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Register a new userThinking Clearly, Talking Fast: Concept-Guided Non-Autoregressive Generation for Open-Domain Dialogue Systems
التفكير بوضوح، والنحدث بسرعة: الجيل غير المركزي على مفهوم أنظمة الحوار مفتوح المجال
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يحتوي الحوار البشري على مفاهيم متطورة، ومكبرات الصوت تربط بشكل طبيعي مفاهيم متعددة لإثبات استجابة.ومع ذلك، فإن طرازات الحوار الحالية مع إطار SEQ2SEQ تفتقر إلى القدرة على إدارة مفهوم المفهوم بفعالية ويمكن أن تعد بالكاد مفاهيم متعددة للردود بطريقة فك التشفير المتسلسلة.لتسهيل حوار قابل للتحكم ومتماسك، في هذا العمل، نركض نموذجا غير مرشد من المفاهيم (CG-NAR) لتوليد الحوار مفتوح المجال.يشتمل النموذج المقترح على وحدة تخطيط متعددة المفاهيم يتعلم تحديد مفاهيم متعددة مرتبطة من الرسم البياني للمفهوم ومحول الإدراج المخصص يؤدي جيل غير توجيهي من المفاهيم إلى إكمال استجابة.تظهر النتائج التجريبية على مجموعة بيانات عامة أن CG-NAR يمكن أن ينتج استجابات متنوعة ومتماسكة، مما يتفوق على خطوط خطوط خطوط أساسية في كل من التقييمات التلقائية والإنسانية مع سرعة الاستدلال بشكل كبير.
Human dialogue contains evolving concepts, and speakers naturally associate multiple concepts to compose a response. However, current dialogue models with the seq2seq framework lack the ability to effectively manage concept transitions and can hardly introduce multiple concepts to responses in a sequential decoding manner. To facilitate a controllable and coherent dialogue, in this work, we devise a concept-guided non-autoregressive model (CG-nAR) for open-domain dialogue generation. The proposed model comprises a multi-concept planning module that learns to identify multiple associated concepts from a concept graph and a customized Insertion Transformer that performs concept-guided non-autoregressive generation to complete a response. The experimental results on two public datasets show that CG-nAR can produce diverse and coherent responses, outperforming state-of-the-art baselines in both automatic and human evaluations with substantially faster inference speed.
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Research on open-domain dialogue systems that allow free topics is challenging in the field of natural language processing (NLP). The performance of the dialogue system has been improved recently by the method utilizing dialogue-related knowledge; ho
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