Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Social Web Data for Emergency Services
Table 4. Performance comparison (% Accuracy) of the standard single-task model with state-of-the-art models on the standard benchmark dataset of amazon reviews. ♠: Deep learning architecture used in exisiting crisis-related work (Nguyen et al., 2016; Alam et al., 2018). ♡: reported scores from (Li et al., 2018b). ♣: (reproduced implementation) Current top performing model for cross domain sentiment classification (Li et al., 2018b). ♢: Standard single-task attention network (Krishnan et al., 2020). Note that AMN and HATN in the table use unlabeled target data for domain adversarial training.
['S → T', 'CNN♠', 'BiLSTM', 'AMN♡', 'HATN♣', 'BiLSTM+ATT♢']
CNN: A standard Convolutional Neural Network is constructed with the following architecture: Word Embeddings(Tx,300)→Conv1D(128,5) →MaxPooling1D(5) →Conv1D(128,5)→MaxPooling1D(5) →Conv1D(128,5)→GlobalMaxPooling1D()→Dense(128) →Dense(2)→y. This is combined with dropouts, relu activations, and ending with softmax activation producing labels for binary classification. State-of-the-art BiLSTM a  passed through the following: Dense(10)→Dense(2)→y also including dropouts, relu activation, and ending with softmax. AMN and HATN: AMN ( HATN is an extension to AMN by adding the hierarchical component and jointly training a pivot and a non-pivot networks. This evaluation on amazon reviews is conducted to show the single-task model’s universal usability for domain adaptation of text classification tasks in general and not just crisis data. This shows how well it can perform when compared with the existing top-performing benchmark models on standardized datasets. HATN uses unlabeled target data, gradient reversal, explicit pivot extraction, and joint training making it a computationally expensive method. As shown in the experimental evaluation, we use the same Amazon dataset and GoogleNews word vectors for our experiments. Our baseline model, being unsupervised with no need of unlabeled target data, performed competitively with an overall accuracy of 85.02%.