106 citations · 785 across the 59 of their papers we have counts for
11 papers · 1 filter
Distributionally Robust Recurrent Decoders with Random Network Distillation
Antonio Valerio Miceli-Barone, Alexandra Birch, Rico Sennrich
Neural machine learning models can successfully model language that is similar to their training distribution, but they are highly susceptible to degradation under distribution shi…
On the Limits of Minimal Pairs in Contrastive Evaluation
Jannis Vamvas, Rico Sennrich
Minimal sentence pairs are frequently used to analyze the behavior of language models. It is often assumed that model behavior on contrastive pairs is predictive of model behavior…
Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models
Jiaoda Li, Duygu Ataman, Rico Sennrich
Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available. However,…
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT
Elena Voita, Rico Sennrich, Ivan Titov
Differently from the traditional statistical MT that decomposes the translation task into distinct separately learned components, neural machine translation uses a single neural ne…
How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?
Chantal Amrhein, Rico Sennrich
Data-driven subword segmentation has become the default strategy for open-vocabulary machine translation and other NLP tasks, but may not be sufficiently generic for optimal learni…
Improving Zero-shot Cross-lingual Transfer between Closely Related Languages by injecting Character-level Noise
Noëmi Aepli, Rico Sennrich
Cross-lingual transfer between a high-resource language and its dialects or closely related language varieties should be facilitated by their similarity. However, current approache…
Revisiting Negation in Neural Machine Translation
Gongbo Tang, Philipp Rönchen, Rico Sennrich +1
In this paper, we evaluate the translation of negation both automatically and manually, in English--German (EN--DE) and English--Chinese (EN--ZH). We show that the ability of neura…
Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translation
Mathias Müller, Rico Sennrich
Neural Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy no…
On Biasing Transformer Attention Towards Monotonicity
Annette Rios, Chantal Amrhein, Noëmi Aepli +1
Many sequence-to-sequence tasks in natural language processing are roughly monotonic in the alignment between source and target sequence, and previous work has facilitated or enfor…
Sparse Attention with Linear Units
Biao Zhang, Ivan Titov, Rico Sennrich
Recently, it has been argued that encoder-decoder models can be made more interpretable by replacing the softmax function in the attention with its sparse variants. In this work, w…
BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation
Yuchen Eleanor Jiang, Tianyu Liu, Shuming Ma +7
Standard automatic metrics, e.g. BLEU, are not reliable for document-level MT evaluation. They can neither distinguish document-level improvements in translation quality from sente…