Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation

By Eleftheria Briakou, Marine Carpuat. 2021

15 citations recorded by the source corpus.

While it has been shown that Neural Machine Translation (NMT) is highly sensitive to noisy parallel training samples, prior work treats all types of mismatches between source and target as noise. As a result, it remains unclear how samples that are mostly equivalent but contain a small number of semantically divergent tokens impact NMT training. To close this gap, we analyze the impact of different types of fine-grained semantic divergences on Transformer models. We show that models trained on synthetic divergences output degenerated text more frequently and are less confident in their predictions. Based on these findings, we introduce a divergent-aware NMT framework that uses factors to help NMT recover from the degradation caused by naturally occurring divergences, improving both translation quality and model calibration on EN-FR 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. Phillip Rust
6. Tiezheng Yu
7. Anders Søgaard
8. Yejin Bang

In Natural Language Processing

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

Citations in the corpus

• Survey of Hallucination in Natural Language Generation (2022, background): “[153] Briakou and Carpuat [15], Junczys-Dowmunt [79] Captioning Biten et al.”
• Challenges and Strategies in Cross-Cultural NLP (2022, background): “In MT, semantic divergences are usually treated as noise, or in any case, as imperfect translations (Briakou and Carpuat, 2021).”