Segmenting Scientific Abstracts into Discourse Categories
Table 6. Summary of ablation analysis.
['Dataset', 'Accuracy']
We observe that the abstract processing step, with the bidirectional LSTM to extract the conceptual information from the sentences, contributes the greatest toward the model’s performance. Embeddings, with their capability to represent the contextual relationship between the tokens, has the second-highest contribution. The CRF in the output generation layer also has a major impact on the model’s performance. This layer takes advantage of the sequential dependency in the output label sequence. This ties into the poor performance on the cs. NI dataset, as the sequential information is somewhat lacking in that dataset.