most citedNeural machine translation for low-resource languages

30 citations · 32 across the 5 of their papers we have counts for

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cs.CL2019

Zero-shot transfer for implicit discourse relation classification

Murathan Kurfalı, Robert Östling

Automatically classifying the relation between sentences in a discourse is a challenging task, in particular when there is no overt expression of the relation. It becomes even more…

cs.CL20191 cited

What do Language Representations Really Represent?

Johannes Bjerva, Robert Östling, Maria Han Veiga +2

A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the cor…

cs.CL2017

The Helsinki Neural Machine Translation System

Robert Östling, Yves Scherrer, Jörg Tiedemann +2

We introduce the Helsinki Neural Machine Translation system (HNMT) and how it is applied in the news translation task at WMT 2017, where it ranked first in both the human and autom…

cs.CL201730 cited

Neural machine translation for low-resource languages

Robert Östling, Jörg Tiedemann

Neural machine translation (NMT) approaches have improved the state of the art in many machine translation settings over the last couple of years, but they require large amounts of…

cs.CL2017

SU-RUG at the CoNLL-SIGMORPHON 2017 shared task: Morphological Inflection with Attentional Sequence-to-Sequence Models

Robert Östling, Johannes Bjerva

This paper describes the Stockholm University/University of Groningen (SU-RUG) system for the SIGMORPHON 2017 shared task on morphological inflection. Our system is based on an att…

cs.CL20171 cited

Articulation rate in Swedish child-directed speech increases as a function of the age of the child even when surprisal is controlled for

Johan Sjons, Thomas Hörberg, Robert Östling +1

In earlier work, we have shown that articulation rate in Swedish child-directed speech (CDS) increases as a function of the age of the child, even when utterance length and differe…