Unsupervised Bilingual POS Tagging with Markov Random Fields

In this paper, we give a treatment to the problem of bilingual part-of-speech induction with parallel data. We demonstrate that naïve optimization of log-likelihood with joint MRFs suffers from a severe problem of local maxima, and suggest an alternative – using contrastive estimation for estimation of the parameters. Our experiments show that estimating the parameters this way, using overlapping features with joint MRFs performs better than previous work on the 1984 dataset.

Exploiting Cross-Lingual Representations For Natural Language Processing

Traditional approaches to supervised learning require a generous amount of labeled data for good generalization. While such annotation-heavy approaches have proven useful for some Natural Language Processing (NLP) tasks in high-resource languages (like English), they are unlikely to scale to languages where collecting labeled data is di cult and time-consuming. Translating supervision available in English is also not a viable solution, because developing a good machine translation system requires expensive to annotate resources which are not available for most languages. In this thesis, I argue that cross-lingual representations are an effective means of extending NLP tools to languages beyond English without resorting to generous amounts of annotated data or expensive machine translation. These representations can be learned in an inexpensive manner, often from signals completely unrelated to the task of interest. I begin with a review of different ways of inducing such representations using a variety of cross-lingual signals and study algorithmic approaches of using them in a diverse set of downstream tasks. Examples of such tasks covered in this thesis include learning representations to transfer a trained model across languages for document classification, assist in monolingual lexical semantics like word sense induction, identify asymmetric lexical relationships like hypernymy between words in different languages, or combining supervision across languages through a shared feature space for cross-lingual entity linking. In all these applications, the representations make information expressed in other languages available in English, while requiring minimal additional supervision in the language of interest

Towards zero-shot cross-lingual named entity disambiguation

[EN]In cross-Lingual Named Entity Disambiguation (XNED) the task is to link Named Entity mentions in text in some native language to English entities in a knowledge graph. XNED systems usually require training data for each native language, limiting their application for low resource languages with small amounts of training data. Prior work have proposed so-called zero-shot transfer systems which are only trained in English training data, but required native prior probabilities of entities with respect to mentions, which had to be estimated from native training examples, limiting their practical interest. In this work we present a zero-shot XNED architecture where, instead of a single disambiguation model, we have a model for each possible mention string, thus eliminating the need for native prior probabilities. Our system improves over prior work in XNED datasets in Spanish and Chinese by 32 and 27 points, and matches the systems which do require native prior information. We experiment with different multilingual transfer strategies, showing that better results are obtained with a purpose-built multilingual pre-training method compared to state-of-the-art generic multilingual models such as XLM-R. We also discovered, surprisingly, that English is not necessarily the most effective zero-shot training language for XNED into English. For instance, Spanish is more effective when training a zero-shot XNED system that dis-ambiguates Basque mentions with respect to an English knowledge graph.This work has been partially funded by the Basque Government (IXA excellence research group (IT1343-19) and DeepText project), Project BigKnowledge (Ayudas Fundacion BBVA a equipos de investigacion cientifica 2018) and via the IARPA BETTER Program contract 2019-19051600006 (ODNI, IARPA activity). Ander Barrena enjoys a post-doctoral grant ESPDOC18/101 from the UPV/EHU and also acknowledges the support of the NVIDIA Corporation with the donation of a Titan V GPU used for this research. The author thankfully acknowledges the computer resources at CTE-Power9 + V100 and technical support provided by Barcelona Supercomputing Center (RES-IM-2020-1-0020)