- Zhuyun Dai, Jamie Callan. 2019. An Evaluation of Weakly-Supervised DeepCT in the TREC 2019 Deep Learning Track. Abstract: This paper describes our participation in the TREC 2019 Deep Learning Track document ranking task. We developed a deep learning based document term weighting approach based on our previous work of DeepCT. It used the contextualized token embeddings generated by BERT to estimate a term’s importance in passages, and combines passage term weights into document-level term weights. The weighted document is stored in an ordinary inverted index and searched using a multi-ﬁeld BM25, which is eﬃcient. We tested two ways of training DeepCT: a query-based method using sparse relevant query-document pairs, and a weakly-supervised method using document title-body pairs.
