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Found 14 papers, 1 papers with code
Locality-Sensitive Hashing for Long Context Neural Machine Translation
After its introduction the Transformer architecture quickly became the gold standard for the task of neural machine translation.
Analysis of Positional Encodings for Neural Machine Translation
In this work we analyze and compare the behavior of the Transformer architecture when using different positional encoding methods.
The RWTH Aachen Machine Translation Systems for IWSLT 2017
This work describes the Neural Machine Translation (NMT) system of the RWTH Aachen University developed for the English$German tracks of the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2017.
Detecting Various Types of Noise for Neural Machine Translation
The filtering and/or selection of training data is one of the core aspects to be considered when building a strong machine translation system. In their influential work, Khayrallah and Koehn (2018) investigated the impact of different types of noise on the performance of machine translation systems. In the same year the WMT introduced a shared task on parallel corpus filtering, which went on to be repeated in the following years, and resulted in many different filtering approaches being proposed. In this work we aim to combine the recent achievements in data filtering with the original analysis of Khayrallah and Koehn (2018) and investigate whether state-of-the-art filtering systems are capable of removing all the suggested noise types. We observe that most of these types of noise can be detected with an accuracy of over 90% by modern filtering systems when operating in a well studied high resource setting. However, we also find that when confronted with more refined noise categories or when working with a less common language pair, the performance of the filtering systems is far from optimal, showing that there is still room for improvement in this area of research.
Recurrent Attention for the Transformer
In this work, we conduct a comprehensive investigation on one of the centerpieces of modern machine translation systems: the encoder-decoder attention mechanism.
Efficient Sequence Training of Attention Models using Approximative Recombination
Lastly, it is shown that this technique can be used to effectively perform sequence discriminative training for attention-based encoder-decoder acoustic models on the LibriSpeech task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Integrated Training for Sequence-to-Sequence Models Using Non-Autoregressive Transformer
Complex natural language applications such as speech translation or pivot translation traditionally rely on cascaded models.
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer
Pivot-based neural machine translation (NMT) is commonly used in low-resource setups, especially for translation between non-English language pairs.
Data Filtering using Cross-Lingual Word Embeddings
While we find that our approaches come out at the top on all three tasks, different variants perform best on different tasks.
The RWTH Aachen University Machine Translation Systems for WMT 2019
For the De-En task, none of the tested methods gave a significant improvement over last years winning system and we end up with the same performance, resulting in 39. 6{\%} BLEU on newstest2019.
Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron
We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data.
The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task
We use several rule-based, heuristic methods to preselect sentence pairs.
The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018
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.
