Content-Based Weak Supervision for Ad-Hoc Re-Ranking

1 Jul 2017 Sean MacAvaney Andrew Yates Kai Hui Ophir Frieder

One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that already exhibit relevance (e.g., newswire headline-content pairs and encyclopedic heading-paragraph pairs)... (read more)

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