Frustratingly Easy Cross-Lingual Transfer for Transition-Based Dependency Parsing

Abstract

In this paper, we present a straightforward strategy for transferring dependency parsers across languages. The proposed method learns a parser from partially annotated data obtained through the projection of annotations across unambiguous word alignments. It does not rely on any modeling of the reliability of dependency and/or alignment links and is therefore easy to implement and parameter free. Experiments on six languages show that our method is at par with recent algorithmically demanding methods, at a much cheaper computational cost. It can thus serve as a fair baseline for transferring dependencies across languages with the use of parallel corpora.

Cross-lingual Dependency Transfer : What Matters? Assessing the Impact of Pre- and Post-processing

“…Nonetheless, to avoid this problem several works have proposed ad hoc rules to complete the trees. For instance, Spreyer and Kuhn (2009) propose to attach unaligned tokens to a fake root in order to ignore the actions associated with these dependencies during learning; Li et al (2014) choose to add all dependency possibilities of the unaligned tokens to preserve ambiguity during learning; Ma and Xia (2014) consider, the potentially noisy, dependencies predicted by a delexicalized parser. Applying these heuristics allows, at the expense of adding potentially fake tokens or noisy dependencies, to label automatically a corpus of full parsing trees in the target language on which a standard learning method can be used.…”

Multi-source annotation projection of coreference chains: assessing strategies and testing opportunities

“…There have been some influential work on annotation projection for different NLP tasks which performed quite well cross-lingually, e.g. for semantic role labelling (Akbik et al, 2015) or syntactic parsing (Lacroix et al, 2016). At the same time, several recent studies on annotation projection for coreference have proven it to be a more difficult task than POS tagging or syntactic parsing, which is hard to be tackled by projection algorithms.…”

Cross-Lingual Syntactic Transfer with Limited Resources

“…The rows are sorted by frequency in the G1 languages. Lacroix et al, 2016;Schlichtkrull and Søgaard, 2017). Other recent work (Tiedemann et al, 2014;Tiedemann, 2015;Tiedemann and Agić, 2016) has considered treebank translation, where a statistical machine translation system (e.g., MOSES (Koehn et al, 2007)) is used to translate a source language treebank into the target language, complete with reordering of the input sentence.…”