MultiHop attention for knowledge diagnosis of mathematics examination

He, Xinhua; Zhang, Tongxuan; Zhang, Guiyun

doi:10.1007/s10489-022-04033-x

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…It serves this purpose in the baseline method by enabling the model to give more weights to the most helpful parts of the passage for answering the question. However, single-hop attention is insufficient to perform more complex hierarchical reasoning [23,24]. Therefore, we proposed multihop attention.…”

Section: B: Multi-hop Attentionmentioning

confidence: 99%

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Unlocking Everyday Wisdom: Enhancing Machine Comprehension with Script Knowledge Integration

Zhou,

Yue,

Liang

et al. 2023

Applied Sciences

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Harnessing commonsense knowledge poses a significant challenge for machine comprehension systems. This paper primarily focuses on incorporating a specific subset of commonsense knowledge, namely, script knowledge. Script knowledge is about sequences of actions that are typically performed by individuals in everyday life. Our experiments were centered around the MCScript dataset, which was the basis of the SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. As a baseline, we utilized our Three-Way Attentive Networks (TriANs) framework to model the interactions among passages, questions, and answers. Building upon the TriAN, we proposed to: (1) integrate a pre-trained language model to capture script knowledge; (2) introduce multi-layer attention to facilitate multi-hop reasoning; and (3) incorporate positional embeddings to enhance the model’s capacity for event-ordering reasoning. In this paper, we present our proposed methods and prove their efficacy in improving script knowledge integration and reasoning.

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Section: B: Multi-hop Attentionmentioning

confidence: 99%

“…Step 2: The Second Hop The input of the second hop is a concatenation of the input of the first hop, the output embeddings from first hop, and the positional embeddings, as shown in Equations ( 21)- (23). Then, we applied the same attention mechanism as in the first hop and obtained…”

Section: Model Architecturementioning

confidence: 99%