Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations

@inproceedings{Zhang2019ImprovingLC,
  title={Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations},
  author={Rui Zhang and Caitlin Westerfield and Sungrok Shim and G. Bingham and Alexander R. Fabbri and Neha Verma and William Hu and Dragomir R. Radev},
  booktitle={ACL},
  year={2019}
}
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… Expand
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