Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
Table 5: Performance of different models on matching natural language sentences. Results with ∗ are for Bidirectional LSTM, reported in Williams et al. (2017). Our reported results on MultiNLI are only trained MultiNLI training set (without training data from SNLI). For MSRP dataset, we follow the setup in Hu et al. (2014) and do not use any additional features.
['[BOLD] Model', '[BOLD] SNLI', '[BOLD] MultiNLI  [BOLD] Matched', '[BOLD] MultiNLI  [BOLD] Mismatched', '[BOLD] WikiQA', '[BOLD] WikiQA', '[BOLD] Quora', '[BOLD] MSRP', '[BOLD] MSRP']
To gain a deeper understanding regarding the modeling capacity of word embeddings, we further investigate the problem of sentence matching, including natural language inference, answer sentence selection and paraphrase identification. Surprisingly, on most of the datasets considered (except WikiQA), SWEM demonstrates the best results compared with those with CNN or the LSTM encoder. As a result, with only 120K parameters, our SWEM-max achieves a test accuracy of 83.8%, which is very competitive among state-of-the-art sentence encoding-based models (in terms of both performance and number of parameters)