UDPipe is a trainable pipeline which performs sentence segmentation, tokenization, POS tagging, lemmatization and dependency parsing (Straka et al., 2016) . We present a prototype for UDPipe 2.0 and evaluate it in the CoNLL 2018 UD Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, which employs three metrics for submission ranking. Out of 26 participants, the prototype placed first in the MLAS ranking, third in the LAS ranking and third in the BLEX ranking. In extrinsic parser evaluation EPE 2018, the system ranked first in the overall score. The prototype utilizes an artificial neural network with a single joint model for POS tagging, lemmatization and dependency parsing, and is trained only using the CoNLL-U training data and pretrained word embeddings, contrary to both systems surpassing the prototype in the LAS and BLEX ranking in the shared task. The open-source code of the prototype is available at http://github.com/ CoNLL-UD-2018/UDPipe-Future. After the shared task, we slightly refined the model architecture, resulting in better performance both in the intrinsic evaluation (corresponding to first, second and second rank in MLAS, LAS and BLEX shared task metrics) and the extrinsic evaluation. The improved models will be available shortly in UDPipe at
REF was one of the winning systems of the 2018 shared task, performing the POS tagging, lemmatization and dependency parsing jointly.
UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task
CoNLL Shared Task
Computer Science