# Fine-Grained Sentiment-Controlled Text Generation Approach Based on Pre-Trained Language Model
## 4 Experiments
### Generated Quality Evaluation
#### 4.3.2 Sentiment Evaluation  
As to measure the quality of sentiment containment in the generated sentence and indicate whether the input sentiments are correctly expressed in the generated text, we employ two metrics: **Coverage** (Cov. ), just like in [PERSON] et al. [46], which is the average rate of input sentiment pairs presented in the generated texts. This metric includes Cov-a, Cov-o, and Cov-ao, representing the presenting rate of aspect, opinion, and aspect-opinion pairs, respectively. **Accuracy** (Acc.) is a rate indicating how many fine-grained sentiments are accurately expressed in the sentence, and it is evaluated by the external sentiment classifier [30] trained on MAMS-ASTA. Table 3 shows the results of sentiment coverage and accuracy for generated texts. It is worth noting that for a linguistically complicated sentence, its aspect-level sentiments are more difficult to be correctly predicted by the external classifier than a relatively simple sentence, so its sentiment accuracy may be lower than the actual situation. What is more, T5's original seq2 seq architecture allows it to generate texts that highly correspond to \begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline
**Dataset** & **Models** & **BLEU-3 (\uparrow)** & **BLEU-4 (\uparrow)** & **METEOR (\uparrow)** & **ROUGE-1 (\uparrow)** & **Self-Bleu-4(\downarrow)** & **Dist-1 (\uparrow)** & **Dist-2 (\uparrow)** & **Dist-3 (\uparrow)** \\ \hline \multirow{8}{*}{ASETE-Data-V2} & PPLM & 0.196 & 0.032 & 14.078 & 13.827 & **7.939** & 0.0841 & 0.4102 & 0.7180 \\ \cline{2-10}  & HTT & 13.100 & 7.656 & 34.899 & 42.544 & 42.664 & 0.0525 & 0.2356 & 0.4113 \\  & T-base & 21.246 & 13.216 & 29.007 & 41.092 & 22.580 & 0.1621 & 0.4725 & 0.6101 \\  & T5-large & 24.747 & 16.462 & 29.986 & 43.614 & 23.045 & **0.1721** & 0.4658 & 0.934 \\  & UniLM & 33.093 & 27.486 & 46.808 & 52.582 & 20.334 & 0.1489 & 0.4961 & 0.6663 \\  & BERT-Gen & 32.693 & 28.050 & 45.225 & 45.162 & 24.149 & 0.1450 & 0.457 & 0.6411 \\  & UniLM-v2 & 32.159 & 27.525 & 45.107 & 44.514 & 22.830 & 0.1451 & 0.5060 & 0.6553 \\ \cline{2-10}  & AlSeCond & **40.453** & **34.611** & 55.127 & **63.720** & 15.972 & 0.1610 & **0.5439** & 0.7073 \\  & w/o sentiment loss & 32.961 & 32.190 & **58.499** & 62.911 & 16.195 & 0.1525 & 0.5301 & 0.7028 \\  & w/o query-shirt & 34.305 & 29.080 & 55.391 & 61.237 & 14.442 & 0.1551 & 0.5431 & **0.7264** \\  & w/o unlabeled dataset & 29.085 & 26.387 & 42.661 & 48.213 & 21.727 & 0.1444 & 0.4942 & 0.6628 \\ \hline \multirow{8}{*}{AMS-ASTA} & HTT & 2.279 & 0.412 & 17.193 & 23.1397 & 51.373 & 0.0602 & 0.2771 & 0.4003 \\  & T-base & 3.653 & 1.479 & 14.400 & 24.181 & 27.671 & 0.1299 & 0.3761 & 0.5541 \\  & T5-large & 4.212 & 1.767 & 15.180 & 25.828 & 27.626 & 0.1418 & 0.3761 & 0.5591 \\  & UniLM & 3.178 & 1.251 & 18.833 & 23.872 & 37.890 & 0.1032 & 0.121 & 0.4878 \\  & BERT-Gen & 4.003 & 1.605 & 17.751 & 24.162 & 28.284 & 0.1284 & 0.4024 & 0.5778 \\  & UniLM-v2 & 3.898 & 1.599 & 17.757 & 23.999 & 27.858 & 0.1255 & 0.3989 & 0.5796 \\ \cline{2-10}  & AlSeCond & **5.159** & **2.113** & 19.736 & **31.738** & 13.714 & **0.1627** & 0.5085 & 0.6811 \\  & w/o sentiment loss & 4.944 & 1.999 & **23.734** & 31.302 & 14.112 & 0.1477 & 0.4478 & 0.7217 \\  & w/o query-shirt & 4.208 & 1.635 & **23.461** & 29.497 & **10.835** & 0.1604 & **0.5538** & **0.7563** \\  & w/o unlabeled dataset & 3.458 & 1.026 & 20.761 & 28.924 & 15.787 & 0.1478 & 0.4728 & 0.6627 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for the fluency and diversity evaluation. Note that “\(\uparrow\)” means the higher the better, “\(\downarrow\)” means the lower the better, “\(\downarrow\)” means the lower the better, “w/o” means “no”. the input sequences. Hence its coverage and accuracy scores serve as an upper bound, although its generated results' syntax is relatively simple and repetitive.