# MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering

arXiv: https://arxiv.org/abs/2210.10350 · HTML: https://mlsanity.com/paper/2210.10350/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance](https://mlsanity.com/paper/2105.07624/) (2021)
- [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://mlsanity.com/paper/2004.07347/) (2020)
- [PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://mlsanity.com/paper/1904.09537/) (2019)
- [Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases](https://mlsanity.com/paper/2011.07743/) (2020)
- [Open Question Answering over Tables and Text](https://mlsanity.com/paper/2010.10439/) (2020)
- [MATE: Multi-view Attention for Table Transformer Efficiency](https://mlsanity.com/paper/2109.04312/) (2021)
- [Unsupervised Multi-hop Question Answering by Question Generation](https://mlsanity.com/paper/2010.12623/) (2020)
- [Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader](https://mlsanity.com/paper/1905.07098/) (2019)
- [Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering](https://mlsanity.com/paper/2108.02866/) (2021)
- [Representations for Question Answering from Documents with Tables and Text](https://mlsanity.com/paper/2101.10573/) (2021)
- [RoR: Read-over-Read for Long Document Machine Reading Comprehension](https://mlsanity.com/paper/2109.04780/) (2021)
- [UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation](https://mlsanity.com/paper/2210.08249/) (2022)
- [OPERA:Operation-Pivoted Discrete Reasoning over Text](https://mlsanity.com/paper/2204.14166/) (2022)

## Built on by (most cited first, 9 of 9)

- [CABINET: Content Relevance based Noise Reduction for Table Question Answering](https://mlsanity.com/paper/2402.01155/) (2024)
- [S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering](https://mlsanity.com/paper/2305.11725/) (2023)
- [SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models](https://mlsanity.com/paper/2311.09818/) (2023)
- [Exploring Hybrid Question Answering via Program-based Prompting](https://mlsanity.com/paper/2402.10812/) (2024)
- [A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions](https://mlsanity.com/paper/2212.13465/) (2022)
- [Hybrid Graphs for Table-and-Text based Question Answering using LLMs](https://mlsanity.com/paper/2501.17767/) (2025)
- [TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization](https://mlsanity.com/paper/2406.14732/) (2024)
- [TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering](https://mlsanity.com/paper/2305.14682/) (2023)
- [HopPG: Self-Iterative Program Generation for Multi-Hop Question Answering over Heterogeneous Knowledge](https://mlsanity.com/paper/2308.11257/) (2023)

## Abstract (arXiv)

Hybrid question answering (HQA) aims to answer questions over heterogeneous data, including tables and passages linked to table cells. The heterogeneous data can provide different granularity evidence to HQA models, e.t., column, row, cell, and link. Conventional HQA models usually retrieve coarse- or fine-grained evidence to reason the answer. Through comparison, we find that coarse-grained evidence is easier to retrieve but contributes less to the reasoner, while fine-grained evidence is the opposite. To preserve the advantage and eliminate the disadvantage of different granularity evidence, we propose MuGER2, a Multi-Granularity Evidence Retrieval and Reasoning approach. In evidence retrieval, a unified retriever is designed to learn the multi-granularity evidence from the heterogeneous data. In answer reasoning, an evidence selector is proposed to navigate the fine-grained evidence for the answer reader based on the learned multi-granularity evidence. Experiment results on the HybridQA dataset show that MuGER2 significantly boosts the HQA performance. Further ablation analysis verifies the effectiveness of both the retrieval and reasoning designs.