# An End-to-End Mutually Interactive Emotion-Cause Pair Extractor via Soft Sharing
## 4 Results
### Baselines and Settings  
We include four baseline methods in our comparison with Emiece: the original two-step model ECPE [12], a relatively lightweight end-to-end model E2E-PExtE [13], and two state-of-the-art ECPE models ECPE-2D(BERT) [14] and ECPE-MLL(ISML-6) [15].  
* ECPE [12]: As a second step, a Cartesian product is applied to the emotion clauses and causal relationships extracted from the multi-task learning network in the first step in order to compose them into pairs, and a filtering model is trained so that the pair containing the causal relationship is the final output. Bi-LSTM and attention [47; 48] is the word-level encoder used in the first extraction step, and Bi-LSTM [48] is used in the emotion and cause extractors as well. Logic regression is used to filter the pairs in the second step.
* ECPE-2D(BERT) [14]: The interactions in the emotion-cause pairs were modeled by a 2D transformer, which in turn represented the pairs in a two-dimensional form, i.e., a square matrix. Two-dimensional representations are integrated with interactions and predictions using a joint framework. The encoding part uses a word-level Bi-LSTM and an attention mechanism [47], while the clause-level emotion extractor and cause extractor leverage BERT [49] to enhance the overall effectiveness of the model.
* ECPE-MLL(ISML-6) [15]: Multi-label learning (MLL) was introduced in the ECPE task. To obtain a representation of the clause, the emotion clause and cause clause are specified as the center of the multi-label learning window. An iterative synchronous multi-task learning (ISML) model with six iterations is used for clause encoding, while the same Bi-LSTM [47] is used for word-level embedding.
* E2E-PExtE [13]: Using Bi-LSTM plus attention [47], the clause-level representation is obtained based on the word-level one. The clause level representation uses another Bi-LSTM network to further extract contextual information and is used to determine whether the clause is an emotion one or a cause one. Finally, the predicted pair is obtained by a fully connected neural network.  
We denote Emiece-LSTM that using as the stacked Bi-LSTM [48] clause-level encoder. Emiece-LSTM is trained for 30 epochs using the Adam optimizer [53]. We set the learning rate \(\alpha\) = 0.005, and batch size \(N\) = 64. The model parameters \(\phi\) are initialized randomly following uniform distribution \(\phi\sim U(-0.1,0.1)\). We leverage GloVe word embedding [54] of 200 dimensions. The dropout rate is set to 0.8 for word embeddings and \(\ell_{2}\) decay is set to \(10^{-5}\) on softmax parameters. The loss weights are set as \(\lambda_{e}\):\(\lambda_{c}\):\(\lambda_{p}\):\(\lambda_{sf}\) = 1:1:2:5:0.75 and the negative pair weight \(\lambda_{-}\) = 0.4. Here, the value of \(\lambda_{-}\) is taken with reference to the work of [13], and we have made many attempts to take the value of \(\lambda_{sf}\), as shown in Figure 4. As a result of setting \(\lambda_{sf}\) to 0.75, the model performs best in combination at validation, i.e., it performs best on the more important F1-score metric and relatively well on the other two metrics. We also denote Emiece-BERT with the clause-level encoder setting to BERT [49]. Constrained training server performance, we set the batch size of Emiece-BERT to 16 and vary the learning rate by 2 \(\times\)\(10^{-5}\), keeping other hyperparameters the same.  
In order to achieve higher model performance through better positional embeddings, randomly initialized embeddings are trained after setting the clipping distance [50] to 10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Annotated Emotion** & **\# Corresponding Emotion Clause** \\ \hline happiness & 741 \\ surprise & 388 \\ sadness & 638 \\ fear & 622 \\ anger & 269 \\ disgust & 214 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The distribution of emotion clauses corresponding to the six annotated emotions in the dataset.