论文标题
Muboost：求解的有效方法指示多语言文本分类问题
muBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem
论文作者
论文摘要
文本分类是许多自然语言处理任务的组成部分，例如讽刺检测，情感分析和更多此类应用。许多电子商务网站，社交媒体/娱乐平台都使用此类模型来增强用户体验以产生流量，从而在其平台上收入。在本文中，我们将在Sharechat提供的印度视频共享社交网络服务Moj上介绍了多语言滥用评论标识问题。该问题涉及在MOJ平台上的视频上使用13种区域性指示语言（例如印地语，泰卢固语，卡纳达语等）中检测滥用评论的问题。我们的解决方案利用新颖的Muboost，这是印度语言模型（MURIL）模型的Catboost分类器模型和多语言表示的合奏，以在指示文本分类任务上产生SOTA性能。我们能够在测试数据上达到平均F1分数为89.286，这比基线Muril模型的改进，F1分数为87.48。
Text Classification is an integral part of many Natural Language Processing tasks such as sarcasm detection, sentiment analysis and many more such applications. Many e-commerce websites, social-media/entertainment platforms use such models to enhance user-experience to generate traffic and thus, revenue on their platforms. In this paper, we are presenting our solution to Multilingual Abusive Comment Identification Problem on Moj, an Indian video-sharing social networking service, powered by ShareChat. The problem dealt with detecting abusive comments, in 13 regional Indic languages such as Hindi, Telugu, Kannada etc., on the videos on Moj platform. Our solution utilizes the novel muBoost, an ensemble of CatBoost classifier models and Multilingual Representations for Indian Languages (MURIL) model, to produce SOTA performance on Indic text classification tasks. We were able to achieve a mean F1-score of 89.286 on the test data, an improvement over baseline MURIL model with a F1-score of 87.48.
