Question: What are the advantages of the author's method over others'?
Evidence: The purpose of text classification is that, given a sentence $x$ , the model should predict labels $\hat{y}$ from a pre-defined label set $\mathcal {Y}$ . From the description in the previous section, we can compute the distributed representation $\mathbf {h}_j$ of the phrase at node $j$ of a tree: 
$$\mathbf {h}_{j} &= \operatornamewithlimits{DC-TreeNN}(\mathbf {x}_j, \mathbf {h}_{j}^l, \mathbf {h}_{j}^r,\theta )$$   (Eq. 18) 
 After this recursive process, the hidden state $\mathbf {h}_R$ at the root node is used as the sentential representation, which then followed by a softmax classifier to predict the probability distribution over classes. 
$${\hat{\mathbf {y}}} = \operatornamewithlimits{softmax}(\mathbf {W}_t \mathbf {h}_R + \mathbf {b}_t)$$   (Eq. 19) 
 where ${\hat{\mathbf {y}}}$ is prediction probabilities, $\mathbf {W}_t$ and $\mathbf {b}_t$ are the parameters of the classifier.
We evaluate our models on five different datasets. The detailed statistics about the five datasets are listed in Table 2 . Each dataset is briefly described as follows.
SST The movie reviews with two classes (negative, positive) in the Stanford Sentiment Treebank BIBREF23 .
MR The movie reviews with two classes BIBREF24 .
QC The TREC questions dataset involves six different question types. BIBREF25 .
SUBJ Subjectivity dataset where the goal is to classify each instance (snippet) as being subjective or objective. BIBREF26 
IE Idiom enhanced sentiment classification. BIBREF27 . Each sentence contains at least one idiom.
As shown in Table 3 , DC-TreeLSTM consistently outperforms RecNN, MV-RecNN, RNTN, and TreeLSTM by a large margin while achieving comparable results to the CNN and using much fewer parameters.(The number of parameters in our models is approximately 10K while in CNN the number of parameters is about 400K). Compared with RecNN, DC-RecNN performs better, indicating the effectiveness of the dynamic compositional function. Additionally, both DC-RecNN and DC-TreeLSTM achieve substantial improvement on IE dataset, which covers the richness of compositionality (idiomaticity). We attribute the success on IE to its power in modeling more complicated compositionality.
As shown in Table 3, DC-TreeLSTM consistently outperforms RecNN, MV-RecNN, RNTN, and TreeLSTM by a large margin while achieving comparable results to the CNN and using much fewer parameters.(The number of parameters in our models is approximately 10K while in CNN the number of parameters is about 400K). Compared with RecNN, DC-RecNN performs better, indicating the effectiveness of the dynamic compositional function. Additionally, both DC-RecNN and DC-TreeLSTM achieve substantial improvement on IE dataset, which covers the richness of compositionality (idiomaticity).
Answer: As shown in Table 3, DC-TreeLSTM consistently outperforms RecNN, MV-RecNN, RNTN, and TreeLSTM by a large margin while achieving comparable results to the CNN and using much fewer parameters.Compared with RecNN, DC-RecNN performs better, indicating the effectiveness of the dynamic compositional function. Additionally, both DC-RecNN and DC-TreeLSTM achieve substantial improvement on IE dataset, which covers the richness of compositionality (idiomaticity).