Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning
Table 6: Test accuracy on CKBC triple classification task..
['[BOLD] Method', '[BOLD] Dev2', '[BOLD] Test']
Finally, we evaluate our approach on the CKBC task, which should directly benefit from commonsense knowledge. In addition, we remove raw sentences belonging to CKBC’s test set from our GLM training corpora to avoid data leakage. There are two possible reasons why performance actually drops with more data: 1) the training triples are sorted w.r.t annotated confidence, so additional triples have a lower quality and may introduce noise; 2) more negative sampling must be done with more training triples, which introduces more false negative examples (from 1.25% to 2.42%).