Hindsight: Posterior-guided training of retrievers for improved
open-ended generation
- URL: http://arxiv.org/abs/2110.07752v1
- Date: Thu, 14 Oct 2021 22:24:57 GMT
- Title: Hindsight: Posterior-guided training of retrievers for improved
open-ended generation
- Authors: Ashwin Paranjape, Omar Khattab, Christopher Potts, Matei Zaharia,
Christopher D. Manning
- Abstract summary: We propose an additional guide retriever that is allowed to use the target output and "in hindsight" retrieve relevant passages during training.
For informative conversations from the Wizard of Wikipedia dataset, with posterior-guided training, the retriever finds passages with higher relevance in the top-10.
- Score: 41.59136233128446
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Many text generation systems benefit from using a retriever to retrieve
passages from a textual knowledge corpus (e.g., Wikipedia) which are then
provided as additional context to the generator. For open-ended generation
tasks (like generating informative utterances in conversations) many varied
passages may be equally relevant and we find that existing methods that jointly
train the retriever and generator underperform: the retriever may not find
relevant passages even amongst the top-10 and hence the generator may not learn
a preference to ground its generated output in them. We propose using an
additional guide retriever that is allowed to use the target output and "in
hindsight" retrieve relevant passages during training. We model the guide
retriever after the posterior distribution Q of passages given the input and
the target output and train it jointly with the standard retriever and the
generator by maximizing the evidence lower bound (ELBo) in expectation over Q.
For informative conversations from the Wizard of Wikipedia dataset, with
posterior-guided training, the retriever finds passages with higher relevance
in the top-10 (23% relative improvement), the generator's responses are more
grounded in the retrieved passage (19% relative improvement) and the end-to-end
system produces better overall output (6.4% relative improvement).
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