Gupta, P.; Banchs, R.E.; Rosso, P.: Continuous space models for CLIR (2017)
0.01
0.009000885 = product of:
0.027002655 = sum of:
0.027002655 = product of:
0.081007965 = sum of:
0.081007965 = weight(_text_:network in 3295) [ClassicSimilarity], result of:
0.081007965 = score(doc=3295,freq=4.0), product of:
0.19402927 = queryWeight, product of:
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.043569047 = queryNorm
0.41750383 = fieldWeight in 3295, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
4.4533744 = idf(docFreq=1398, maxDocs=44218)
0.046875 = fieldNorm(doc=3295)
0.33333334 = coord(1/3)
0.33333334 = coord(1/3)
- 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.
