Neural Machine Translation With Explicit Phrase Alignment

By Jiacheng Zhang, Huanbo Luan, Maosong Sun, Feifei Zhai, Jingfang Xu, Yang Liu. 2019

34 citations recorded by the source corpus.

While neural machine translation has achieved state-of-the-art translation performance, it is unable to capture the alignment between the input and output during the translation process. The lack of alignment in neural machine translation models leads to three problems: it is hard to (1) interpret the translation process, (2) impose lexical constraints, and (3) impose structural constraints. These problems not only increase the difficulty of designing new architectures for neural machine translation, but also limit its applications in practice. To alleviate these problems, we propose to introduce explicit phrase alignment into the translation process of arbitrary neural machine translation models. The key idea is to build a search space similar to that of phrase-based statistical machine translation for neural machine translation where phrase alignment is readily available. We design a new decoding algorithm that can easily impose lexical and structural constraints. Experiments show that our approach makes the translation process of neural machine translation more interpretable without sacrificing translation quality. In addition, our approach achieves significant improvements in lexically and structurally constrained translation tasks.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Pascale Fung
2. Andrea Madotto
3. Wenliang Dai
4. Nayeon Lee
5. Yan Xu
6. Etsuko Ishii
7. Ziwei Ji
8. Dan Su

In Computer Vision and Pattern Recognition

1. Pascale Fung
2. Wenliang Dai
3. Ziwei Ji
4. Dan Su
5. Tiezheng Yu
6. Heng Yu
7. Weihua Luo
8. Yejin Bang

In Natural Language Processing

1. Pascale Fung
2. Andrea Madotto
3. Nayeon Lee
4. Etsuko Ishii
5. Yejin Bang
6. Nathaniel Weir
7. Yan Xu
8. Rita Frieske

Citations in the corpus

• Survey of Hallucination in Natural Language Generation (2022, methodology): “There are also some work modifying the decoder structure for avoiding hallucination, such as multibranch decoder [132], uncertainty-aware decoder [177], dual decoder consisting of a sequential decoder and a tree-based decoder [145], and constrained decoder with lexical or structural limitations [5, ”
• InFillmore: Frame-Guided Language Generation with Bidirectional Context (2021, methodology): “, 2020), machine translation (Zhang et al., 2021), and abstractive summarization (Mao et al.”
• Towards Enhancing Faithfulness for Neural Machine Translation (2020, background): “Zhang et al. (2019), Feng et al. (2020) and Garg et al. (2019) proposed to introduce word alignment information in Trans-former to improve translation accuracy.”