A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences
TABLE II: Accuracies (%) of using different models on SNLI.
['Models', 'Train', 'Test']
Here, the proposed method using SNELSD models was compared with other published state-of-the-art methods on the benchmark SNLI task. All the models compared here utilized the framework of word-by-word attention between the semantic representation of two sentences. The methods marked with (1)-(7) adopted a single model for NLI, and the methods marked with (8)-(10) ensembled multi-models for decision making. The most obvious difference between them is that the decomposable attention model employs feedforward neural networks, whereas the EBIM model uses BLSTM-RNNs to encode the sentences and to compose the inference information. The models marked with (6) and (7) utilized joint-mode SNELSD in an EBIM and ESIM fashion, respectively. When combining joint-mode SNELSD with ESIM, the outputs of SNELSD and 1-layer BLSTM-RNN were concatenated after inference collection and dimension reduction. It can be observed that models (6) and (7) achieved higher NLI accuracies than their counterparts using 1-layer BLSTM-RNN for sentence modeling. A close examination on the test set errors made by models (4) and (7) shows that about 3.5% test set samples were misclassfied by model (4) while classified correctly by model (7). On the contrary, about 3.2% test set samples were misclassfied by model (7) while classified correctly by model (4). More than 70% of the errors made by these two models were the same. This implies that there still exist common deficiencies with these two models. We also observed that some of the common errors made by these two models were due to the lack of background and commonsense knowledge during the inference.