Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study
Table 6: Comparison to previous work. LAS on Faroese test set. Note that the first results uses predicted segmentation and tokenization whereas the rest used gold.
['Work', 'Result']
The highest scoring system in the 2018 CoNLL shared task was that of \newciterosa-marecek-2018-cuni who achieved a LAS score of 49.4 on the Faroese test set. \newcitetyers-etal-2018-multi report an LAS of 64.43 with a monolingual multi-source approach. Our implementation which uses a different parser (AllenNLP versus UDPipe) and pre-trained word embeddings achieves an LAS of 68. Our highest score of 71.51 is achieved through the combination of projecting from strong monolingual source models and then training multi-treebank POS tagging and parsing models on the outputs.