Research Paper
Table 3. Classification accuracies and F1-Scores for news arcticle classifications for different source and target domains. The first row corresponds to the baseline performance trained on only the target dataset. The following two rows shows the performance of instance-infusion method with and without the usage of penalty function. In all the three cases, our approach outperforms the baselines by a significant margin.
['[BOLD] METHOD', 'Target Source', 'News20 BBC', 'News20 BBC', 'BBC News20', 'BBC News20', 'BBC Sports BBC', 'BBC Sports BBC']
The source and target datasets are chosen in a manner such that the source dataset is able to provide relevant information. 20Newsgroups contains news articles from all categories, so a good choice for source dataset is BBC which also encompasses articles from similar broad categories. For the same reason BBC also has 20Newsgroups as its source dataset. BBC Sports focuses on sports articles, BBC is chosen as the source dataset as the news articles share a common domain (articles come from same news media BBC). For the proper functioning of the model, the final layer of the instance-infused model is replaced while the rest of the network is inherited from the pre-trained target only model. For 20Newsgroups the improvement over baseline model is 12%, BBC and BBC Sports datasets show an improvement of around 5%. As mentioned earlier, our approach is independent of the sentence encoder being used. Instead of bi-LSTM any other model can be used. As the proposed approach is independent of the source encoding procedure and the source instance embeddings are kept constant during the training procedure, we can incorporate source instances from multiple datasets simultaneously. In the subsequent experimental setups, we try varying setups to prove the robustness and efficacy of our proposed model.