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20162024
most citedRoot Mean Square Layer Normalization

106 citations · 471 across the 41 of their papers we have counts for

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Showing 2020Show all

11 papers · 1 filter

cs.CL2020

The Impact of Text Presentation on Translator Performance

Samuel Läubli, Patrick Simianer, Joern Wuebker +3

Widely used computer-aided translation (CAT) tools divide documents into segments such as sentences and arrange them in a side-by-side, spreadsheet-like view. We present the first…

cs.CL2020

Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks

Denis Emelin, Ivan Titov, Rico Sennrich

Word sense disambiguation is a well-known source of translation errors in NMT. We posit that some of the incorrect disambiguation choices are due to models' over-reliance on datase…

cs.CL2020★ 6 cited

Subword Segmentation and a Single Bridge Language Affect Zero-Shot Neural Machine Translation

Annette Rios, Mathias Müller, Rico Sennrich

Zero-shot neural machine translation is an attractive goal because of the high cost of obtaining data and building translation systems for new translation directions. However, prev…

cs.CL2020★ 4 cited

Fast Interleaved Bidirectional Sequence Generation

Biao Zhang, Ivan Titov, Rico Sennrich

Independence assumptions during sequence generation can speed up inference, but parallel generation of highly inter-dependent tokens comes at a cost in quality. Instead of assuming…

cs.CL2020

Adaptive Feature Selection for End-to-End Speech Translation

Biao Zhang, Ivan Titov, Barry Haddow +1

Information in speech signals is not evenly distributed, making it an additional challenge for end-to-end (E2E) speech translation (ST) to learn to focus on informative features. I…

cs.CL2020

Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation

Elena Voita, Rico Sennrich, Ivan Titov

In Neural Machine Translation (and, more generally, conditional language modeling), the generation of a target token is influenced by two types of context: the source and the prefi…