Titlebook: Natural Language Processing and Chinese Computing; 11th CCF Internation Wei Lu,Shujian Huang,Xiabing Zhou Conference proceedings 2022 The E

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Contrastive Learning for?Robust Neural Machine Translation with?ASR Errorsso very brittle and easily falter when fed with noisy sentences, i.e., from automatic speech recognition (ASR) output. Due to the lack of Chinese-to-English translation test set with natural Chinese-side ASR output, related studies artificially add noise into Chinese sentences to evaluation translat

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擁擠前

Regularized Contrastive Learning of?Semantic Searchroperly learn the semantics of sentences. Transformer-based models are widely used as retrieval models due to their excellent ability to learn semantic representations. in the meantime, many regularization methods suitable for them have also been proposed. In this paper, we propose a new regularizat

設(shè)施

Kformer: Knowledge Injection in?Transformer Feed-Forward Layersthe PTMs’ own ability with quantities of implicit knowledge stored in parameters. A recent study [.] has observed knowledge neurons in the Feed Forward Network (FFN), which are responsible for expressing factual knowledge. In this work, we propose a simple model, Kformer, which takes advantage of th

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Doge Tickets: Uncovering Domain-General Language Models by?Playing Lottery Ticketsrning capacity of LMs can also lead to large learning variance. In a pilot study, we find that, when faced with multiple domains, a critical portion of parameters behave unexpectedly in a domain-specific manner while others behave in a domain-general one. Motivated by this phenomenon, we for the fir

Retrieval

BART-Reader: Predicting Relations Between Entities via?Reading Their Document-Level Context Informat separate entities, the importance of its mentions varies, which means the entity representation should be different. However, most of the previous RE models failed to make the relation classification entity-pair aware effectively. To that end, we propose a novel adaptation to simultaneously utilize

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