Czech

This paper considers statistical parsing of Czech, which differs radically from English in at least two respects: (1) it is a highly inflected language, and (2) it has relatively free …...

We present an effective training al- gorithm for linearly-scored dependency parsers that implements online large- margin multi-class training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient …...

Most current dependency parsers presuppose that input words have been morphologically disambiguated using a part-of-speech tagger before parsing begins. We present a transition- based system for joint part-of-speech tagging and …...

We propose a technique to generate non- projective word orders in an efficient statisti- cal linearization system. Our approach pre- dicts liftings of edges in an unordered syntac- tic tree …...

AbstractAbstract Meaning Representations (AMRs) are rooted, directional and labeled graphs that abstract away from morpho-syntactic idiosyncrasies such as word category (verbs and nouns), word order, and function words (determiners, some …...

AbstractWe release a massive expansion of the paraphrase database (PPDB) that now includes a collection of paraphrases in 23 different languages. The resource is derived from large volumes of bilingual …...

As part of the SemEval-2015 shared task on Broad-Coverage Semantic Dependency Pars- ing, we evaluate the performace of our last year’s system (TurboSemanticParser) on mul- tiple languages and out-of-domain data. …...

AbstractIn the present paper, we analyse variation of discourse phenomena in two typologically different languages, i.e. in German and Czech. The novelty of our approach lies in the nature of …...

AbstractConventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in …...

While recent changes in Machine Trans- lation state-of-the-art brought translation quality a step further, it is regularly ac- knowledged that the standard automatic metrics do not provide enough insights to …...

Neural machine translation (NMT) models are conventionally trained with fixed-size vocabu- laries to control the computational complexity and the quality of the learned word represen- tations. This, however, limits the …...

AbstractNeural dependency parsing models that compose word representations from characters can presumably exploit morphosyntax when making attachment decisions. How much do they know about morphology? We investigate how well they …...

AbstractRecent work has shown that neural rerankers can improve results for dependency parsing over the top k trees produced by a base parser. However, all neural rerankers so far have …...

AbstractWe present a richly annotated and genre-diversified language resource, the Prague Dependency Treebank-Consolidated 1.0 (PDT-C 1.0), the purpose of which is - as it always been the case for the …...

AbstractIn this paper, we introduce UniSent universal sentiment lexica for 1000+ languages. Sentiment lexica are vital for sentiment analysis in absence of document-level annotations, a very common scenario for low-resource …...