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Register a new userLabel-Guided Learning for Item Categorization in e-Commerce
التعلم الموجه في التسمية لتصنيف البند في التجارة الإلكترونية
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تصنيف البند هو تطبيق مهم لتصنيف النص في التجارة الإلكترونية بسبب تأثيرها على تجربة التسوق عبر الإنترنت للمستخدمين.يتم استخدام فئة واحدة من تقنيات تصنيف النص التي اكتسبت الاهتمام مؤخرا المعلومات الدلالية للملصقات لتوجيه مهمة التصنيف.لقد أجرينا تحقيقا منهجيا في الفوائد المحتملة لهذه الطرق على بيانات حقيقية مجموعة من شركة تجارة إلكترونية كبرى في اليابان.علاوة على ذلك، باستخدام مساحة مفرط لتضمين ملصقات المنتجات التي يتم تنظيمها في هيكل هرمي أدت إلى أداء أفضل مقارنة باستخدام تضمين الفضاء الإقليدي التقليدي.توضح هذه النتائج كيف يمكن للتعلم الموجه على التسمية تحسين أنظمة تصنيف البند في مجال التجارة الإلكترونية.
Item categorization is an important application of text classification in e-commerce due to its impact on the online shopping experience of users. One class of text classification techniques that has gained attention recently is using the semantic information of the labels to guide the classification task. We have conducted a systematic investigation of the potential benefits of these methods on a real data set from a major e-commerce company in Japan. Furthermore, using a hyperbolic space to embed product labels that are organized in a hierarchical structure led to better performance compared to using a conventional Euclidean space embedding. These findings demonstrate how label-guided learning can improve item categorization systems in the e-commerce domain.
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We propose a practical instant question answering (QA) system on product pages of e-commerce services, where for each user query, relevant community question answer (CQA) pairs are retrieved. User queries and CQA pairs differ significantly in languag
e characteristics making relevance learning difficult. Our proposed transformer-based model learns a robust relevance function by jointly learning unified syntactic and semantic representations without the need for human labeled data. This is achieved by distantly supervising our model by distilling from predictions of a syntactic matching system on user queries and simultaneously training with CQA pairs. Training with CQA pairs helps our model learning semantic QA relevance and distant supervision enables learning of syntactic features as well as the nuances of user querying language. Additionally, our model encodes queries and candidate responses independently allowing offline candidate embedding generation thereby minimizing the need for real-time transformer model execution. Consequently, our framework is able to scale to large e-commerce QA traffic. Extensive evaluation on user queries shows that our framework significantly outperforms both syntactic and semantic baselines in offline as well as large scale online A/B setups of a popular e-commerce service.
Electronic arbitration and its electronic decisions are
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Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of domain-specific knowledge, whi
ch is essential for downstream tasks in many domains, such as tasks in e-commerce scenarios. In this paper, we propose K-PLUG, a knowledge-injected pre-trained language model based on the encoder-decoder transformer that can be transferred to both natural language understanding and generation tasks. Specifically, we propose five knowledge-aware self-supervised pre-training objectives to formulate the learning of domain-specific knowledge, including e-commerce domain-specific knowledge-bases, aspects of product entities, categories of product entities, and unique selling propositions of product entities. We verify our method in a diverse range of e-commerce scenarios that require domain-specific knowledge, including product knowledge base completion, abstractive product summarization, and multi-turn dialogue. K-PLUG significantly outperforms baselines across the board, which demonstrates that the proposed method effectively learns a diverse set of domain-specific knowledge for both language understanding and generation tasks. Our code is available.
Humans can distinguish new categories very efficiently with few examples, largely due to the fact that human beings can leverage knowledge obtained from relevant tasks. However, deep learning based text classification model tends to struggle to achie
ve satisfactory performance when labeled data are scarce. Inspired by human intelligence, we propose to introduce external knowledge into few-shot learning to imitate human knowledge. A novel parameter generator network is investigated to this end, which is able to use the external knowledge to generate different metrics for different tasks. Armed with this network, similar tasks can use similar metrics while different tasks use different metrics. Through experiments, we demonstrate that our method outperforms the SoTA few-shot text classification models.
Supervised learning assumes that a ground truth label exists. However, the reliability of this ground truth depends on human annotators, who often disagree. Prior work has shown that this disagreement can be helpful in training models. We propose a n
ovel method to incorporate this disagreement as information: in addition to the standard error computation, we use soft-labels (i.e., probability distributions over the annotator labels) as an auxiliary task in a multi-task neural network. We measure the divergence between the predictions and the target soft-labels with several loss-functions and evaluate the models on various NLP tasks. We find that the soft-label prediction auxiliary task reduces the penalty for errors on ambiguous entities, and thereby mitigates overfitting. It significantly improves performance across tasks, beyond the standard approach and prior work.
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