Efficient Sentence Embedding via Semantic Subspace Analysis
TABLE I: Experimental results on textual similarity tasks in terms of the Pearson correlation coefficients (%), where the best results for parameterized and non-parameterized are in bold respectively.
['Model', 'Dim', 'STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STSB', 'SICK-R', 'Avg.']
Recent parameterized method SBERT-WK provides the best performance and outperforms other method by a large margin. S3E method using L.F.P word embedding is the second best method in average comparing with both parameterized and non-parameterized methods. As mentioned, our work is compatible with any weight-based methods. With better weighting schemes, the S3E method has a potential to perform even better. Therefore, choosing more powerful word embedding models can be helpful in performance boost.