Julian Schamper


2019

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Generalizing Back-Translation in Neural Machine Translation
Miguel Graça | Yunsu Kim | Julian Schamper | Shahram Khadivi | Hermann Ney
Proceedings of the Fourth Conference on Machine Translation (Volume 1: Research Papers)

Back-translation — data augmentation by translating target monolingual data — is a crucial component in modern neural machine translation (NMT). In this work, we reformulate back-translation in the scope of cross-entropy optimization of an NMT model, clarifying its underlying mathematical assumptions and approximations beyond its heuristic usage. Our formulation covers broader synthetic data generation schemes, including sampling from a target-to-source NMT model. With this formulation, we point out fundamental problems of the sampling-based approaches and propose to remedy them by (i) disabling label smoothing for the target-to-source model and (ii) sampling from a restricted search space. Our statements are investigated on the WMT 2018 German <-> English news translation task.

2018

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The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018
Miguel Graça | Yunsu Kim | Julian Schamper | Jiahui Geng | Hermann Ney
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). Our work is based on iterative back-translation using a shared encoder-decoder NMT model. We extensively compare different vocabulary types, word embedding initialization schemes and optimization methods for our model. We also investigate gating and weight normalization for the word embedding layer.

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The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018
Julian Schamper | Jan Rosendahl | Parnia Bahar | Yunsu Kim | Arne Nix | Hermann Ney
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

This paper describes the statistical machine translation systems developed at RWTH Aachen University for the German→English, English→Turkish and Chinese→English translation tasks of the EMNLP 2018 Third Conference on Machine Translation (WMT 2018). We use ensembles of neural machine translation systems based on the Transformer architecture. Our main focus is on the German→English task where we to all automatic scored first with respect metrics provided by the organizers. We identify data selection, fine-tuning, batch size and model dimension as important hyperparameters. In total we improve by 6.8% BLEU over our last year’s submission and by 4.8% BLEU over the winning system of the 2017 German→English task. In English→Turkish task, we show 3.6% BLEU improvement over the last year’s winning system. We further report results on the Chinese→English task where we improve 2.2% BLEU on average over our baseline systems but stay behind the 2018 winning systems.

2017

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Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes
Yunsu Kim | Julian Schamper | Hermann Ney
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words. We scale up the expectation-maximization (EM) algorithm to learn a large translation table without any parallel text or seed lexicon. First, we solve the memory bottleneck and enforce the sparsity with a simple thresholding scheme for the lexicon. Second, we initialize the lexicon training with word classes, which efficiently boosts the performance. Our methods produced promising results on two large-scale unsupervised translation tasks.

2015

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UNRAVELA Decipherment Toolkit
Malte Nuhn | Julian Schamper | Hermann Ney
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Improved Decipherment of Homophonic Ciphers
Malte Nuhn | Julian Schamper | Hermann Ney
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Beam Search for Solving Substitution Ciphers
Malte Nuhn | Julian Schamper | Hermann Ney
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)