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Register a new userUtterance Position-Aware Dialogue Act Recognition
إدراك الوظيفة إدراك الحوار قانون الاعتراف
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تقترح هذه الدراسة نهجا نطق في موقف الكلام لنموذج التعرف على قانون الحوار العصبي القائم على الشبكة (دار)، مما يشتمل على الترميز الموضعي للموقف المطلق أو النسبي للكلام.النهج المقترح مستوحى من الملاحظة أن بعض أعمال الحوار لها اتجاهات مناصب الحدوث.تبين التقييمات على Corpus لوحة المفاتيح أن الترميز الموضعي المقترح للكلام يحسن إحصائيا بشكل كبير أداء دار.
This study proposes an utterance position-aware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance's absolute or relative position. The proposed approach is inspired by the observation that some dialogue acts have tendencies of occurrence positions. The evaluations on the Switchboard corpus show that the proposed positional encoding of utterances statistically significantly improves the performance of DAR.
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Dialogue Act (DA) classification is the task of classifying utterances with respect to the function they serve in a dialogue. Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout t
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