# CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering

Source: [CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering](<https://www.sota2.com/research/paper/68fa68a91e209131cea1d8e5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:42:59.203Z

Authors: Junru Lu, Gabriele Pergola, Lin Gui, Binyang Li, Yulan He

Published: 2020-11-01T14:48:49.000Z

## Abstract

We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidences, and the answer memory working as a buffer continually refining the generated answers. Empirically, we show the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA review dataset. An additional qualitative analysis revealed the interpretability introduced by the memory module.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.00519>)

[PDF](<https://arxiv.org/pdf/2011.00519.pdf>)