Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval (D19-61)
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| Challenge: | Low resource languages often lack relevance annotations for cross-lingual information retrieval . when available, the training data has limited coverage for possible queries . |
| Approach: | They propose a weakly supervised neural model for Cross-lingual information retrieval from low-resource languages using weak supervision instead of relevance annotations. |
| Outcome: | The proposed model achieves 19 MAP points improvement compared to CNNs and 12 points improvement from machine translation-based CLIR models. |
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