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.
To summarize, for all these mentioned tasks, lexical resources are valuable sources of knowledge, but are also costly to build. Language relatedness information is applicable for closely related languages, but it is often the case that a given low-resource language does not have a closely-related, resource-rich language. Parallel data therefore appears to be the most realistic additional source of information for developing NLP systems for low-resource languages @cite_2 @cite_14 @cite_15 , and here we primarily investigate methods to exploit parallel texts.
@cite_15 @cite_14 @cite_2
2140497009 1574126082 2135890475 2125280523
In this paper we address the problem of multilingual part-of-speech tagging for resource-poor languages. We use parallel data to transfer part-of-speech information from resource-rich to resourcepoor languages. Additionally, we use a small amount of annotated data to learn to “correct” errors from projected approach such as tagset mismatch between languages, achieving state-of-the-art performance (91.3 ) across 8 languages. Our approach is based on modest data requirements, and uses minimum divergence classification. For situations where no universal tagset mapping is available, we propose an alternate method, resulting in state-of-the-art 85.6 accuracy on the resource-poor language Malagasy. Despite significant recent work, purely unsupervised techniques for part-of-speech (POS) tagging have not achieved useful accuracies required by many language processing tasks. Use of parallel text between resource-rich and resource-poor languages is one source of weak supervision that significantly improves accuracy. However, parallel text is not always available and techniques for using it require multiple complex algorithmic steps. In this paper we show that we can build POS-taggers exceeding state-of-the-art bilingual methods by using simple hidden Markov models and a freely available and naturally growing resource, the Wiktionary. Across eight languages for which we have labeled data to evaluate results, we achieve accuracy that significantly exceeds best unsupervised and parallel text methods. We achieve highest accuracy reported for several languages and show that our approach yields better out-of-domain taggers than those trained using fully supervised Penn Treebank. While significant effort has been put into annotating linguistic resources for several languages, there are still many left that have only small amounts of such resources. This paper investigates a method of propagating information (specifically mention detection information) into such low resource languages from richer ones. Experiments run on three language pairs (Arabic-English, Chinese-English, and Spanish-English) show that one can achieve relatively decent performance by propagating information from a language with richer resources such as English into a foreign language alone (no resources or models in the foreign language). Furthermore, while examining the performance using various degrees of linguistic information in a statistical framework, results show that propagated features from English help improve the source-language system performance even when used in conjunction with all feature types built from the source language. The experiments also show that using propagated features in conjunction with lexically-derived features only (as can be obtained directly from a mention annotated corpus) yields similar performance to using feature types derived from many linguistic resources. This paper explores the potential for annotating and enriching data for low-density languages via the alignment and projection of syntactic structure from parsed data for resource-rich languages such as English. We seek to develop enriched resources for a large number of the world’s languages, most of which have no significant digital presence. We do this by tapping the body of Web-based linguistic data, most of which exists in small, analyzed chunks embedded in scholarly papers, journal articles, Web pages, and other online documents. By harvesting and enriching these data, we can provide the means for knowledge discovery across the resulting corpus that can lead to building computational resources such as grammars and transfer rules, which, in turn, can be used as bootstraps for building additional tools and resources for the languages represented. 1