# Improving Sentence Representations via Component Focusing
## 4 Experiment
### Evaluation--Entailment Classification  
To evaluate the broader utility of our sentence representations, we leverage them for a different application: the SemEval-2014 textual entailment task (SICK-E). For this task, we solely rely on the same representations learned for predicting semantic textual similarity (SICK-R), and simply apply standard learning methods to do the entailment classification. From the sentence representations emb_SA, emb_SB of each pair of sentences, we first pass the pooling_model to get sentence embeddings U, V of sentence A and B, we then compute the following simple feature: element-wise (absolute) differences \(|\text{U}-\text{V}|\). Using only this feature label, we train a logistic regression to classify the entailment labels. The logistic regression classifier is trained on various tasks in a 10-fold cross-validation setup  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline  & \multicolumn{2}{c}{**SICK-R**} & \multicolumn{2}{c}{**STS-B**} \\ \cline{3-6}  & \multicolumn{1}{c}{**Models**} & \multicolumn{1}{c}{\(r\)} & \multicolumn{1}{c}{\(\rho\)} & \multicolumn{1}{c}{\(r\)} & \multicolumn{1}{c}{\(\rho\)} \\ \hline Top Four & \multicolumn{1}{c}{ECNU\_run1} & 82.8 & 76.89 & & \\ SemEval-2014 & StanfordNLP\_run5 & 82.72 & 75.59 & & \\ submissions for & \multicolumn{1}{c}{The\_Meaning\_Factory\_run1} & 82.68 & 77.22 & & \\ SICK-R [40] & \multicolumn{1}{c}{UNAL-NLP\_run1} & 80.43 & 74.58 & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Recently \\ methods \\ (2015-2018) \\ \end{tabular} } & Avg. GloVe embeddings [2] & 69.43 & 53.76 & 61.23 & 58.02 \\  & Avg. BERT embeddings [28; 29] & 71.4 & 58.4 & 51.17 & 46.35 \\  & Universal Sentence Encoder (USE) [27] & 82.65 & 76.69 & 75.53 & 74.92 \\  & USE (with CF \(W_{df}=0.2\)) (our) & 83.51 & 77.38 & 76.22 & 75.54 \\  & Skip-thought [23] & 85.84 & 79.16 & 77.1 & 76.7 \\  & Infersent [24] & 88.4 & 83.1 & 75.8 & 75.5 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} BERT-based \\ methods \\ \end{tabular} } & BERTBASE [12] & 87.9 & 83.3 & 87.1 & 85.8 \\  & SERETBASE [31] & 87.81 & 83.26 & 87.04 & 85.76 \\  & CF-BERTBASE (with CF \(W_{df}=0.2\)) (our) & 88.39 & 83.77 & 87.58 & 86.29 \\ \cline{1-1} \cline{2-6}  & BERT LARGE [12] & 88.9 & 84.2 & 87.6 & 86.5 \\ \cline{1-1}  & SERET LARGE [31] & 88.78 & 83.93 & 87.41 & 86.35 \\ \cline{1-1}  & CF-BERT LARGE (with CF \(W_{df}=0.2\)) (our) & 89.29 & 84.35 & 87.83 & 86.82 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test set Pearson correlations (\(r\)) and Spearman correlations (\(\rho\)) for STS task.  
and the prediction accuracy is computed for the test-fold. The results are depicted in Table 6. Accuracy (*100) is reported for the results. The first group of results are the top four SemEval-2014 submissions for SICK-E, the second are more recently proposed methods (including average methods), and the third is BERT-based methods. The results in black bold represent the best of all methods.  
Table 6 shows that all methods with the addition of component focus have improved performance compared to methods without component focusing. USE improves performance with around 0.8 points, CF-BERTBASE improves performance with about 0.5 points, and CF-BERTLARGE improves performance with about 0.4 points. The performance of CF-BERT is slightly better than that of BERT; CF-BERTLARGE outperforms all other textual-entailment systems.