The language barrier in many multilingual natural language processing (NLP) tasks can be overcome by mapping objects from different languages (“views”) into a common low-dimensional subspace. For example, the name transliteration task involves mapping bilingual names and word translation mining involves mapping bilingual words into a common low-dimensional subspace. Multi-view models learn such a low-dimensional subspace using a training corpus of paired objects, e.g., names written in different languages, represented as feature vectors.

The central idea of my dissertation is to learn low-dimensional subspaces (or interlingual representations) that are effective for various multilingual and monolingual NLP tasks. First, I demonstrate the effectiveness of interlingual representations in mining bilingual word translations, and then proceed to developing models for diverse situations that often arise in NLP tasks. In particular, I design models for the following problem settings: 1) when there are more than two views but we only have training data from a single pivot view into each of the remaining views 2) when an object from one view is associated with a ranked list of objects from another view, and finally 3) when the underlying objects have rich structure, such as a tree.

These problem settings arise often in real world applications. I choose a canonical task for each of the settings and compare my model with existing state-of-the-art baseline systems. I provide empirical evidence for the first two models on multilingual name transliteration and reranking for the part-of-speech tagging tasks, espectively. For the third problem setting, I experiment with the task of re-scoring target language word translations based on the source word's context. The model roposed for this problem builds on the ideas proposed in the previous models and, hence, leads to a natural conclusion.