Document (#40296)

Author

Gupta, P.
Banchs, R.E.
Rosso, P.

Title

Continuous space models for CLIR

Source

Information processing and management. 53(2017) no.2, S.359-370

Year

2017

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.

Content

Vgl.: http://www.sciencedirect.com/science/article/pii/S0306457316305982 [http://dx.doi.org/10.1016/j.ipm.2016.11.002].

Theme

Multilinguale Probleme

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5. Kishida, K.: Technical issues of cross-language information retrieval : a review (2005) 0.21

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