# DUMA: Reading Comprehension with Transposition Thinking

arXiv: https://arxiv.org/abs/2001.09415 · HTML: https://mlsanity.com/paper/2001.09415/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2020.

## Built on by (most cited first, 8 of 8)

- [From LSAT: The Progress and Challenges of Complex Reasoning](https://mlsanity.com/paper/2108.00648/) (2021)
- [Context-guided Triple Matching for Multiple Choice Question Answering](https://mlsanity.com/paper/2109.12996/) (2021)
- [EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval](https://mlsanity.com/paper/2310.00970/) (2023)
- [XRJL-HKUST at SemEval-2021 Task 4: WordNet-Enhanced Dual Multi-head Co-Attention for Reading Comprehension of Abstract Meaning](https://mlsanity.com/paper/2103.16102/) (2021)
- [U3E: Unsupervised and Erasure-based Evidence Extraction for Machine Reading Comprehension](https://mlsanity.com/paper/2210.02621/) (2022)
- [Transfer Learning Enhanced Single-choice Decision for Multi-choice Question Answering](https://mlsanity.com/paper/2404.17949/) (2024)
- [Correct after Answer: Enhancing Multi-Span Question Answering with Post-Processing Method](https://mlsanity.com/paper/2410.16788/) (2024)
- [Investigating Recent Large Language Models for Vietnamese Machine Reading Comprehension](https://mlsanity.com/paper/2503.18062/) (2025)

## Abstract (arXiv)

Multi-choice Machine Reading Comprehension (MRC) requires model to decide the correct answer from a set of answer options when given a passage and a question. Thus in addition to a powerful Pre-trained Language Model (PrLM) as encoder, multi-choice MRC especially relies on a matching network design which is supposed to effectively capture the relationships among the triplet of passage, question and answers. While the newer and more powerful PrLMs have shown their mightiness even without the support from a matching network, we propose a new DUal Multi-head Co-Attention (DUMA) model, which is inspired by human's transposition thinking process solving the multi-choice MRC problem: respectively considering each other's focus from the standpoint of passage and question. The proposed DUMA has been shown effective and is capable of generally promoting PrLMs. Our proposed method is evaluated on two benchmark multi-choice MRC tasks, DREAM and RACE, showing that in terms of powerful PrLMs, DUMA can still boost the model to reach new state-of-the-art performance.