Dependency Graph-to-String Statistical Machine Translation

• URL: http://arxiv.org/abs/2103.11089v1
• Date: Sat, 20 Mar 2021 04:20:56 GMT
• Title: Dependency Graph-to-String Statistical Machine Translation
• Authors: Liangyou Li and Andy Way and Qun Liu
• Abstract summary: Source graphs are constructed from dependency trees with extra links so that non-syntactic phrases are connected. Inspired by phrase-based models, we first introduce a translation model which segments a graph into a sequence of disjoint subgraphs and generates a translation by combining subgraph translations left-to-right using beam search.
• Score: 40.91501320179243
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present graph-based translation models which translate source graphs into target strings. Source graphs are constructed from dependency trees with extra links so that non-syntactic phrases are connected. Inspired by phrase-based models, we first introduce a translation model which segments a graph into a sequence of disjoint subgraphs and generates a translation by combining subgraph translations left-to-right using beam search. However, similar to phrase-based models, this model is weak at phrase reordering. Therefore, we further introduce a model based on a synchronous node replacement grammar which learns recursive translation rules. We provide two implementations of the model with different restrictions so that source graphs can be parsed efficiently. Experiments on Chinese--English and German--English show that our graph-based models are significantly better than corresponding sequence- and tree-based baselines.

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