@inproceedings{zhu-etal-2024-towards-graph,
title = "Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database",
author = "Zhu, Minjun and
Weng, Yixuan and
He, Shizhu and
Liu, Kang and
Liu, Haifeng and
Jun, Yang jun and
Zhao, Jun",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1437",
pages = "16539--16549",
abstract = "In textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps. While existing benchmarks are limited to single-chain or single-hop retrieval scenarios. In this paper, we propose to conduct Graph-Hop {---}{---} a novel multi-chains and multi-hops retrieval and reasoning paradigm in complex question answering. We construct a new benchmark called ReasonGraphQA, which provides explicit and fine-grained evidence graphs for complex question to support comprehensive and detailed reasoning. In order to further study how graph-based evidential reasoning can be performed, we explore what form of Graph-Hop works best for generating textual evidence explanations in knowledge reasoning and question answering. We have thoroughly evaluated existing evidence retrieval and reasoning models on the ReasonGraphQA. Experiments highlight Graph-Hop is a promising direction for answering complex questions, but it still has certain limitations. We have further studied mitigation strategies to meet these challenges and discuss future directions.",
}
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<abstract>In textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps. While existing benchmarks are limited to single-chain or single-hop retrieval scenarios. In this paper, we propose to conduct Graph-Hop —— a novel multi-chains and multi-hops retrieval and reasoning paradigm in complex question answering. We construct a new benchmark called ReasonGraphQA, which provides explicit and fine-grained evidence graphs for complex question to support comprehensive and detailed reasoning. In order to further study how graph-based evidential reasoning can be performed, we explore what form of Graph-Hop works best for generating textual evidence explanations in knowledge reasoning and question answering. We have thoroughly evaluated existing evidence retrieval and reasoning models on the ReasonGraphQA. Experiments highlight Graph-Hop is a promising direction for answering complex questions, but it still has certain limitations. We have further studied mitigation strategies to meet these challenges and discuss future directions.</abstract>
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%0 Conference Proceedings
%T Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database
%A Zhu, Minjun
%A Weng, Yixuan
%A He, Shizhu
%A Liu, Kang
%A Liu, Haifeng
%A Jun, Yang jun
%A Zhao, Jun
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zhu-etal-2024-towards-graph
%X In textual question answering (TQA) systems, complex questions often require retrieving multiple textual fact chains with multiple reasoning steps. While existing benchmarks are limited to single-chain or single-hop retrieval scenarios. In this paper, we propose to conduct Graph-Hop —— a novel multi-chains and multi-hops retrieval and reasoning paradigm in complex question answering. We construct a new benchmark called ReasonGraphQA, which provides explicit and fine-grained evidence graphs for complex question to support comprehensive and detailed reasoning. In order to further study how graph-based evidential reasoning can be performed, we explore what form of Graph-Hop works best for generating textual evidence explanations in knowledge reasoning and question answering. We have thoroughly evaluated existing evidence retrieval and reasoning models on the ReasonGraphQA. Experiments highlight Graph-Hop is a promising direction for answering complex questions, but it still has certain limitations. We have further studied mitigation strategies to meet these challenges and discuss future directions.
%U https://aclanthology.org/2024.lrec-main.1437
%P 16539-16549
Markdown (Informal)
[Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database](https://aclanthology.org/2024.lrec-main.1437) (Zhu et al., LREC-COLING 2024)
ACL
- Minjun Zhu, Yixuan Weng, Shizhu He, Kang Liu, Haifeng Liu, Yang jun Jun, and Jun Zhao. 2024. Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16539–16549, Torino, Italia. ELRA and ICCL.