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Register a new userBuilder, we have done it: Evaluating \& Extending Dialogue-AMR NLU Pipeline for Two Collaborative Domains
Builder، لقد فعلنا ذلك: تقييم \ & retending dialogue-amr nlu خط أنابيب اثنين من المجالات التعاونية
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نعتمد وتقييم وتحسين خطاب خط أنابيب طبيعي من خطوتين (NLU) على خطاب (NLU) الذي يرصد تدريجيا على تباين إيداع اللغة الطبيعية غير المقيدة والخرائط إلى سلوكيات الروبوت القابلة للتنفيذ.يقوم خط الأنابيب أولا بإضافة تمثيل تمثيل المعنى التجريدي (AMR) لالتقاط المحتوى المقترح للكلام بالكلام، وتحول ثانيا إلى هذا الحوار-عمرو، "مما يؤدي إلى زيادة AMR القياسية مع معلومات عن التوتر والجانب والعقار والكلمات.يتم تقييم العديد من الأساليب البديلة وتدريب مجموعات البيانات التدريبية لكلا الخطوتين والمكونات المقابلة لخط الأنابيب، بعضها يتفوق على الأصل الأصلي.نقوم بتوسيع مخطط التعليق التوضيحي للحوار - AMR لتغطية مجال التعليمات التعاوني المختلفة وتقييم على كلا النطاقات.مع القليل جدا من البيانات التدريبية، نحقق أداء واعد في المجال الجديد، مما يدل على قابلية هذا النهج.
We adopt, evaluate, and improve upon a two-step natural language understanding (NLU) pipeline that incrementally tames the variation of unconstrained natural language input and maps to executable robot behaviors. The pipeline first leverages Abstract Meaning Representation (AMR) parsing to capture the propositional content of the utterance, and second converts this into Dialogue-AMR,'' which augments standard AMR with information on tense, aspect, and speech acts. Several alternative approaches and training datasets are evaluated for both steps and corresponding components of the pipeline, some of which outperform the original. We extend the Dialogue-AMR annotation schema to cover a different collaborative instruction domain and evaluate on both domains. With very little training data, we achieve promising performance in the new domain, demonstrating the scalability of this approach.
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