کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6940728 1450018 2018 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Integrated neural network model for identifying speech acts, predicators, and sentiments of dialogue utterances
ترجمه فارسی عنوان
مدل شبکه عصبی یکپارچه برای شناسایی اعمال گفتاری، پیش بینی کننده ها و احساسات گفتار گفتاری
کلمات کلیدی
مدل شناسایی قواعد یکپارچه، شناسایی عمل سخنرانی، شناسایی پیش بینی کننده، شناسایی احساسات، بازگشت جزئی به عقب،
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
A dialogue system should capture speakers' intentions, which can be represented by combinations of speech acts, predicators, and sentiments. To identify these intentions from speakers' utterances, many studies have independently dealt with speech acts, predicators, and sentiments. However, these three elements composing speakers' intentions are tightly associated with each other. To resolve this problem, we propose a convolutional neural network model that simultaneously identifies speech acts, predicators, and sentiments. The proposed model has well-designed hidden layers for embedding informative abstractions appropriate for speech act identification, predicator identification, and sentiment identification. Nodes in the hidden layers are partially trained by three cycles of error backpropagation: training the nodes associated with speech act identification, predicator identification, and sentiment identification. In the experiments, the proposed model showed higher F1-scores than independent models: 6.8% higher in speech act identification, 6.2% higher in predicator identification, and 4.9% higher in sentiment identification. Based on the experimental results, we conclude that the proposed integration architecture and partial error backpropagation can help to increase the performance of intention identification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 101, 1 January 2018, Pages 1-5
نویسندگان
, ,