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

arXiv: https://arxiv.org/abs/2011.00519 · HTML: https://mlsanity.com/paper/2011.00519/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization](https://mlsanity.com/paper/2302.05574/) (2023)
- [Product Question Answering in E-Commerce: A Survey](https://mlsanity.com/paper/2302.08092/) (2023)
- [Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network](https://mlsanity.com/paper/2210.05499/) (2022)
- [Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation](https://mlsanity.com/paper/2210.12902/) (2022)
- [SPARTAN: Sparse Hierarchical Memory for Parameter-Efficient Transformers](https://mlsanity.com/paper/2211.16634/) (2022)
- [ViLegalExpert: A Large-Scale Benchmark for Vietnamese Legal Retrieval and Question Answering from Real-World Consultations](https://mlsanity.com/paper/2609.39189/) (2026)

## Abstract (arXiv)

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.