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.
Code-mixed texts are abundant, especially in social media, and poses a problem for NLP tools, which are typically trained on monolingual corpora.
We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction.
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.
We present an analysis of a number of coreference phenomena in English-Croatian human and machine translations.
We present the Uppsala system for the CoNLL 2018 Shared Task on universal dependency parsing.
We provide a comprehensive analysis of the interactions between pre-trained word embeddings, character models and POS tags in a transition-based dependency parser.
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.
We describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction.
We present the Uppsala submission to the CoNLL 2017 shared task on parsing from raw text to universal dependencies.
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.
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.
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.