Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Table 8: Test accuracies with different compositional functions on (short) sentence classifications.
['[BOLD] Model', '[BOLD] MR', '[BOLD] SST-1', '[BOLD] SST-2', '[BOLD] Subj', '[BOLD] TREC']
We now consider sentence-classification tasks (with approximately 20 words on average). We experiment on three sentiment classification datasets, i.e., MR, SST-1, SST-2, as well as subjectivity classification (Subj) and question classification (TREC). Compared with CNN/LSTM compositional functions, SWEM yields inferior accuracies on sentiment analysis datasets, consistent with our observation in the case of document categorization. However, SWEM exhibits comparable performance on the other two tasks, again with much less parameters and faster training. Further, we investigate two sequence tagging tasks: the standard CoNLL2000 chunking and CoNLL2003 NER datasets. Results are shown in the Supplementary Material, where LSTM and CNN again perform better than SWEMs. Generally, SWEM is less effective at extracting representations from short sentences than from long documents. This may be due to the fact that for a shorter text sequence, word-order features tend to be more important since the semantic information provided by word embeddings alone is relatively limited.