Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Stymne, Sara"'
We describe the Uppsala NLP submission to SemEval-2021 Task 2 on multilingual and cross-lingual word-in-context disambiguation. We explore the usefulness of three pre-trained multilingual language models, XLM-RoBERTa (XLMR), Multilingual BERT (mBERT)
Externí odkaz:
http://arxiv.org/abs/2104.03767
Autor:
Guillou, Liane, Hardmeier, Christian, Nakov, Preslav, Stymne, Sara, Tiedemann, Jörg, Versley, Yannick, Cettolo, Mauro, Webber, Bonnie, Popescu-Belis, Andrei
Publikováno v:
WMT-2016
We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provide predictions on what pronoun class label should rep
Externí odkaz:
http://arxiv.org/abs/1911.12091
There is a growing interest in investigating what neural NLP models learn about language. A prominent open question is the question of whether or not it is necessary to model hierarchical structure. We present a linguistic investigation of a neural p
Externí odkaz:
http://arxiv.org/abs/1907.07950
We present the Uppsala system for the CoNLL 2018 Shared Task on universal dependency parsing. Our system is a pipeline consisting of three components: the first performs joint word and sentence segmentation; the second predicts part-of- speech tags a
Externí odkaz:
http://arxiv.org/abs/1809.02237
We provide a comprehensive analysis of the interactions between pre-trained word embeddings, character models and POS tags in a transition-based dependency parser. While previous studies have shown POS information to be less important in the presence
Externí odkaz:
http://arxiv.org/abs/1808.09060
How to make the most of multiple heterogeneous treebanks when training a monolingual dependency parser is an open question. We start by investigating previously suggested, but little evaluated, strategies for exploiting multiple treebanks based on co
Externí odkaz:
http://arxiv.org/abs/1805.05089
Autor:
Stymne, Sara
In this thesis I aim to improve phrase-based statistical machine translation (PBSMT) in a number of ways by the use of text harmonization strategies. PBSMT systems are built by training statistical models on large corpora of human translations. This
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-76766
Autor:
Stymne, Sara
In this thesis I explore how compound processing can be used to improve phrase-based statistical machine translation (PBSMT) between English and German/Swedish. Both German and Swedish generally use closed compounds, which are written as one word wit
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51416
Autor:
Stymne, Sara
In this thesis I have investigated verb frame divergences in a bilingual Head-driven Phrase Structure Grammar for machine translation. The purpose was threefold: (1) to describe and classify verb frame divergences (VFDs) between Swedish and English,
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6708