  Long Document retrieval (DR) has always been a tremendous challenge for
reading comprehension and information retrieval. The pre-training model has
achieved good results in the retrieval stage and Ranking for long documents in
recent years. However, there is still some crucial problem in long document
ranking, such as data label noises, long document representations, negative
data Unbalanced sampling, etc. To eliminate the noise of labeled data and to be
able to sample the long documents in the search reasonably negatively, we
propose the bag sampling method and the group-wise Localized Contrastive
Estimation(LCE) method. We use the head middle tail passage for the long
document to encode the long document, and in the retrieval, stage Use dense
retrieval to generate the candidate's data. The retrieval data is divided into
multiple bags at the ranking stage, and negative samples are selected in each
bag. After sampling, two losses are combined. The first loss is LCE. To fit bag
sampling well, after query and document are encoded, the global features of
each group are extracted by convolutional layer and max-pooling to improve the
model's resistance to the impact of labeling noise, finally, calculate the LCE
group-wise loss. Notably, our model shows excellent performance on the MS MARCO
Long document ranking leaderboard.
