论文标题
整合神经机器翻译的矢量化词汇约束
Integrating Vectorized Lexical Constraints for Neural Machine Translation
论文作者
论文摘要
在许多实际情况下，控制具有预先指定约束的NMT模型的生成的NMT模型的生成，该神经机器翻译（NMT）在许多实际情况下都很重要。由于NMT模型中离散约束和连续向量之间的表示差距，大多数现有作品选择构建合成数据或修改解码算法以施加词汇约束，将NMT模型视为黑匣子。在这项工作中，我们建议通过将约束直接整合到NMT模型中来打开这个黑匣子。具体而言，我们将源和目标约束矢量化为连续的密钥和值，这些密钥和值可以通过NMT模型的注意模块来利用。提出的集成方法基于以下假设：注意模块中的密钥和值之间的对应关系自然适合建模约束对。实验结果表明，我们的方法始终在四个语言对上胜过几个代表性基准，这表明了整合矢量化词汇约束的优越性。
Lexically constrained neural machine translation (NMT), which controls the generation of NMT models with pre-specified constraints, is important in many practical scenarios. Due to the representation gap between discrete constraints and continuous vectors in NMT models, most existing works choose to construct synthetic data or modify the decoding algorithm to impose lexical constraints, treating the NMT model as a black box. In this work, we propose to open this black box by directly integrating the constraints into NMT models. Specifically, we vectorize source and target constraints into continuous keys and values, which can be utilized by the attention modules of NMT models. The proposed integration method is based on the assumption that the correspondence between keys and values in attention modules is naturally suitable for modeling constraint pairs. Experimental results show that our method consistently outperforms several representative baselines on four language pairs, demonstrating the superiority of integrating vectorized lexical constraints.
