Local context selection for aligning sentences in parallel corpora

Abstract

This paper presents a novel language-independent context-based sentence alignment technique given parallel corpora. We can view the problem of aligning sentences as finding translations of sentences chosen from different sources. Unlike current approaches which rely on pre-defined features and models, our algorithm employs features derived from the distributional proper-ties of sentences and does not use any language dependent knowledge. We make use of the context of sentences and introduce the notion of Zipfian word vectors which effectively models the distributional properties of a given sentence. We accept the context to be the frame in which the reasoning about sentence alignment is done. We examine alternatives for local context models and demonstrate that our context based sentence alignment algorithm per-forms better than prominent sentence alignment techniques. Our system dynamically selects the local context for a pair of set of sentences which maximizes the correlation. We evaluate the performance of our system based on two different measures: sentence alignment accuracy and sentence alignment coverage. We compare the performance of our system with commonly used sentence alignment systems and show that our system performs 1.1951 to 1.5404 times better in reducing the error rate in alignment accuracy and coverage.