# Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction
## 5 Discussion
### A Critical Point Is How Appropriate Triggers Are Generated  
The generative approach for the triggers did not demonstrate significant performance improvement, particularly in the 32-shot and the full-shot settings. We attribute this minor performance gain to the insufficient quality of the generated triggers, as the annotated ground-truth triggers in the dataset for training are highly scarce. Therefore, we conducted additional comparison experiments to analyze this assumption in detail by changing the type of PLM and inserting an additional input feature. Tables 4 and 5 show the efficacy comparison of the generated triggers according to the type of trigger generation models. We compared two typical generative pre-trained language models (PLMs) with encoder-decoder architecture, T5 and BART [38]. Specifically, T5-large and BART-large models are adopted for the trigger generation module, and the fine-tuned models with the generated triggers by each model are shown as _GenTr_ (T5) and _GenTr_ (BART), respectively. In the full-shot setting, _GenTr_ (T5) outperformed _GenTr_ (BART) by approximately 0.5%p both at F1 and F1\({}_{c}\) scores in the development set, but in the test set, _GenTr_ (BART) demonstrated approximately 0.3%p higher scores. Moreover, the _GenTr_ (T5) model showed higher overall performance improvements than the _GenTr_(BART) in the few-shot setting except for the F1\({}_{c}\) score when \(K\) is 32. In regard to these results, we assume that the T5 model more effectively handled the problem of lack of triggers to learn due to its large parameter size than the BART model, but we found that this was not a determinant factor. In addition, _GenTr_ (w/rel) in the full-shot setting indicates a model where the triggers were generated by providing the relation class \(r\) as an additional training input feature, and the input for this model is constructed as \"
 \(D\) 
 \(E\) 
 \(r\) 
\". Utilizing the triggers generated in this way led to an exponential increase in the relation prediction performances, achieving performance improvements of more than 10%p at F1 and F1\({}_{c}\) scores in both data splits. This result confirms the significance of providing appropriately generated triggers to the model as demonstrated in the previous paper [9]. Table 6 shows an example of the comparison between the generated triggers by the three model types, i.e., _GenTr_ (T5), _GenTr_ (BART) and _GenTr_ (w/rel). In the case of the first relation (R1), all three models generated \"boyfriend\" as a trigger, whereas only the _GenTr_ (w/rel) model correctly generated a trigger \"love\", for the second relation (R2). In addition to the given examples in the table, there are several cases in which only the model with the triggers generated using relation classes as an additional input feature correctly predicted triggers, such as a trigger \"husband\" for the relation \"per:spouse\". In other words, redundant phrases. Thus, with regard to the trigger generation method, we conclude that simply providing a dialogue and an entity pair as input features for generation is insufficient to guide the generative model effectively on the critical contextual information due to the scarcity of the annotated triggers. Moreover, to play a decisive role, triggers should be generated by supplementing the input features with another complement with informational importance comparable to the relation class \(r\). Based on this perspective, discovering the additional