Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Low-Resource Cross-lingual Document Retrieval by Reranking
  with Deep Bilingual Representations   In this paper, we propose to boost low-resource cross-lingual document
retrieval performance with deep bilingual query-document representations. We
match queries and documents in both source and target languages with four
components, each of which is implemented as a term interaction-based deep
neural network with cross-lingual word embeddings as input. By including query
likelihood scores as extra features, our model effectively learns to rerank the
retrieved documents by using a small number of relevance labels for
low-resource language pairs. Due to the shared cross-lingual word embedding
space, the model can also be directly applied to another language pair without
any training label. Experimental results on the MATERIAL dataset show that our
model outperforms the competitive translation-based baselines on
English-Swahili, English-Tagalog, and English-Somali cross-lingual information
retrieval tasks.

26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex