Training Effective Neural CLIR by Bridging the Translation Gap

Author:

Bonab Hamed1,Sarwar Sheikh Muhammad1,Allan James1

Affiliation:

1. University of Massachusetts Amherst, Amherst, MA, USA

Funder

IARPA, subcontract: USC (University of Southern California)

Air Force Research Laboratory (AFRL) and IARPA, subcontract: Raytheon BBN Technologies Corporation

Publisher

ACM

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1. Soft Prompt Decoding for Multilingual Dense Retrieval;Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval;2023-07-18

2. Detecting Cross-Lingual Information Gaps in Wikipedia;Companion Proceedings of the ACM Web Conference 2023;2023-04-30

3. Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation;Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining;2023-02-27

4. A Novel Cross Language Neural Retrieval Model;2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA);2022-10-28

5. Neural topic-enhanced cross-lingual word embeddings for CLIR;Information Sciences;2022-08

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