SCIENTIA SINICA Informationis, Volume 47, Issue 8: 1036(2017) https://doi.org/10.1360/N112016-00281

## Learning dependency edge transfer rule representation using encoder-decoder

• ReceivedMar 8, 2016
• AcceptedApr 2, 2016
• PublishedJun 20, 2017
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### Abstract

In existing statistical machine translation models, especially syntax-based models, there has always been a trade-off between the amount of information a translation unit preserves and its ability to generalize when translating new sentences. Neural networks have been successfully employed in reordering and end-to-end machine translation problems. In this paper, we propose a novel syntactic translation rule encoder-decoder based on neural networks. It is a dependency edge transfer rule encoder-decoder (DETED) that leverages the source side of a transfer rule and local context as input, and outputs the target side of that in order to learn the source-to-target matching of the dependency edge transfer rules. It shares not only the benefit of dependency edge, which is the most relaxed syntactic constraint, in order to ensure its generalization ability, but also the local context as additional information in order to improve its matching ability. The structure of the encoder-decoder is quite concise. With the source side of a translation rule as the input, it decodes the corresponding target side of the translation rule, and makes it clear the positional relation of the dependency edge. The generator is used to re-score the transfer rules when decoding. Experiments on three NIST test sets are presented. The results indicate a significant performance improvement with an average BLEU score of 1.39 above the baseline value.

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• Figure 1

Dependency edge transfer rules. (a) Dependency tree and word alignment; (b) extracted dependency edge transfer rules; (c) generalisation of transfer rules; (d) ambiguities of dependency edge transfer rules

• Figure 2

Dependency edge transfer translation process. (a), (b) Analysis; (c) transfer; (d)$\sim$(f) generation

• Figure 3

Dependency edge transfer rule encoder-decoder

• Figure 4

Source dependency edge encoder

•

Algorithm 1 在翻译解码时使用依存边转换翻译规则编码解码器计算目标端依存边的概率

Require: 源端依存树的节点$n$; 依存边转换翻译规则集合$R$; 依存边转换翻译规则编码解码器$D$;

Output: 节点$n$作为头节点时, 所有源端依存边对应的候选目标端依存边的概率集合$P$;

if $n$ 不是叶子节点 then

抽取该节点与其所有依存节点之间的源端依存边集合$E$;

for $e\in E$

利用$R$, 将$e$投射得到候选目标端依存边集合$F$;

将$e$输入到$D$中;

在$D$的输出层, 计算$F$中每条候选目标端依存边的概率$p$;

将$p$放入集合$P$中;

end for

return $P$

end if

• Table 1   BLEU-4 scores (%) on NIST MT03$\sim$05 $^{\rm a)b)}$
 System MT03 MT04 MT05 Average Moses 32.30 33.43 31.44 32.39 DEBT 32.57 35.06 31.36 32.99 +DETED 33.8* 36.58* 32.76* 34.38
• Table 2   BLEU-4 scores (%) of different components
 System MT03 MT04 MT05 Average DEBT 32.57 35.06 31.36 32.99 +${\rm head}_{\rm tgt}$ 33.52 36.35 31.67 33.85 +${\rm dep}_{\rm tgt}$ 33.43 35.81 31.40 33.55 +${\rm lr}_{\rm tgt}$ 33.30 36.32 32.10 33.91 +${\rm cd}_{\rm tgt}$ 33.41 36.50 32.39 34.10 +DETED 33.80 36.58 32.76 34.38
• Table 3   BLEU-4 scores (%) on NIST MT03$\sim$05 test set, with different contexts as input $^{\rm c)d)}$
 System MT03 MT04 MT05 Average DEBT 32.57 35.06 31.36 32.99 +nocon 33.56 36.06 32.37 33.99 +con1 33.80 36.58 32.76 34.38 +con2 33.73 36.52 32.45 34.23 +con3 33.94 36.24 32.49 34.22
• Table 4   Decoding time cost on NIST MT03
 System Decoding time cost (s) Diff Moses 1081.71 – DEBT 1090.89 – +DETED 1297.83 +18.97%
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