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Found 20 papers, 1 papers with code
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.
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.
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.
Document-Level Language Models for Machine Translation
Finally, we explore language model fusion in the light of recent advancements in large language models.
Improving Long Context Document-Level Machine Translation
Document-level context for neural machine translation (NMT) is crucial to improve the translation consistency and cohesion, the translation of ambiguous inputs, as well as several other linguistic phenomena.
Improving Language Model Integration for Neural Machine Translation
The integration of language models for neural machine translation has been extensively studied in the past.
On Search Strategies for Document-Level Neural Machine Translation
On the other hand, in most works, the question on how to perform search with the trained model is scarcely discussed, sometimes not mentioned at all.
Does Joint Training Really Help Cascaded Speech Translation?
Currently, in speech translation, the straightforward approach - cascading a recognition system with a translation system - delivers state-of-the-art results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Revisiting Checkpoint Averaging for Neural Machine Translation
Checkpoint averaging is a simple and effective method to boost the performance of converged neural machine translation models.
Is Encoder-Decoder Redundant for Neural Machine Translation?
Encoder-decoder architecture is widely adopted for sequence-to-sequence modeling tasks.
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics
Here, we propose a method for continual active learning operating on a stream of medical images in a multi-scanner setting.
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.
Towards a Better Understanding of Label Smoothing in Neural Machine Translation
In order to combat overfitting and in pursuit of better generalization, label smoothing is widely applied in modern neural machine translation systems.
Diving Deep into Context-Aware Neural 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.
Dynamic memory to alleviate catastrophic forgetting in continuous learning settings
In medical imaging, technical progress or changes in diagnostic procedures lead to a continuous change in image appearance.
Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University
AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Exploring Kernel Functions in the Softmax Layer for Contextual Word Classification
Prominently used in support vector machines and logistic regressions, kernel functions (kernels) can implicitly map data points into high dimensional spaces and make it easier to learn complex decision boundaries.
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.
Improving Neural Language Models with Weight Norm Initialization and Regularization
Embedding and projection matrices are commonly used in neural language models (NLM) as well as in other sequence processing networks that operate on large vocabularies.
