no code implementations • 16 Feb 2024 • Qi Shi, Han Cui, Haofeng Wang, Qingfu Zhu, Wanxiang Che, Ting Liu
Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data.
no code implementations • 12 Dec 2023 • Qi Shi
This paper shows that the newly proposed notion of responsibility and counterfactual responsibility are not definable through each other and studies the responsibility gap for these two forms of responsibility.
1 code implementation • 1 Jun 2023 • Han Cui, Shangzhan Li, Yu Zhang, Qi Shi
The generation of explanation graphs is a significant task that aims to produce explanation graphs in response to user input, revealing the internal reasoning process.
no code implementations • 24 Nov 2022 • Yueqing Sun, Yu Zhang, Le Qi, Qi Shi
In this paper, we aim to address the above limitation by leveraging the implicit knowledge stored in PrLMs and propose a two-stage prompt-based unsupervised commonsense question answering framework (TSGP).
no code implementations • 8 Aug 2022 • Sophia Knight, Pavel Naumov, Qi Shi, Vigasan Suntharraj
The article proposes a new technique for proving the undefinability of logical connectives through each other and illustrates the technique with several examples.
2 code implementations • 20 Jan 2022 • Qi Shi, Qian Liu, Bei Chen, Yu Zhang, Ting Liu, Jian-Guang Lou
In this work, we propose LEMON, a general framework for language-based environment manipulation tasks.
no code implementations • 6 Dec 2021 • Qi Shi, Dong Hao
The second mechanism has an asymmetric Bayesian Nash equilibrium, and agents' behaviors in equilibrium show a vast diversity which is strongly related to their social relations.
1 code implementation • NAACL 2022 • Yueqing Sun, Qi Shi, Le Qi, Yu Zhang
Specifically, JointLK performs joint reasoning between LM and GNN through a novel dense bidirectional attention module, in which each question token attends on KG nodes and each KG node attends on question tokens, and the two modal representations fuse and update mutually by multi-step interactions.
1 code implementation • EMNLP 2021 • Qi Shi, Yu Zhang, Qingyu Yin, Ting Liu
Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table.
no code implementations • COLING 2020 • Qi Shi, Yu Zhang, Qingyu Yin, Ting Liu
Table-based fact verification is expected to perform both linguistic reasoning and symbolic reasoning.