Leonard Dahlmann


Diving Deep into Context-Aware Neural Machine Translation
Jingjing Huo | Christian Herold | Yingbo Gao | Leonard Dahlmann | Shahram Khadivi | Hermann Ney
Proceedings of the Fifth Conference on Machine Translation

Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT models is not well explored yet. This paper analyzes the performance of document-level NMT models on four diverse domains with a varied amount of parallel document-level bilingual data. We conduct a comprehensive set of experiments to investigate the impact of document-level NMT. We find that there is no single best approach to document-level NMT, but rather that different architectures come out on top on different tasks. Looking at task-specific problems, such as pronoun resolution or headline translation, we find improvements in the context-aware systems, even in cases where the corpus-level metrics like BLEU show no significant improvement. We also show that document-level back-translation significantly helps to compensate for the lack of document-level bi-texts.


Word-based Domain Adaptation for Neural Machine Translation
Shen Yan | Leonard Dahlmann | Pavel Petrushkov | Sanjika Hewavitharana | Shahram Khadivi
Proceedings of the 15th International Conference on Spoken Language Translation

In this paper, we empirically investigate applying word-level weights to adapt neural machine translation to e-commerce domains, where small e-commerce datasets and large out-of-domain datasets are available. In order to mine in-domain like words in the out-of-domain datasets, we compute word weights by using a domain-specific and a non-domain-specific language model followed by smoothing and binary quantization. The baseline model is trained on mixed in-domain and out-of-domain datasets. Experimental results on En → Zh e-commerce domain translation show that compared to continuing training without word weights, it improves MT quality by up to 3.11% BLEU absolute and 1.59% TER. We have also trained models using fine-tuning on the in-domain data. Pre-training a model with word weights improves fine-tuning up to 1.24% BLEU absolute and 1.64% TER, respectively.


Neural and Statistical Methods for Leveraging Meta-information in Machine Translation
Shahram Khadivi | Patrick Wilken | Leonard Dahlmann | Evgeny Matusov
Proceedings of Machine Translation Summit XVI: Research Track

Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search
Leonard Dahlmann | Evgeny Matusov | Pavel Petrushkov | Shahram Khadivi
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In this paper, we introduce a hybrid search for attention-based neural machine translation (NMT). A target phrase learned with statistical MT models extends a hypothesis in the NMT beam search when the attention of the NMT model focuses on the source words translated by this phrase. Phrases added in this way are scored with the NMT model, but also with SMT features including phrase-level translation probabilities and a target language model. Experimental results on German-to-English news domain and English-to-Russian e-commerce domain translation tasks show that using phrase-based models in NMT search improves MT quality by up to 2.3% BLEU absolute as compared to a strong NMT baseline.