunities for factoring corrections become much more ...in ... See full document

... In the phrase-based approach, sentences are first segmented into variable length segments or phrases, then reordered. Word reorderings can be divided in two tighly intertwined parts: local reorderings that take ... See full document

... Despite the encouraging experiment results, it is still not very clear how the syntax-based and distance-based models complement each other in improving word reordering. In future we need to ... See full document

... In rule-based MT, various linguistic rules are defined and combined in (Arnold, ...tistical machine translation models have re- sulted from the word-based models (Brown et ...in ... See full document

... Petrov, S., Barrett, L., Thibaux, R., Klein, D. (2006). Learning accurate, compact, and interpretable tree annotation. Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual ... See full document