Multilingual unsupervised word alignment models and their application

Word alignment is an essential task in natural language processing because of its critical role in training statistical machine translation (SMT) models, error analysis for neural machine translation (NMT), building bilingual lexicon, and annotation transfer. In this thesis, we explore models for word alignment, how they can be extended to incorporate linguistically-motivated alignment types, and how they can be neuralized in an end-to-end fashion. In addition to these methodological developments, we apply our word alignment models to cross-lingual part-of-speech projection. First, we present a new probabilistic model for word alignment where word alignments are associated with linguistically-motivated alignment types. We propose a novel task of joint prediction of word alignment and alignment types and propose novel semi-supervised learning algorithms for this task. We also solve a sub-task of predicting the alignment type given an aligned word pair. The proposed joint generative models (alignment-type-enhanced models) significantly outperform the models without alignment types in terms of word alignment and translation quality. Next, we present an unsupervised neural Hidden Markov Model for word alignment, where emission and transition probabilities are modeled using neural networks. The model is simpler in structure, allows for seamless integration of additional context, and can be used in an end-to-end neural network. Finally, we tackle the part-of-speech tagging task for the zero-resource scenario where no part-of-speech (POS) annotated training data is available. We present a cross-lingual projection approach where neural HMM aligners are used to obtain high quality word alignments between resource-poor and resource-rich languages. Moreover, high quality neural POS taggers are used to provide annotations for the resource-rich language side of the parallel data, as well as to train a tagger on the projected data. Our experimental results on truly low-resource languages show that our methods outperform their corresponding baselines

Conditional Random Field Autoencoders for Unsupervised Structured Prediction

We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the observable data using a feature-rich conditional random field. Then a reconstruction of the input is (re)generated, conditional on the latent structure, using models for which maximum likelihood estimation has a closed-form. Our autoencoder formulation enables efficient learning without making unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate insightful connections to traditional autoencoders, posterior regularization and multi-view learning. We show competitive results with instantiations of the model for two canonical NLP tasks: part-of-speech induction and bitext word alignment, and show that training our model can be substantially more efficient than comparable feature-rich baselines

Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure

It has been established that incorporating word cluster features derived from large unlabeled corpora can significantly improve prediction of linguistic structure. While previous work has focused primarily on English, we extend these results to other languages along two dimensions. First, we show that these results hold true for a number of languages across families. Second, and more interestingly, we provide an algorithm for inducing cross-lingual clusters and we show that features derived from these clusters significantly improve the accuracy of cross-lingual structure prediction. Specifically, we show that by augmenting direct-transfer systems with cross-lingual cluster features, the relative error of delexicalized dependency parsers, trained on English treebanks and transferred to foreign languages, can be reduced by up to 13%. When applying the same method to direct transfer of named-entity recognizers, we observe relative improvements of up to 26%