Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition

Abstract

This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the networks for accurate text stream alignment. Experiments on Chinese-English news streams show our approach not only outperforms previous approaches on bilingual lexicon extraction from coordinated text streams but also can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining, which makes it promising to be a new paradigm for automatic language knowledge acquisition.

Training with Streaming Annotation

“…Most approaches regard streaming data processing [16,17,18] focus on methods that output labels on the data arriving in stream, while our framework consumes annotation arriving in stream.…”