text: Cross lingual projection of linguistic annotation suffers from many sources of bias and noise, leading to unreliable annotations that cannot be used directly. In this paper, we introduce a novel approach to sequence tagging that learns to correct the errors from cross-lingual projection using an explicit debiasing layer. This is framed as joint learning over two corpora, one tagged with gold standard and the other with projected tags. We evaluated with only 1,000 tokens tagged with gold standard tags, along with more plentiful parallel data. Our system equals or exceeds the state-of-the-art on eight simulated low-resource settings, as well as two real low-resource languages, Malagasy and Kinyarwanda.
  @cite_18: We present a simple method for transferring dependency parsers from source languages with labeled training data to target languages without labeled training data. We first demonstrate that delexicalized parsers can be directly transferred between languages, producing significantly higher accuracies than unsupervised parsers. We then use a constraint driven learning algorithm where constraints are drawn from parallel corpora to project the final parser. Unlike previous work on projecting syntactic resources, we show that simple methods for introducing multiple source languages can significantly improve the overall quality of the resulting parsers. The projected parsers from our system result in state-of-the-art performance when compared to previously studied unsupervised and projected parsing systems across eight different languages.
  @cite_8: In this paper, we describe a resource-light system for the automatic morphological analysis and tagging of Russian. We eschew the use of extensive resources (particularly, large annotated corpora and lexicons), exploiting instead (i) pre-existing annotated corpora of Czech; (ii) an unannotated corpus of Russian. We show that our approach has benefits, and present what we believe to be one of the first full evaluations of a Russian tagger in the openly available literature.
  @cite_0: We present a new collection of treebanks with homogeneous syntactic dependency annotation for six languages: German, English, Swedish, Spanish, French and Korean. To show the usefulness of such a resource, we present a case study of crosslingual transfer parsing with more reliable evaluation than has been possible before. This ‘universal’ treebank is made freely available in order to facilitate research on multilingual dependency parsing. 1
  @cite_2: 
  @cite_13: We describe a novel approach for inducing unsupervised part-of-speech taggers for languages that have no labeled training data, but have translated text in a resource-rich language. Our method does not assume any knowledge about the target language (in particular no tagging dictionary is assumed), making it applicable to a wide array of resource-poor languages. We use graph-based label propagation for cross-lingual knowledge transfer and use the projected labels as features in an unsupervised model (Berg-, 2010). Across eight European languages, our approach results in an average absolute improvement of 10.4 over a state-of-the-art baseline, and 16.7 over vanilla hidden Markov models induced with the Expectation Maximization algorithm.
  @cite_20: We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages. Past approaches project labels across bitext and use them as features or gold labels for training. We propose a new method that projects model expectations rather than labels, which facilities transfer of model uncertainty across language boundaries. We encode expectations as constraints and train a discriminative CRF model using Generalized Expectation Criteria (Mann and McCallum, 2010). Evaluated on standard Chinese-English and German-English NER datasets, our method demonstrates F 1 scores of 64 and 60 when no labeled data is used. Attaining the same accuracy with supervised CRFs requires 12k and 1.5k labeled sentences. Furthermore, when combined with labeled examples, our method yields significant improvements over state-of-the-art supervised methods, achieving best reported numbers to date on Chinese OntoNotes and German CoNLL-03 datasets.
summary: Parallel bilingual corpora provide another important source of information. These corpora are often plentiful even for many low-resource languages in the form of multilingual government documents, book translations, multilingual websites, etc. Word alignments can provide a bridge to project information from a resource-rich source language to a resource-poor target language. For example, parallel data has been used for named entity recognition @cite_20 based on the observation that named entities are most often preserved in translation and also in syntactic tasks such as POS tagging @cite_2 @cite_13 and dependency parsing @cite_0 . Clues from related languages can also compensate for the lack of annotated data, as we expect there to be information shared between closely related languages in terms of the lexical items, morphology and syntactic structure. Some successful applications using language relatedness information are dependency parsing @cite_18 and POS tagging @cite_8 . However, these approaches are limited to closely related languages such as Czech and Russian, or Telugu and Kannada, and it is unclear whether these techniques will work well in situations where parallel data only exists for less-related languages, as is often the case in practice.