## 5 Experiments
### Experimental Results
#### 5.6.2 Results of Clause Parsing  
We employed the bottom-up and top-down discourse parsers in [18] as our clause-parsing models and conducted experimental trials of the base version of different pretrained language models (PLMs, i.e., BERT [39], RoBERTa [40], SpanBERT [41], XLNet [42], and DeBERTa [43]) to obtain better performances. Table 11 demonstrates the main results of both the bottom-up and top-down models on clause parsing, as well as that on discourse parsing for comparison. From the results, we have the following observations:  
* Better performances were obtained in clause parsing than in discourse parsing by either top-down or bottom-up parsers with whatever PLMs.
* The best performances of clause parsing were obtained by the bottom-up parser with the pretrained DeBERTa, where the performance reached up to 97.0 F\({}_{1}\)-scores on _Parseval-Span_ and fell to 87.7 F\({}_{1}\)-scores on _Parseval-Full_.
* All the best performances in either clause parsing or discourse parsing were obtained by parsers with pretrained XLNet and DeBERTa, indicating that these two PLMs are more suitable for relation classification tasks than other PLMs.
* Experimenting with a same model and the same pretrained language model, the performances on clause parsing had about 6-9 _Parseval-Full_ scores, higher than that on discourse parsing, indicating that the performance gaps could be attributed to the differences between corpora. RST-DT contains 18 classes of relations partitioned from 78 types of blurry rhetorical relations, while HCA has 18 types of distinguishable semantic relations, half of which were subdivided from _adverbial_.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Task** & **Dataset** & **Model** & **P** & **R** & **F\({}_{1}\)** \\ \hline \multirow{8}{*}{Discourse Segmentation} & \multirow{3}{*}{GUM} & GumDrop [38] & 96.5 & 90.8 & 93.5 \\  & & - _all feats._* & **97.7** & 87.4 & 92.3 \\ \cline{3-5}  & & DisCoDisCo [17] & 93.9 & 94.4 & **94.2** \\  & & - _all feats._* & 92.7 & **92.6** & 92.6 \\ \cline{2-5}  & \multirow{3}{*}{STAC} & GumDrop [38] & 95.3 & **95.4** & **95.3** \\  & & - _all feats._* & 85.0 & 76.7 & 80.6 \\ \cline{3-5}  & & DisCoDisCo [17] & **96.3** & 93.6 & 94.9 \\  & & - _all feats._* & 91.8 & 92.1 & 91.9 \\ \cline{3-5}  & \multirow{3}{*}{RST-DT} & GumDrop [38] & 94.9 & 96.5 & 95.7 \\  & & - _all feats._* & 96.3 & 94.6 & 95.4 \\ \cline{3-5}  & & DisCoDisCo [17] & 96.4 & **96.9** & **96.6** \\  & & - _all feats._* & **96.8** & 95.9 & 96.4 \\ \hline \multirow{8}{*}{Clause Segmentation} & \multirow{3}{*}{HCA-AMR2.0} & DisCoDisCo & **92.9** & 89.7 & **91.3** \\  & & - _lem._\({}^{\circ}\) & 86.8 & **93.9** & 90.2 \\ \cline{1-1}  & & - _dp._\({}^{\circ}\) & 91.0 & 87.7 & 89.3 \\ \cline{1-1}  & & - _pos_\({}^{\circ}\) & 91.4 & 87.6 & 89.4 \\ \cline{1-1}  & & - _all feats._\({}^{\circ}\) & 89.2 & 85.4 & 87.2 \\ \cline{1-1}  & & - _fastText_ & 90.5 & 82.7 & 86.4 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performances of the adapted DisCoDisCo model on HCA-AMR2.0 for clause segmentation, and performances of DisCoDisCo and GumDrop on three datasets for the contrastive task, discourse segmentation. Note that * and \({}^{\circ}\) indicate gold annotated features from the corresponding dataset and silver features annotated by Stanza, respectively. **Bold** numbers are the best scores on each dataset. All the experiments on the clause segmentation task were conducted for five runs with different seeds, and the experimental results were averaged.  
To obtain a clause parser with better performances, we conducted experimental trials with the large versions of the PLMs, and the results are illustrated in Table 12. As can be observed from the results:  
* All the parsers with the corresponding large PLMs performed better than with the base PLMs.
* The bottom-up parser with DeBERTa-large achieved a better performance, with a 0.8 F\({}_{1}\)-score improvement on _Parseval-Full_ over the parser with DeBERTa-base.