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• W4306888994 abstract "Pre-trained language models (PrLMs) have achieved great success on a wide range of natural language processing tasks by virtue of the universal language representation ability obtained by self-supervised learning on a large corpus. These models are pre-trained on standard plain texts with general language model (LM) training objectives, which would be insufficient to model dialogue-exclusive attributes like specificity and informativeness reflected in these tasks that are not explicitly captured by the pre-trained universal language representations. In this work, we propose dialogue-adaptive pre-training objectives (DAPO) derived from quality estimation to simulate dialogue-specific features, namely coherence, specificity, and informativeness. As the foundation for model pre-training, we synthesize a new dialogue corpus and build our training set with two unsupervised methods: 1) coherence-oriented context corruption, including utterance ordering, insertion, and replacement, to help the model capture the coherence inside the dialogue contexts; and 2) specificity-oriented automatic rescoring, which encourages the model to measure the quality of the synthesized data for dialogue-adaptive pre-training by considering specificity and informativeness. Experimental results on widely used open-domain response selection and quality estimation benchmarks show that DAPO significantly improves the baseline models and achieves state-of-the-art performance on the MuTual leaderboard, verifying the effectiveness of estimating quality evaluation factors into pre-training." @default.
• W4306888994 created "2022-10-21" @default.
• W4306888994 creator A5036050911 @default.
• W4306888994 creator A5070962435 @default.
• W4306888994 creator A5077320577 @default.
• W4306888994 date "2023-01-01" @default.
• W4306888994 modified "2023-10-06" @default.
• W4306888994 title "Dialogue-adaptive language model pre-training from quality estimation☆" @default.
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• W4306888994 doi "https://doi.org/10.1016/j.neucom.2022.10.036" @default.
• W4306888994 hasPublicationYear "2023" @default.
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