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Register a new userImproving Unsupervised Dialogue Topic Segmentation with Utterance-Pair Coherence Scoring
تحسين تجزئة موضوع الحوار غير الخاضعة للرقابة مع سجل تماسك الزوج
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تجزئة موضوع الحوار أمر بالغ الأهمية في العديد من مشاكل نموذج الحوار.ومع ذلك، فإن النهج الشائعة غير المعينة الشائعة لاستغلال الميزات السطحية فقط في تقييم التماسك الموضعي بين الكلام.في هذا العمل، نتعامل مع هذا القيد من خلال الاستفادة من الإشارات الإشرافية من مهمة التسجيل في اتساق زوج الكلام.أولا، نقدم استراتيجية بسيطة ولكنها فعالة لتوليد كوربوس التدريب لتسجيلات التماسك زوج الكلام.بعد ذلك، ندرب نموذج متماسك نطق برت مقره برت مع Corpus التدريب الذي تم الحصول عليه.أخيرا، يتم استخدام هذا النموذج لقياس الملاءمة الموضعية بين الكلام، والتصرف كأساس لاستدلال التجزئة.تجارب على ثلاث مجموعات بيانات عامة باللغة الإنجليزية والصينية توضح أن اقتراحنا يتفوق على خطوط الأساس الحديثة.
Dialogue topic segmentation is critical in several dialogue modeling problems. However, popular unsupervised approaches only exploit surface features in assessing topical coherence among utterances. In this work, we address this limitation by leveraging supervisory signals from the utterance-pair coherence scoring task. First, we present a simple yet effective strategy to generate a training corpus for utterance-pair coherence scoring. Then, we train a BERT-based neural utterance-pair coherence model with the obtained training corpus. Finally, such model is used to measure the topical relevance between utterances, acting as the basis of the segmentation inference. Experiments on three public datasets in English and Chinese demonstrate that our proposal outperforms the state-of-the-art baselines.
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