  Recently, substantial progress has been made in text ranking based on
pretrained language models such as BERT. However, there are limited studies on
how to leverage more powerful sequence-to-sequence models such as T5. Existing
attempts usually formulate text ranking as classification and rely on
postprocessing to obtain a ranked list. In this paper, we propose RankT5 and
study two T5-based ranking model structures, an encoder-decoder and an
encoder-only one, so that they not only can directly output ranking scores for
each query-document pair, but also can be fine-tuned with "pairwise" or
"listwise" ranking losses to optimize ranking performances. Our experiments
show that the proposed models with ranking losses can achieve substantial
ranking performance gains on different public text ranking data sets. Moreover,
when fine-tuned with listwise ranking losses, the ranking model appears to have
better zero-shot ranking performance on out-of-domain data sets compared to the
model fine-tuned with classification losses.
