TACAM: Topic And Context Aware Argument Mining
Table 4. Topic dependent cross-topic classification results
['[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']
For the two-classes problem we observe a massive performance drop of ten points in macro-f1 score for the BiCLSTM model. Nonetheless, the model still makes use of topic information and outperforms the standard BiLSTM by two macro-f1 score points. Our approach TACAM-BERT Base is more robust, the performance falls by moderate four score points and the gap to the counterpart model without topic information is incredible 17 score points large. We observe a similar behaviour in the three-classes scenario. Our TACAM-BERT Base approach achieves the same average score as in the original cross topic task. In contrast the performance of the BiCLSTM model drops by 11 score points and it even performs worse than the same model without topic information on this more complex task. Thus we conclude that unlike previous models our approaches are indeed able to grasp the context of the argument and topic and are able to relate them with each other.