# Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction
## 6 Conclusions  
This paper explored simple yet effective methods in dialogue relation extraction by introducing prompt-based fine-tuning and the trigger generation approach. Also, their effectiveness is analyzed with additional experiments. In particular, unlike the previous extractive approach, we adopted the generative approach for the trigger generation module and compared the efficacy of the generated triggers between representative generative pre-trained language models (PLMs), i.e., BART and T5. The generated triggers have shown more significant effects in the few-shot setting compared to the full-shot setting, specifically when the shot K is 8. However, due to the insufficiency of ground-truth triggers for training, there still are points to improve the trigger generation module in the future. In addition, the prompt-based approach, including the prompt manual initialization method, which considers the prior distributional knowledge, demonstrated its effectiveness, showing significant performance improvements compared to the baseline model. To summarize, this study aimed to directly exploit the model's implicit knowledge in the dialogue relation extraction task through the utilization of a trigger-generation method and a prompt-based approach and guide the model to clues about relational facts. To this end, attempts were conducted to utilize generative models, add soft prompts, and deliberately initialize inserted prompts. Furthermore, motivated by the observations, it is expected to improve task performance by utilizing more diverse generative models for enriching the quality of generated triggers in future work. Conceptualization, J.K. and J.S. ; methodology, J.K.; software, J.K., G.K. and J.S. ; validation, J.K.; formal analysis, J.K.; investigation, J.K. and G.K.; resources, J.K. and J.S. ; data curation, J.S. ; writing--original draft preparation/review and editing, J.K., G.K. and J.S. ; visualization, J.K. and G.K.; supervision/project administration/funding acquisition, H.L. ; All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{r l} \hline \hline
**Dialogue** \\ \hline
1 & Speaker 1: So, um...I’m proposing to [PERSON] tonight. \\
2 & Speaker 2: Tonight?! Isn’t an engagement ring supposed to have a diamond? Oh, there it is! \\
3 & Speaker 1: Yeah, well, being a failed scientist doesn’t pay quite as well as you might think. That’s um... \\  & one seventieth of a karat. And the clarity is um...is quite poor. \\
14 & Speaker 3: Ok, my husband just gave your **boyfriend** some very bad advice. \\  & Look, [PERSON] is going to propose to you tonight. \\
15 & Speaker 4: Wow? Really? That’s fantastic! \\
16 & Speaker 3: What are you serious? You wanna marry him? Who...What about [PERSON]? \\
17 & Speaker 4: Oh, ok, you want me to marry [PERSON]? Alright, well, let’s just gag him and handcuff him and \\  & force him down the aisle. I can just see it: “[PERSON], do you take [PERSON]...” You know, it’s \\  & every girl’s dream! \\
18 & Speaker 3: Do you really think marrying someone else is the right answer? \\
19 & Speaker 4: Sure! Look, ok, bottom line: I **love** [PERSON]...[PERSON]! [PERSON]. I love [PERSON]. Don’t look at me \\  & that way, [PERSON]! \\ \hline \hline \end{tabular}
\begin{tabular}{r l} \hline \hline
**Argument pair** & **GT Trigger** & **Generated Triggers** & **Relation Type** \\  & & _GenTr_ (T5) & _GenTr_ (BART) & _GenTr_ (w/rel) \\ \hline
**R1** & (Speaker 1, Speaker 4) & boyfriend & boyfriend \\
**R2** & (Speaker 4, [PERSON]) & love & boyfriend & boyfriend \\ \hline \hline \end{tabular}
\end{table} 
Table 6: This table shows an example of the generated triggers by three model types (_GenTr_ (T5), _GenTr_ (BART) and _GenTr_ (w/rel)) in the DialogRE development set. _GenTr_ (w/rel) is a model where relation class information is provided as an input feature when generating a trigger. The ground-truth triggers (GT Trigger) and the generated triggers (_GenTr_) by the models are bolded. R1 (R2) indicates the given relational information.