Low-resource
Multilingual Projection for Parsing Truly Low Resource Languages
... We propose a novel approach to cross-lingual part-of-speech tagging and dependency pars- ing for truly low-resource languages. Our an- notation projection-based approach yields tag- ging and parsing models ...
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Contextualized Representations for Low resource Utterance Tagging
... In this paper, we adapt the technique of learning contextualized representations using unsupervised pretraining to learn representations for utterances in the context of the dialogue. We first introduce a general model ...
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TALEN: Tool for Annotation of Low resource ENtities
... Named entity recognition (NER), the task of find- ing and classifying named entities in text, has been well-studied in English, and a select few other lan- guages, resulting in a wealth of resources, par- ticularly ...
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Cognate Projection for Low Resource Inflection Generation
... For cognate projection, we need training sets composed of cognate pairs, Finding good par- allel bitexts for low-resource languages is quite challenging. Small bitexts exist in special do- mains, such as ...
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Generalized Data Augmentation for Low Resource Translation
... In the first thread, we focus on creating new parallel sentences through back-translation. Back- translating from the target language to the source (Sennrich et al., 2016) is a common practice in data augmentation, but ...
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Selection Criteria for Low Resource Language Programs
... of low resource ...a resource collection paradigm in which raw text is available digitally in sufficient quantity; others (Amazigh, Guarani, Maguindanao) were chosen to force the program to deal with ...
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Proceedings of the 2nd Workshop on Deep Learning Approaches for Low Resource NLP (DeepLo 2019)
... bilingually resource-poor ...many low-resource languages, and for high-resource languages it can be difficult to find linguistically annotated data of sufficient size and quality to allow ...
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Statistical Machine Translation in Low Resource Settings
... erated words (specifically, names) in a pair of lan- guages, built a module to transliterate from one lan- guage to the other, and integrated the output into an end-to-end SMT system. In my thesis, I will use this ...
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Grammatical Error Correction in Low Resource Scenarios
... Grammatical error correction in English is a long studied problem with many existing systems and datasets. However, there has been only a limited research on error correction of other languages. In this paper, we present ...
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Abstract Text Summarization: A Low Resource Challenge
... the low resource condition and is par- ticularly helpful for our multilingual scenario where availability of summarizing data is still a challenging ...
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Low Resource Response Generation with Template Prior
... the low-resource problem has been studied in tasks such as machine translation (Gu et ...to low-resource open domain response generation which is untouched by existing ...in ...
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Pushing the Limits of Low Resource Morphological Inflection
... for low-resource morphological inflection and propose several simple, yet e ff ective approaches to mitigating problems caused by extreme lack of data which, put together, improve accuracy by 15 percentage ...
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Motor Severity in Children With Cerebral Palsy Studied in a High-Resource and Low-Resource Country
... and low-resource countries, the severity and motor pat- terns of children who have CP in these 2 contexts is thought to ...and low-resource settings using the same diagnostic and classi fi ...
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Handling Syntactic Divergence in Low resource Machine Translation
... into the source language order consistently brings large performance gains, which demonstrates the importance of reordering. These results are no- table given previous reports that explicit reorder- ing is not beneficial ...
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Transfer Learning for Low Resource Neural Machine Translation
... the low-resource experiments, and Firat et ...both low-resource and high-resource lan- guages, while in our case the datasets come from vastly different domains, which makes the task ...
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Addressing Low Resource Scenarios with Character aware Embeddings
... In addition, we sampled two (sub)corpora with 10 and 100 million characters to evaluate the mod- els’ effectiveness on limited training data. To gen- erate a subcorpus of a particular size, articles were sampled ...
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Low Resource Syntactic Transfer with Unsupervised Source Reordering
... We have described a cross-lingual dependency transfer method that takes into account the prob- lem of word order differences between the source and target languages. We have shown that ap- plying projection-driven ...
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Meta Learning for Low Resource Neural Machine Translation
... In this paper, we follow up on these latest ap- proaches based on multilingual NMT and propose a meta-learning algorithm for low-resource neural machine translation. We start by arguing that the recently ...
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Reevaluating Argument Component Extraction in Low Resource Settings
... for low resource tasks are necessary, above and beyond pure statistical inductive biases, if tasks such as argumentation component extraction are to achieve the same level of success as lower- level tasks ...
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Deep Learning on Low-Resource Datasets
... In comparison to supervised techniques that are trained on strong labels, there has been relatively little work on learning to perform audio event transcription using weakly labelled data. In [7,8] the authors try to ...
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