We describe a unified and coherent syntactic framework for supporting a semanticallyinformed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality—and further demonstrates that large gains can be achieved for low-resource languages with different word order than English.
Related work in syntax-based MT includes @cite_4 , where a series of syntax rules are applied to a source language string to produce a target language phrase structure tree. The Penn English Treebank @cite_5 is used as the source for the syntactic labels and syntax trees are relabeled to improve translation quality. In this work, node-internal and node-external information is used to relabel nodes, similar to earlier work where structural context was used to relabel nodes in the parsing domain @cite_9 . Klein and Manning's methods include lexicalizing determiners and percent markers, making more fine-grained VP categories, and marking the properties of sister nodes on nodes. All of these labels are derivable from the trees themselves and not from an auxiliary source.
@cite_5 @cite_9 @cite_4
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We identify problems with the Penn Tree-bank that render it imperfect for syntax-based machine translation and propose methods of relabeling the syntax trees to improve translation quality. We develop a system incorporating a handful of relabeling strategies that yields a statistically significant improvement of 2.3 BLEU points over a baseline syntax-based system. We present a simple and effective framework for exploiting multiple monolingual treebanks with different annotation guidelines for parsing. Several types of transformation patterns (TP) are designed to capture the systematic annotation inconsistencies among different tree-banks. Based on such TPs, we design quasi-synchronous grammar features to augment the baseline parsing models. Our approach can significantly advance the state-of-the-art parsing accuracy on two widely used target tree-banks (Penn Chinese Treebank 5.1 and 6.0) using the Chinese Dependency Treebank as the source treebank. The improvements are respectively 1.37 and 1.10 with automatic part-of-speech tags. Moreover, an indirect comparison indicates that our approach also outperforms previous work based on treebank conversion. In example (1), a sentence from a Wall Street Journal article taken from the Penn TreeBank corpus is further segmented into four EDUs, (1a), (1b), (1c) and (1d) (RST, 2002). Discourse segmentation, clearly, is not as easy as sentence boundary detection. The lack of consensus with regards to what constitutes an elementary discourse unit adds to the difficulty. Building a rule based discourse segmenter can be a tedious task since these rules would have to be based on the underlying grammar of the particular parser that is to be used. Therefore, we adopted a neural network model for automatically building a discourse segmenter from an underlying corpus of segmented text. We chose to use part-of-speech tags, syntactic information, discourse cues and punctuation. Our ultimate goal is to build a discourse parser that uses this discourse segmenter. The data that we used to train and test our discourse segmenter is the RST-DT (RST, 2002) corpus. The corpus contains 385 Wall Street Journal articles from the Penn Treebank. The training set consists of 347 articles for a total of 6132 sentences, whilst the test set contains 38 articles for a total of 991 sentences. The RST-DT corpus provides us with pairs of sentences and EDUs. For the syntactic structure of the sentences, we have used both the gold standard Penn Treebank data and syntactic parse trees generated by (Charniak, 2000). As regards the discourse cues, we used a list of 168 possible discourse markers. This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in (1998). We apply the boosting method to parsing the Wall Street Journal treebank. The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model. The new model achieved 89.75 F-measure, a 13 relative decrease in F measure error over the baseline model's score of 88.2 . The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach. We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work on feature selection methods within log-linear (maximum-entropy) models. Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation.