论文标题
印地语/孟加拉人情绪分析使用转移学习和共同双重输入学习以自我关注
Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self Attention
论文作者
论文摘要
情感分析通常是指使用自然语言处理，文本分析和计算语言学来从文本数据中提取基于情感和情感的信息。我们的工作探讨了如何在转移学习和联合双重输入学习环境中有效地使用深层神经网络，以有效地对情感进行分类并检测到印地语和孟加拉语数据中的仇恨言论。我们首先训练word2vec word嵌入textbf {hasoc dataset}和孟加拉仇恨言论，然后训练LSTM，然后训练基于参数的转移转移学习，通过重复使用和调查训练的训练的培训的分类器，并将基于两个分类者的训练的训练的分类器与我们的基础纳入我们的研究。最后，我们在联合双输入学习环境中使用Bilstm自我关注，在该环境中，我们同时使用其各自的嵌入方式训练印地语和孟加拉数据集的单个神经网络。
Sentiment Analysis typically refers to using natural language processing, text analysis and computational linguistics to extract affect and emotion based information from text data. Our work explores how we can effectively use deep neural networks in transfer learning and joint dual input learning settings to effectively classify sentiments and detect hate speech in Hindi and Bengali data. We start by training Word2Vec word embeddings for Hindi \textbf{HASOC dataset} and Bengali hate speech and then train LSTM and subsequently, employ parameter sharing based transfer learning to Bengali sentiment classifiers by reusing and fine-tuning the trained weights of Hindi classifiers with both classifier being used as baseline in our study. Finally, we use BiLSTM with self attention in joint dual input learning setting where we train a single neural network on Hindi and Bengali dataset simultaneously using their respective embeddings.
