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- W2908006036 abstract "Slot filling techniques are often adopted in language understanding components for task-oriented dialogue systems. In recent approaches, neural models for slot filling are trained on domain-specific datasets, making it difficult porting to similar domains when few or no training data are available. In this paper we use multi-task learning to leverage general knowledge of a task, namely Named Entity Recognition (NER), to improve slot filling performance on a semantically similar domain-specific task. Our experiments show that, for some datasets, transfer learning from NER can achieve competitive performance compared with the state-of-the-art and can also help slot filling in low resource scenarios." @default.
- W2908006036 created "2019-01-11" @default.
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- W2908006036 date "2018-01-01" @default.
- W2908006036 modified "2023-09-24" @default.
- W2908006036 title "From General to Specific: Leveraging Named Entity Recognition for Slot Filling in Conversational Language Understanding" @default.
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- W2908006036 doi "https://doi.org/10.4000/books.aaccademia.3423" @default.
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