Translation Rules with Right-Hand Side Lattices

Cromierès, Fabien; Kurohashi, Sadao

doi:10.3115/v1/d14-1063

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Flexible non-terminals are powerful to handle floating subtrees and it achieve better translation quality. However the computational cost of decoding becomes high even though they are compactly represented in the lattice form (Cromieres and Kurohashi, 2014). In our experiments, using flexible nonterminals causes the decoding to be 3 to 6 times slower than when they are not used.…”

Section: Introductionmentioning

confidence: 83%

Insertion Position Selection Model for Flexible Non-Terminals in Dependency Tree-to-Tree Machine Translation

Nakazawa

Richardson

Kurohashi

2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Dependency tree-to-tree translation models are powerful because they can naturally handle long range reorderings which is important for distant language pairs. The translation process is easy if it can be accomplished only by replacing non-terminals in translation rules with other rules. However it is sometimes necessary to adjoin translation rules. Flexible non-terminals have been proposed as a promising solution for this problem. A flexible non-terminal provides several insertion position candidates for the rules to be adjoined, but it increases the computational cost of decoding. In this paper we propose a neural network based insertion position selection model to reduce the computational cost by selecting the appropriate insertion positions. The experimental results show the proposed model can select the appropriate insertion position with a high accuracy. It reduces the decoding time and improves the translation quality owing to reduced search space.

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Section: Introductionmentioning

confidence: 83%

Insertion Position Selection Model for Flexible Non-Terminals in Dependency Tree-to-Tree Machine Translation

Nakazawa

Richardson

Kurohashi

2016

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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“…There are many available hypotheses for one subtree, and also, there are many possible hypothesis combinations. The best combination is detected by a lattice-based decoder, which optimizes a loglinear model (Cromieres and Kurohashi, 2014). In the example in Figure 1, four hypotheses are used.…”

Section: Overview Of the Kyotoebmt Systemmentioning

confidence: 99%

Cross-language Projection of Dependency Trees with Constrained Partial Parsing for Tree-to-Tree Machine Translation

Shen¹,

Chu²,

Cromierès³

et al. 2016

Proceedings of the First Conference on Machine Translation: Volume 1, Research Papers

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Tree-to-tree machine translation (MT) that utilizes syntactic parse trees on both source and target sides suffers from the non-isomorphism of the parse trees due to parsing errors and the difference of annotation criterion between the two languages. In this paper, we present a method that projects dependency parse trees from the language side that has a high quality parser, to the side that has a low quality parser, to improve the isomorphism of the parse trees. We first project a part of the dependencies with high confidence to make a partial parse tree, and then complement the remaining dependencies with partial parsing constrained by the already projected dependencies. MT experiments verify the effectiveness of our proposed method.

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“…There are many available hypotheses for one subtree, and also, there are many possible hypothesis combinations. The best combination is detected by a lattice-based decoder, which optimizes a log-linear model (Cromieres and Kurohashi 2014). In the example in Figure 1, four hypotheses are used.…”

Section: Overview Of the Kyotoebmt Systemmentioning

confidence: 99%

Constrained Partial Parsing Based Dependency Tree Projection for Tree-to-Tree Machine Translation

Chu

Shen

Cromierès

et al. 2017

Journal of Natural Language Processing

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Ideally, tree-to-tree machine translation (MT) that utilizes syntactic parse trees on both source and target sides could preserve non-local structure, and thus generate fluent and accurate translations. In practice, however, firstly, high quality parsers for both source and target languages are difficult to obtain; secondly, even if we have high quality parsers on both sides, they still can be non-isomorphic because of the annotation criterion difference between the two languages. The lack of isomorphism between the parse trees makes it difficult to extract translation rules. This extremely limits the performance of tree-to-tree MT. In this article, we present an approach that projects dependency parse trees from the language side that has a high quality parser, to the side that has a low quality parser, to improve the isomorphism of the parse trees. We first project a part of the dependencies with high confidence to make a partial parse tree, and then complement the remaining dependencies with partial parsing constrained by the already projected dependencies. Experiments conducted on the Japanese-Chinese and English-Chinese language pairs show that our proposed method significantly improves the performance on both the two language pairs.

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Translation Rules with Right-Hand Side Lattices

Cited by 4 publications

References 15 publications

Insertion Position Selection Model for Flexible Non-Terminals in Dependency Tree-to-Tree Machine Translation

Insertion Position Selection Model for Flexible Non-Terminals in Dependency Tree-to-Tree Machine Translation

Cross-language Projection of Dependency Trees with Constrained Partial Parsing for Tree-to-Tree Machine Translation

Constrained Partial Parsing Based Dependency Tree Projection for Tree-to-Tree Machine Translation

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