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- W3154154276 abstract "In this paper we exploit cross-lingual models to enable dialogue act recognition for specific tasks with a small number of annotations. We design a transfer learning approach for dialogue act recognition and validate it on two different target languages and domains. We compute dialogue turn embeddings with both a CNN and multi-head self-attention model and show that the best results are obtained by combining all sources of transferred information. We further demonstrate that the proposed methods significantly outperform related cross-lingual DA recognition approaches." @default.
- W3154154276 created "2021-04-26" @default.
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- W3154154276 date "2020-05-19" @default.
- W3154154276 modified "2023-10-01" @default.
- W3154154276 title "Cross-lingual Approaches for Task-specific Dialogue Act Recognition." @default.
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