Cross-Lingual Syntactic Transfer with Limited Resources
Table 7: Comparison of our work using Bible and Europarl data, with previous work: MX14 [Ma and Xia2014], LA16 [Lacroix et al.2016], ZB15 [Zhang and Barzilay2015], GCY16 [Guo et al.2016], AMB 16 [Ammar et al.2016], and RC15 [Rasooli and Collins2015]. “Supervised” refers to the performance of the parser trained on fully gold standard data in a supervised fashion (i.e. the practical upper-bound of our model). “avg\textbackslash en” refers to the average accuracy for all datasets except English.
['Lang.', 'MX14', 'LA16', 'ZB15', 'ZB15', 'GCY16', 'GCY16', 'AMB16', 'AMB16', 'RC15', 'RC15', 'This paper Bible', 'This paper Bible', 'This paper Europarl', 'This paper Europarl', 'Supervised', 'Supervised']
1) \newcitema -xia:2014: P14-1, who describe an annotation projection method based on entropy regularization ; 2) \newcitelacroix-EtAl:2016:N16-1, who describe an annotation projection method based on training on partial trees with dynamic oracles; 3) \newciteyuanregina15, who describe a method that learns cross-lingual embeddings and bilingual dictionaries from Europarl data, and uses these features in a discriminative parsing model; 4) \newciteguo2016representation, who describe a method that learns cross-lingual embeddings from Europarl data and uses a shift-reduce neural parser with these representations; 5) \newciteammar2016one, who use the same embeddings as \newciteguo2016representation, within an LSTM-based parser; and 6) \newciterasooli -collins:2015: EMNLP who use the density-driven approach on the Europarl data. Our method gives significant improvements over the first three models, in spite of using the Bible translation data rather than Europarl. When using the Europarl data, our method improves the state-of-the-art model of \newciterasooli-collins:2015:EMNLP. There has recently been a great deal of work on syntactic transfer. These cross-lingual representations are usually learned from parallel translation data.