# Aspect Sentiment Triplet Extraction Based on Deep Relationship Enhancement Networks
## 4 Experimental Process
### Comparison Experiment  
To assess the effectiveness of the model of DREN in this paper, the Peng-two-stage-IOG [27], GTS-CNN/GTS-BiLSTM/GTS-BERT [28], S\({}^{3}\)E\({}^{2}\)[20], CMLA+ [29], RINANTE [29], Li-unified-R [29], OTE-MTL [30], and JET-BERT [25] are chosen for comparison, in which CMLA+, RINANTE+, Li-unified-R, and Peng-two-stage are Pipeline methods, while GTS-CNN/GTS-BiLSTM/GTS-BERT, S\({}^{3}\)E\({}^{2}\), OTE-MTL, and JET-BERT are End-to-End models. The following is a description of several baselines:  
Peng-two-stage-IOG: It divides the triplet extraction task into two stages. In the first stage, BiLSTM and GCN are used to predict the possible descriptive aspect words and opinion words; then, in the second stage, the aspect words and opinion words predicted in the first stage are paired.  
GTS-CNN/GTS-BiLSTM/GTS-BERT: It defines six kinds of word relationships, which are encoded via CNN/BillSTM/BERT and attention mechanism on the encoder. After pooling and some linear transformation operations, the SoftMax classifier is used to classify the word relationships.  
S\({}^{3}\)E\({}^{2}\): In order to solve the long tail problem in the extraction of aspect words and opinion words in review texts, the SoftProto framework makes samples related to each other, thus allowing rare words to be extracted.  
CMLA: It considers that the extractions of aspect words and opinion words need to be correlated with each other and proposes using SWDA to correlate aspect words and opinion words. Each layer of attention mechanism is composed of a pair of attention mechanisms, one of which extracts aspect words and one of which extracts opinion words. Through interactive learning, the extraction performance is improved.  
RINANTE: It proposes to use BiLSTM-CRF to train the data, which is composed of two parts: one is manually labeled data, and the other is automatically labeled based on dependency syntax and Part-of-Speech annotators.  
Li-unified-R: It uses a two-layer recurrent neural network to process the extraction task. The first layer of the recurrent neural network is used to generate labeled results, and the second layer is used to label the target boundary.  
OTE-MTL: It proposes a multi-task learning framework for joint extraction of attributive words and opinion words while using bilinear affine attention mechanism to record the sentiment dependency between words.  
JET-BERT: It believes that attributive words, opinion words, and polarity judgments need to be extracted in End-to-End, rather than through multiple stages. The paper proposes using two kinds of location information to improve the extraction accuracy of recognition: one is to mark the distance between the sentiment words and attributive words, with attributive words as the center, and the other is to calculate the distance between attributive words and sentiment words, with sentiment words as the center.  
There are comparative experimental baseline and DREN results for two datasets in Tables 3 and 4. Some baseline results can be found in [12; 25; 30; 31].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **14 res** & **14 lap** & **15 res** & **16 res** \\ \hline D1\_train & 1452 & 2356 & 1038 & 1421 \\ \hline D1\_valid & 383 & 580 & 239 & 348 \\ \hline D1\_test & 547 & 1008 & 493 & 525 \\ \hline D2\_train & 1460 & 2338 & 1013 & 1394 \\ \hline D2\_valid & 346 & 577 & 249 & 339 \\ \hline D2\_test & 543 & 994 & 485 & 514 \\ \hline \end{tabular}
\end{table}
Table 2: Statistics regarding the number of aspect words in each dataset.