# Contradiction Detection with Contradiction-Specific Word Embedding
## 4 Experiments
### Results and Analysis  
We first compare different methods for contradiction detection task, then make a comparison among different embedding by incorporating in our model. Given a pair of sentences, we aim to predict the correct semantic relation between them. The semantic relations include: entailment, contradiction and neutral. The ideal results are that the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Train** & **Trial** & **Test** \\ \hline Contradiction & 665 & 74 & 720 \\ Entailment & 1300 & 144 & 1414 \\ Neutral & 2536 & 282 & 2793 \\ All & 4501 & 500 & 4927 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of the dataset in SemEval 2014 task1. model can improve the accuracy of identifying the contradiction category on the premise that the three-category accuracy is at least comparable with the top-performing system. We compare the proposed method with baseline methods on test data, and the results are shown in Table 4. The comparison results show that our method gains the highest accuracy on the contradiction category. Illinois-LH system gains the best result on the average accuracy of three categories. Our method has a comparable result to the Illinois-LH system on the average accuracy of all categories. The state of the art method, Illinois-LH, applied feature engineering and a MaxEnt model to do the task. Specifically, it made deep semantic and syntactic analyses and recognized antonyms by lexical resources. However, most of contradictory words or phrases in the dataset are not antonyms and are hard to be recognized by lexical resources. Our method uses a neural network based model to learn the representation of the semantic relation between input sentences, and treat the representation as features in the classification. The model incorporates the contradiction-specific word embedding. The advantage of our method is the capability to recognize the contradictory meanings between input sentences. The model consists of two parts, which respectively represent the semantic relation between pairs of sentences and the semantic relation between pairs of unaligned phrases among the sentence pairs. The designing of the model also benefits the contradiction detection task. TreeRNN outperforms the LSTM-RNN models; however, it cannot beat other methods. The results of the SVM methods show that CWE is significantly beneficial, not only in detecting contradiction but also in the three-category classification. The LSTM-RNN model has a more than 90% accuracy on the training data and 76% in the test data. LSTM-RNN does not perform very well on the current dataset because of overfitting on the training data. LSTM-RNN has more parameters than other baseline methods and the proposed method, which needs a larger training dataset to avoid the overfitting problem. Our proposed method is used by incorporating CWE and shallow features. The accuracy of the contradiction category is 80.28%, which is far higher than 75.97% of the Illinois-LH system. When we fine-tune CWE during training the proposed model, the average accuracy of all categories and the accuracy of the contradiction category both improve. The final accuracy on the contradiction category is 82.08%. The effectiveness of CWE and three shallow features is analyzed in Table 5. The accuracies clearly decrease when CWE is abandoned. This shows the effectiveness of CWE in representing semantic relations. When we abandon CWE, the model actually turns to be a softmax classifier with shallow features. Thus, the comparison between CWE and other features just show the effectiveness of the proposed model to learn semantic relation representation of the sentence pairs. Because the negation phenomenon has a high coverage in the whole dataset, the negation feature is also useful. The two