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Register a new userMMTL: The Meta Multi-Task Learning for Aspect Category Sentiment Analysis
MMTL: التعلم Meta متعدد المهام لتحليل المعنويات الفئة في الفئة
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تحليل المعنويات الفئة في الأساس (ACSA)، والتي تهدف إلى تحديد أساور المشاعر المحبوبة من فئات الارتفاع المناقشات في مراجعات المستخدمين. ACSA صعبة ومكلفة عند إجراءها في تطبيقات عالمية حقيقية، والتي ترجع بشكل رئيسي إلى الأسباب التالية: 1.) وعلم بيانات ACSA الفاخرة غالبا ما تكون كثيفة العمالة. 2.) سيتم تحديث فئات الارتفاع بشكل ديناميكي وتعديلها بتطوير سيناريوهات التطبيق، مما يعني أن البيانات يجب أن تنعيم بشكل متكرر. 3.) نظرا لزيادة فئات الارتفاع، يجب إعادة تدريب النموذج بشكل متكرر للتكيف السريع مع بيانات فئة الجانب الإضافية حديثا. للتغلب على المشكلات المذكورة أعلاه، نقدم نهجا جديدا للتعلم من التعلم متعددة المهام (MMTL)، هذه المهام ACSA بمثابة مشكلة في التعلم التلوي (أي فيما يتعلق بمشاكل تصنيف قطباء القطبية في الفئة في الفئة مثل المهام التدريبية المختلفة لل meta - التعلم) لتعلم تهيئة مثالية وقابلة للتخصيرة نموذج التعلم متعدد المهام التي يمكن تكييفها مع مهام ACSA الجديدة بكفاءة وفعالية. تشير نتائج التجربة إلى أن النهج المقترح يتفوق بشكل كبير على النموذج الأساسي القائم على المحولات القائم على المحولات القوية مسبقا، خاصة، في حالة وجود بيانات تدريبية على غرامة أقل وصفها.
Aspect Category Sentiment Analysis (ACSA), which aims to identify fine-grained sentiment polarities of the aspect categories discussed in user reviews. ACSA is challenging and costly when conducting it into real-world applications, that mainly due to the following reasons: 1.) Labeling the fine-grained ACSA data is often labor-intensive. 2.) The aspect categories will be dynamically updated and adjusted with the development of application scenarios, which means that the data must be relabeled frequently. 3.) Due to the increase of aspect categories, the model must be retrained frequently to fast adapt to the newly added aspect category data. To overcome the above-mentioned problems, we introduce a novel Meta Multi-Task Learning (MMTL) approach, that frame ACSA tasks as a meta-learning problem (i.e., regarding aspect-category sentiment polarity classification problems as the different training tasks for meta-learning) to learn an ideal and shareable initialization for the multi-task learning model that can be adapted to new ACSA tasks efficiently and effectively. Experiment results show that the proposed approach significantly outperforms the strong pre-trained transformer-based baseline model, especially, in the case of less labeled fine-grained training data.
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Aspect category sentiment analysis has attracted increasing research attention. The dominant methods make use of pre-trained language models by learning effective aspect category-specific representations, and adding specific output layers to its pre-
trained representation. We consider a more direct way of making use of pre-trained language models, by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. Our method allows more direct use of pre-trained knowledge in seq2seq language models by directly following the task setting during pre-training. Experiments on several benchmarks show that our method gives the best reported results, having large advantages in few-shot and zero-shot settings.
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In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. That is, we are no longer entangled about h
ow to laboriously search the sentiment clues for coarse-grained aspects from the context, but how to preferably find the words highly related to the aspects in the context and determine their importance based on the public knowledge base. In this way, the contextual sentiment clues can be explicitly tracked in ACSA for the aspects in the light of these aspect-related words. To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge. Then, we employ Beta Distribution to educe the aspect-aware weight, which reflects the importance to the aspect, for each aspect-aware word. Afterward, the aspect-aware words are served as the substitutes of the coarse-grained aspect to construct graphs for leveraging the aspect-related contextual sentiment dependencies in ACSA. Experiments on 6 benchmark datasets show that our approach significantly outperforms the state-of-the-art baseline methods.
Sentiment analysis has attracted increasing attention in e-commerce. The sentiment polarities underlying user reviews are of great value for business intelligence. Aspect category sentiment analysis (ACSA) and review rating prediction (RP) are two es
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Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in a sentence, which is an important task in real-world applications. To perform ABSA, the trained model is required to have a good understanding
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