TACAM: Topic And Context Aware Argument Mining
Table 2. Cross-Topic
['[EMPTY]', 'Method', 'Topics Abortion', 'Topics Cloning', 'Topics Death penalty', 'Topics Gun control', 'Topics Marij. legal.', 'Topics Min. wage', 'Topics Nucl. energy', 'Topics School unif.', '\\diameter']
In this experiment, which reflects a real-life argument search scenario, we want to prove our two hypotheses: When classifying potential arguments, it is advantageous to take information about the topic into account. The context of an argument and topic context are important for the classification decision. On the whole, we can see that our two hypotheses are confirmed. In the two-classes scenario the recurrent model improves if topic information is provided by knowledge graph embeddings. By using attention-based models with pre-trained weights we can observe a significant performance boost of eleven score points in average when considering topic information. However, the same model without topic information performs only slightly better than the recurrent models. Therefore, we conclude that both, topic information together with contexts of topic and argument, are important for the correct decision about a potential argument. We observe similar effects in the three-classes scenario. Although in average different contexts for the recurrent model have a similar effect, we can clearly observe that taking topic information into account improves classification results by one score points. The combination of transfer learning for context and topic information again outperforms all other approaches by far. At the same time, the pre-trained model without topic information achieves a macro-f1 score of 0.61 which is 3 points lower than with topic information.