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Register a new userHOTTER: Hierarchical Optimal Topic Transport with Explanatory Context Representations
أكثر سخونة: نقل الموضوع الهرمي المثلى مع تمثيلات السياق التوضيحية
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غالبا ما تكون معالجة اللغات الطبيعية (NLP) هي العمود الفقري لأنظمة اليوم لتفاعلات المستخدم واسترجاع المعلومات وغيرها. تعتمد العديد من تطبيقات NLP هذه على تمثيلات متخصصة متخصصة (E.G. Adgeddings Word، نماذج الموضوع) التي تحسن القدرة على السبب في العلاقات بين وثائق Corpus. يقترن التقدم بالتقدم المحرز في التمثيلات المستفادة، كما أن مقاييس التشابه المستخدمة لمقارنة تمثيل الوثائق تتطور أيضا، مع اختلاف العديد من المقترحات في وقت الحساب أو الترجمة الشفوية. في هذه الورقة نقترح امتدادا لمقياس مسافة توثيق هجينة ناشئة محددة تجمع بين نماذج الموضوع و Adgeddings Word: النقل الهرمي للموضوع (Hott). في محددة، نقوم بتوسيع Hott باستخدام تمثيلات الكلمات المحسنة للسياق. نحن نقدم التحقق من صحة نهجنا على مجموعات البيانات العامة، باستخدام برت نموذج اللغة لمهمة تصنيف المستندات. تشير النتائج إلى أداء تنافسي من متري Hott الموسعة. علاوة على ذلك، قم بتطبيق مقياس التشغيل السريع وتمديده لدعم أبحاث الوسائط التعليمية، بمهمة استرجاع للمواضيع المطابقة في المناهج الدراسية الألمانية إلى ممرات الكتب المدرسية التعليمية، إلى جانب تقديم وثيقة توضيحية مساعدة تمثل الموضوع المهيمن للوثيقة المستردة. في دراسة المستخدم، تفضل طريقة تفسيرنا على الكلمات الرئيسية الموضوعية العادية.
Natural language processing (NLP) is often the backbone of today's systems for user interactions, information retrieval and others. Many of such NLP applications rely on specialized learned representations (e.g. neural word embeddings, topic models) that improve the ability to reason about the relationships between documents of a corpus. Paired with the progress in learned representations, the similarity metrics used to compare representations of documents are also evolving, with numerous proposals differing in computation time or interpretability. In this paper we propose an extension to a specific emerging hybrid document distance metric which combines topic models and word embeddings: the Hierarchical Optimal Topic Transport (HOTT). In specific, we extend HOTT by using context-enhanced word representations. We provide a validation of our approach on public datasets, using the language model BERT for a document categorization task. Results indicate competitive performance of the extended HOTT metric. We furthermore apply the HOTT metric and its extension to support educational media research, with a retrieval task of matching topics in German curricula to educational textbooks passages, along with offering an auxiliary explanatory document representing the dominant topic of the retrieved document. In a user study, our explanation method is preferred over regular topic keywords.
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