Linguistically Motivated Statistical Machine Translation: by Deyi Xiong, Min Zhang

By Deyi Xiong, Min Zhang

This ebook presents a large choice of algorithms and versions to combine linguistic wisdom into Statistical laptop Translation (SMT). It is helping improve traditional SMT to linguistically inspired SMT through bettering the next 3 crucial elements: translation, reordering and bracketing versions. It additionally serves the aim of selling the in-depth research of the affects of linguistic wisdom on computing device translation. ultimately it offers a scientific creation of Bracketing Transduction Grammar (BTG) established SMT, one of many cutting-edge SMT formalisms, in addition to a case learn of linguistically prompted SMT on a BTG-based platform.

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Extra info for Linguistically Motivated Statistical Machine Translation: Models and Algorithms

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This probability does not provide information about the probability of the hypothesis in the context of the complete translation. In A* decoding for SMT (Och et al. 2001; Zhang and Gildea 2006), different heuristic functions are used to estimate a “future” probability for completing a partial hypothesis. In CKY bottom-up parsing, Goodman (1997) introduces a prior probability into the beam threshold pruning. All of these probabilities are capable of capturing contextual information outside partial hypotheses.

Unfortunately, we are not able to find such an ideal beam threshold since we do not know exactly the distribution of hypotheses beforehand. Most researchers empirically select a beam threshold on a development set and then use it constantly on a test set. We call this strategy fixed threshold pruning (FTP). In order to guarantee a high translation quality, a loose beam threshold is usually used at the cost of slow decoding speed. A better strategy is to dynamically adjust the beam threshold with a hidden variable.

Their PMI values are shown in the PMI rectangle in Fig. 6. 10 according to the Eq. 43). 5 Pruning Search space pruning is very important for SMT decoders. Normally, the following four pruning methods are widely used in SMT systems. We introduce them in the context of BTG-based SMT. • Hypothesis recombination. Whenever two partial hypotheses in the same cell are equivalent, we will recombine them by discarding the one with a lower score. By equivalence, we mean that the two partial hypotheses cover the same span on the source side and contain the same leftmost/rightmost n − 1 words on the target 34 2 BTG-Based SMT side.

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