MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale (2020.emnlp-main)
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| Challenge: | a new study examines the zero-shot transfer capabilities of text matching models on a massive scale. |
| Approach: | They propose to integrate self-supervised with supervised multi-task learning on all available source domains to study the zero-shot transfer capabilities of text matching models on a massive scale. |
| Outcome: | The proposed model outperforms in-domain BERT and the previous state of the art on six benchmarks. |
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