Continuous space models for CLIR. Issue 2 (March 2017)

• Record Type: Journal Article
• Authors: Gupta, Parth; Banchs, Rafael E.; Rosso, Paolo
• Is Part Of: Information processing & management. Volume 53:Issue 2(2017:Mar.)
• Page Start: 359
• Page End: 370
• Publication Date: 2017-03
• Subjects: Cross-language information retrieval -- Latens space models; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2016.11.002
• Languages: English
• ISSNs: 0306-4573

Abstract

Abstract: We present and evaluate a novel technique for learning cross-lingual continuous space models to aid cross-language information retrieval (CLIR). Our model, which is referred to as external-data composition neural network (XCNN), is based on a composition function that is implemented on top of a deep neural network that provides a distributed learning framework. Different from most existing models, which rely only on available parallel data for training, our learning framework provides a natural way to exploit monolingual data and its associated relevance metadata for learning continuous space representations of language. Cross-language extensions of the obtained models can then be trained by using a small set of parallel data. This property is very helpful for resource-poor languages, therefore, we carry out experiments on the English-Hindi language pair. On the conducted comparative evaluation, the proposed model is shown to outperform state-of-the-art continuous space models with statistically significant margin on two different tasks: parallel sentence retrieval and ad-hoc retrieval.