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- Li, Jianquan, et al.
(författare)
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Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk
- 2023
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Ingår i: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). - Stroudsburg, PA : Association for Computational Linguistics. - 9781959429722 ; , s. 7581-7596
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Konferensbidrag (refereegranskat)abstract
- Language is the principal tool for human communication, in which humor is one of the most attractive parts. Producing natural language like humans using computers, a.k.a, Natural Language Generation (NLG), has been widely used for dialogue systems, chatbots, text summarization, as well as AI-Generated Content (AIGC), e.g., idea generation, and scriptwriting. However, the humor aspect of natural language is relatively under-investigated, especially in the age of pre-trained language models. In this work, we aim to preliminarily test whether NLG can generate humor as humans do. We build the largest dataset consisting of numerous Chinese Comical Crosstalk scripts (called C3 in short), which is for a popular Chinese performing art called 'Xiangsheng' or '相声' since 1800s. We benchmark various generation approaches including training-from-scratch Seq2seq, fine-tuned middle-scale PLMs, and large-scale PLMs with and without fine-tuning. Moreover, we also conduct a human assessment, showing that 1) large-scale pretraining largely improves crosstalk generation quality; and 2) even the scripts generated from the best PLM is far from what we expect. We conclude humor generation could be largely improved using large-scale PLMs, but it is still in its infancy. The data and benchmarking code are publicly available in https://github.com/anonNo2/crosstalk-generation. © 2023 Association for Computational Linguistics.
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| 2. |
- Liu, Ziming, et al.
(författare)
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A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles
- 2022
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Ingår i: Digital Communications and Networks. - : Elsevier. - 2468-5925 .- 2352-8648. ; 8:6, s. 976-983
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Tidskriftsartikel (refereegranskat)abstract
- Social Internet of Vehicles (SIoV) falls under the umbrella of social Internet of Things (IoT), where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology, which brings new opportunities and challenges, e.g., collaborative power trading can address the mileage anxiety of electric vehicles. However, it relies on a trusted central party for scheduling, which introduces performance bottlenecks and cannot be set up in a distributed network, in addition, the lack of transparency in state-of-the-art Vehicle-to-Vehicle (V2V) power trading schemes can introduce further trust issues. In this paper, we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch center. Based on the game theory, we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social welfare. We use blockchain to record power trading data for trusted pricing and use smart contracts for transaction matching. The simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.
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