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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Syntactic representation is independent of semantics in Mandarin: evidence from syntactic priming ... Xuemei Chen; Branigan, Holly P.; Suiping Wang. - : Taylor & Francis, 2019 BASE Show details 2 Syntactic representation is independent of semantics in Mandarin: evidence from syntactic priming ... Xuemei Chen; Branigan, Holly P.; Suiping Wang. - : Taylor & Francis, 2019 BASE Show details 3 Conversational Emotion Analysis via Attention Mechanisms ... Lian, Zheng; Tao, Jianhua; Liu, Bin; Huang, Jian. - : arXiv, 2019 Abstract: Different from the emotion recognition in individual utterances, we propose a multimodal learning framework using relation and dependencies among the utterances for conversational emotion analysis. The attention mechanism is applied to the fusion of the acoustic and lexical features. Then these fusion representations are fed into the self-attention based bi-directional gated recurrent unit (GRU) layer to capture long-term contextual information. To imitate real interaction patterns of different speakers, speaker embeddings are also utilized as additional inputs to distinguish the speaker identities during conversational dialogs. To verify the effectiveness of the proposed method, we conduct experiments on the IEMOCAP database. Experimental results demonstrate that our method shows absolute 2.42% performance improvement over the state-of-the-art strategies. ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG URL: https://arxiv.org/abs/1910.11263 https://dx.doi.org/10.48550/arxiv.1910.11263 BASE Hide details

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