When Part-of-Speech annotated data is scarce , e.g. for under-resourced languages , one can turn to cross-lingual transfer and crawled dictionaries to collect partially supervised data . We cast this problem in the framework of ambiguous learning and show how to learn an accurate history-based model . Experiments on ten languages show significant improvements over prior state of the art performance . In the past two decades , supervised Machine Learning techniques have established new performance standards for many NLP tasks . Their success however crucially depends on the availability of annotated in-domain data , a not so common situation . This means that for many application domains and/or less-resourced languages , alternative ML techniques need to be designed to accommodate unannotated or partially annotated data . Several attempts have recently been made to mitigate the lack of annotated corpora using parallel data pairing a ( source ) text in a resource-rich language with its counterpart in a less-resourced language . By transferring labels from the source to the target , it becomes possible to obtain noisy , yet useful , annotations that can be used to train a model for the target language in a weakly supervised manner . This research trend was initiated by Yarowsky et al . ( 2001 ) , who consider the transfer of POS and other syntactic information , and further developed in ( Hwa et al . , 2005 ; Ganchev et al . , 2009 ) for syntactic dependencies , in ( Padó and Lapata , 2009 ; Kozhevnikov and Titov , 2013 ; van der Plas et al . , 2014 ) for semantic role labeling and in ( Kim et al . , 2012 ) for named-entity recognition , to name a few . Assuming that labels can actually be projected across languages , these techniques face the issue of extending standard supervised techniques with partial and/or uncertain labels in the presence of alignment noise . In comparison to the early approach of Yarowsky et al . ( 2001 ) in which POS are directly transferred , subject to heuristic filtering rules , recent works consider the integration of softer constraints using expectation regularization techniques ( Wang and Manning , 2014 ) , the combination of alignment-based POS transfer with additional information sources such as dictionaries ( Li et al . , 2012 ; Täckström et al . , 2013 ) ( Section 2 ) , or even the simultaneous use of both techniques ( Ganchev and Das , 2013 ) . In this paper , we reproduce the weakly supervised setting of Täckström et al . ( 2013 ) . By recasting this setting in the framework of ambiguous learning ( Bordes et al . , 2010 ; Cour et al . , 2011 ) ( Section 3 ) , we propose an alternative learning methodology and show that it improves the state of the art performance on a large array of languages ( Section 4 ) . Our analysis of the remaining errors suggests that in cross-lingual settings , improvements of error rates can have multiple causes and should be looked at with great care ( Section 4.2 ) . All tools and resources used in this study are available at http://perso.limsi.fr/ wisniews / ambiguous . In this paper , we have presented a novel learning methodology to learn from ambiguous supervision information , and used it to train several POS taggers . Using this method , we have been able to achieve performance that surpasses the best reported results , sometimes by a wide margin . Further work will attempt to better analyse these results , which could be caused by several subtle differences between HBAL and the baseline system . Nonetheless , these experiments confirm that cross-lingual projection of annotations have the potential to help in building very efficient POS taggers with very little monolingual supervision data . Our analysis of these results also suggests that , for this task , additional gains might be more easily obtained by fixing systematic biases introduced by conflicting mappings between tags or by train / test domain mismatch than by designing more sophisticated weakly supervised learners .

Challenge: To mitigate the data scarcity problem, for POS tagging, there are some works that project labels to other lower-resource languages but it causes alignment noises.
Approach: They propose to treat the cross-lingual POS tagging problem in ambiguous learning and train several POS taggers.
Outcome: Their approach outperforms current best models sometimes by a large margin even with a little monolingual supervision.