1 code implementation • EMNLP (nlpbt) 2020 • Giuseppe Della Corte, Sara Stymne
We discuss a set of methods for the creation of IESTAC: a English-Italian speech and text parallel corpus designed for the training of end-to-end speech-to-text machine translation models and publicly released as part of this work.
2 code implementations • PoliticalNLP (LREC) 2022 • Luise Dürlich, Sebastian Reimann, Gustav Finnveden, Joakim Nivre, Sara Stymne
Causality detection is the task of extracting information about causal relations from text.
no code implementations • EACL (VarDial) 2021 • Harm Lameris, Sara Stymne
We find that training on a very small amount of Scots data was superior to zero-shot transfer from English.
no code implementations • SemEval (NAACL) 2022 • Rafal Cerniavski, Sara Stymne
In an additional experiment, using resources beyond the shared task, we use the training data in Russian and French to improve the English reverse dictionary using unsupervised embeddings alignment and machine translation.
no code implementations • LREC 2022 • Sara Stymne, Carin Östman
The main focus of the SLäNDa corpus is to distinguish between direct speech and the main narrative.
no code implementations • ACL (unimplicit) 2021 • Ahmed Ruby, Christian Hardmeier, Sara Stymne
Exploring aspects of sentential meaning that are implicit or underspecified in context is important for sentence understanding.
no code implementations • COLING (MWE) 2020 • Carlos Ramisch, Agata Savary, Bruno Guillaume, Jakub Waszczuk, Marie Candito, Ashwini Vaidya, Verginica Barbu Mititelu, Archna Bhatia, Uxoa Iñurrieta, Voula Giouli, Tunga Güngör, Menghan Jiang, Timm Lichte, Chaya Liebeskind, Johanna Monti, Renata Ramisch, Sara Stymne, Abigail Walsh, Hongzhi Xu
We present edition 1. 2 of the PARSEME shared task on identification of verbal multiword expressions (VMWEs).
no code implementations • NoDaLiDa 2021 • Antonia Karamolegkou, Sara Stymne
However, when parsing Latin, it has been suggested that languages such as ancient Greek could be helpful.
no code implementations • SEMEVAL 2021 • Huiling You, Xingran Zhu, Sara Stymne
XLMR performs better than mBERT in the cross-lingual setting both with fine-tuning and feature extraction, whereas these two models give a similar performance in the multilingual setting.
no code implementations • LREC 2020 • Sara Stymne, Carin {\"O}stman
We describe a new corpus, SL{\"a}NDa, the Swedish Literary corpus of Narrative and Dialogue.
no code implementations • LREC 2020 • Arra{'}Di Nur Rizal, Sara Stymne
Code-mixed texts are abundant, especially in social media, and poses a problem for NLP tools, which are typically trained on monolingual corpora.
no code implementations • WS 2016 • Liane Guillou, Christian Hardmeier, Preslav Nakov, Sara Stymne, Jörg Tiedemann, Yannick Versley, Mauro Cettolo, Bonnie Webber, Andrei Popescu-Belis
We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction.
1 code implementation • CL (ACL) 2020 • Miryam de Lhoneux, Sara Stymne, Joakim Nivre
We find that the parser learns different information about AVCs and FMVs if only sequential models (BiLSTMs) are used in the architecture but similar information when a recursive layer is used.
no code implementations • WS 2018 • Margita {\v{S}}o{\v{s}}tari{\'c}, Christian Hardmeier, Sara Stymne
We present an analysis of a number of coreference phenomena in English-Croatian human and machine translations.
no code implementations • CONLL 2018 • Aaron Smith, Bernd Bohnet, Miryam de Lhoneux, Joakim Nivre, Yan Shao, Sara Stymne
We present the Uppsala system for the CoNLL 2018 Shared Task on universal dependency parsing.
no code implementations • EMNLP 2018 • Aaron Smith, Miryam de Lhoneux, Sara Stymne, Joakim Nivre
We provide a comprehensive analysis of the interactions between pre-trained word embeddings, character models and POS tags in a transition-based dependency parser.
1 code implementation • ACL 2018 • Sara Stymne, Miryam de Lhoneux, Aaron Smith, Joakim Nivre
How to make the most of multiple heterogeneous treebanks when training a monolingual dependency parser is an open question.
no code implementations • WS 2017 • Sara Stymne, Sharid Lo{\'a}iciga, Fabienne Cap
We describe the Uppsala system for the 2017 DiscoMT shared task on cross-lingual pronoun prediction.
1 code implementation • WS 2017 • Miryam de Lhoneux, Sara Stymne, Joakim Nivre
In this paper, we extend the arc-hybrid system for transition-based parsing with a swap transition that enables reordering of the words and construction of non-projective trees.
no code implementations • WS 2017 • Sharid Lo{\'a}iciga, Sara Stymne, Preslav Nakov, Christian Hardmeier, J{\"o}rg Tiedemann, Mauro Cettolo, Yannick Versley
We describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction.
no code implementations • CONLL 2017 • Miryam de Lhoneux, Yan Shao, Ali Basirat, Eliyahu Kiperwasser, Sara Stymne, Yoav Goldberg, Joakim Nivre
We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies.
no code implementations • LREC 2012 • Sara Stymne, Lars Ahrenberg
Error analysis is a means to assess machine translation output in qualitative terms, which can be used as a basis for the generation of error profiles for different systems.
no code implementations • LREC 2012 • Sara Stymne, Henrik Danielsson, Sofia Bremin, Hongzhan Hu, Johanna Karlsson, Anna Prytz Lillkull, Martin Wester
We present a preliminary study where we use eye tracking as a complement to machine translation (MT) error analysis, the task of identifying and classifying MT errors.
no code implementations • LREC 2012 • Maria Holmqvist, Sara Stymne, Lars Ahrenberg, Magnus Merkel
The reordered text is used to create a second word alignment which can be an improvement of the first alignment, since the word order is more similar.