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.
For most natural language processing tasks, the conventional approach to developing a system is to use supervised learning algorithms trained on a set of annotated data. However, this approach is inappropriate for low-resource languages due to the lack of annotated data. An alternative approach is to harness different source of information aside from annotated text. Knowledge-bases such as dictionaries are one such source, which can be used to inform or constrain models, such as limiting the search space for POS tagging @cite_7 @cite_12 @cite_23 .
@cite_23 @cite_12 @cite_7
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Developing natural language processing tools for low-resource languages often requires creating resources from scratch. While a variety of semi-supervised methods exist for training from incomplete data, there are open questions regarding what types of training data should be used and how much is necessary. We discuss a series of experiments designed to shed light on such questions in the context of part-of-speech tagging. We obtain timed annotations from linguists for the low-resource languages Kinyarwanda and Malagasy (as well as English) and evaluate how the amounts of various kinds of data affect performance of a trained POS-tagger. Our results show that annotation of word types is the most important, provided a sufficiently capable semi-supervised learning infrastructure is in place to project type information onto a raw corpus. We also show that finitestate morphological analyzers are effective sources of type information when few labeled examples are available. The lack of annotated data is an obstacle to the development of many natural language processing applications; the problem is especially severe when the data is non-English. Previous studies suggested the possibility of acquiring resources for non-English languages by bootstrapping from high quality English NLP tools and parallel corpora; however, the success of these approaches seems limited for dissimilar language pairs. In this paper, we propose a novel approach of combining a bootstrapped resource with a small amount of manually annotated data. We compare the proposed approach with other bootstrapping methods in the context of training a Chinese Part-of-Speech tagger. Experimental results show that our proposed approach achieves a significant improvement over EM and self-training and systems that are only trained on manual annotations. In natural language acquisition, it is dicult to gather the annotated data needed for supervised learning; however, unannotated data is fairly plentiful. Active learning methods attempt to select for annotation and training only the most informative examples, and therefore are potentially very useful in natural language applications. However, existing results for active learning have only considered standard classication tasks. To reduce annotation eort while maintaining accuracy, we apply active learning to two non-classication tasks in natural language processing: semantic parsing and information extraction. We show that active learning can signicantly reduce the number of annotated examples required to achieve a given level of performance for these complex tasks. Most semi-supervised methods in Natural Language Processing capitalize on unannotated resources in a single language; however, information can be gained from using parallel resources in more than one language, since translations of the same utterance in different languages can help to disambiguate each other. We demonstrate a method that makes effective use of vast amounts of bilingual text (a.k.a. bitext) to improve monolingual systems. We propose a factored probabilistic sequence model that encourages both cross-language and intra-document consistency. A simple Gibbs sampling algorithm is introduced for performing approximate inference. Experiments on English-Chinese Named Entity Recognition (NER) using the OntoNotes dataset demonstrate that our method is significantly more accurate than state-of-the-art monolingual CRF models in a bilingual test setting. Our model also improves on previous work by (2010), achieving a relative error reduction of 10.8 and 4.5 in Chinese and English, respectively. Furthermore, by annotating a moderate amount of unlabeled bi-text with our bilingual model, and using the tagged data for uptraining, we achieve a 9.2 error reduction in Chinese over the state-of-the-art Stanford monolingual NER system.