The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a realworld setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe data preparation, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.
Apertium is a free/open-source platform for rule-based machine translation. It is being widely used to build machine translation systems for a variety of language pairs, especially in those cases (mainly with related-language pairs) where shallow transfer suffices to produce good quality translations, although it has also proven useful in assimilation scenarios with more distant pairs involved. This article summarises the Apertium platform: the translation engine, the encoding of linguistic data, and the tools developed around the platform. The present limitations of the platform and the challenges posed for the coming years are also discussed. Finally, evaluation results for some of the most active language pairs are presented. An appendix describes Apertium as a free/open-source project.
Universal dependencies (UD) is a framework for morphosyntactic annotation of human language, which to date has been used to create treebanks for more than 100 languages. In this article, we outline the linguistic theory of the UD framework, which draws on a long tradition of typologically oriented grammatical theories. Grammatical relations between words are centrally used to explain how predicate–argument structures are encoded morphosyntactically in different languages while morphological features and part-of-speech classes give the properties of words. We argue that this theory is a good basis for cross-linguistically consistent annotation of typologically diverse languages in a way that supports computational natural language understanding as well as broader linguistic studies.
Sequential neural networks models are powerful tools in a variety of Natural Language Processing (NLP) tasks. The sequential nature of these models raises the questions: to what extent can these models implicitly learn hierarchical structures typical to human language, and what kind of grammatical phenomena can they acquire? We focus on the task of agreement prediction in Basque, as a case study for a task that requires implicit understanding of sentence structure and the acquisition of a complex but consistent morphological system. Analyzing experimental results from two syntactic prediction tasks-verb number prediction and suffix recovery-we find that sequential models perform worse on agreement prediction in Basque than one might expect on the basis of a previous agreement prediction work in English. Tentative findings based on diagnostic classifiers suggest the network makes use of local heuristics as a proxy for the hierarchical structure of the sentence. We propose the Basque agreement prediction task as challenging benchmark for models that attempt to learn regularities in human language.
This paper describes a method of creating synthetic treebanks for cross-lingual dependency parsing using a combination of machine translation (including pivot translation), annotation projection and the spanning tree algorithm. Sentences are first automatically translated from a lesser-resourced language to a number of related highly-resourced languages, parsed and then the annotations are projected back to the lesser-resourced language, leading to multiple trees for each sentence from the lesser-resourced language. The final treebank is created by merging the possible trees into a graph and running the spanning tree algorithm to vote for the best tree for each sentence. We present experiments aimed at parsing Faroese using a combination of Danish, Swedish and Norwegian. In a similar experimental setup to the CoNLL 2018 shared task on dependency parsing we report state-of-the-art results on dependency parsing for Faroese using an off-theshelf parser.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.