Multi-label Categorization of Accounts of Sexism using a Neural Framework
Table 3: Results for the proposed methods and deep learning baselines (using ELMo embeddings) with the EBCE loss and Random
['[EMPTY]', 'Approach', '[ITALIC] FI', '[ITALIC] Fmacro', '[ITALIC] AccI', '[ITALIC] Fmicro']
For each method, the average over three runs is reported for each metric. We find ELMo to be better than GloVe and fastText for word embeddings across multiple baselines and hence show only ELMO-based results for the baselines. We report all results with the EBCE loss; the NCE loss produced inferior results across multiple methods. For our framework, s() denotes sentence-level concatenation; wl() denotes word level concatenation and LSTM-based processing; wc() denotes word level concatenation and CNN-based processing. We note that the results are reported for only some of the many instances that can arise from our configurable architecture. Our framework provides the ability to explore different configurations such as those with multiple s() operations, depending on the problem at hand.