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• W3154760966 abstract "Tracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process. Firstly, we propose to formulate this task as a question answering problem. This enables us to use pre-trained transformer-based language models on other QA benchmarks by adapting those to the procedural text understanding. Secondly, since the transformer-based language models cannot encode the flow of events by themselves, we propose a Time-Stamped Language Model~(TSLM model) to encode event information in LMs architecture by introducing the timestamp encoding. Our model evaluated on the Propara dataset shows improvements on the published state-of-the-art results with a $3.1%$ increase in F1 score. Moreover, our model yields better results on the location prediction task on the NPN-Cooking dataset. This result indicates that our approach is effective for procedural text understanding in general." @default.
• W3154760966 created "2021-04-26" @default.
• W3154760966 creator A5037637824 @default.
• W3154760966 creator A5087682739 @default.
• W3154760966 date "2021-04-15" @default.
• W3154760966 modified "2023-09-27" @default.
• W3154760966 title "Time-Stamped Language Model: Teaching Language Models to Understand the Flow of Events." @default.
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