Persuasion dialogue system reflects the machine's ability to make strategic moves beyond verbal communication, and therefore differentiates itself from task-oriented or open-domain dialogues and has its own unique values. However, the repetition and
inconsistency problems still persist in dialogue response generation and could substantially impact user experience and impede the persuasion outcome. Besides, although reinforcement learning (RL) approaches have achieved big success in strategic tasks such as games, it requires a sophisticated user simulator to provide real-time feedback to the dialogue system, which limits the application of RL on persuasion dialogues. To address these issues towards a better persuasion dialogue system, we apply RL to refine a language model baseline without user simulators, and distill sentence-level information about repetition, inconsistency, and task relevance through rewards. Moreover, to better accomplish the persuasion task, the model learns from human demonstration to imitate human persuasion behavior and selects the most persuasive responses. Experiments show that our model outperforms previous state-of-the-art dialogue models on both automatic metrics and human evaluation results on a donation persuasion task, and generates more diverse, consistent and persuasive conversations according to the user feedback. We will make the code and model publicly available.
This paper describes and examines different systems to address Task 6 of SemEval-2021: Detection of Persuasion Techniques In Texts And Images, Subtask 1. The task aims to build a model for identifying rhetorical and psycho- logical techniques (such a
s causal oversimplification, name-calling, smear) in the textual content of a meme which is often used in a disinformation campaign to influence the users. The paper provides an extensive comparison among various machine learning systems as a solution to the task. We elaborate on the pre-processing of the text data in favor of the task and present ways to overcome the class imbalance. The results show that fine-tuning a RoBERTa model gave the best results with an F1-Micro score of 0.51 on the development set.
Inscribing persuasion techniques in memes is the most impactful way to influence peoples' mindsets. People are more inclined to memes as they are more stimulating and convincing and hence memes are often exploited by tactfully engraving propaganda in
its context with the intent of attaining specific agenda. This paper describes our participation in the three subtasks featured by SemEval 2021 task 6 on the detection of persuasion techniques in texts and images. We utilize a fusion of logistic regression, decision tree, and fine-tuned DistilBERT for tackling subtask 1. As for subtask 2, we propose a system that consolidates a span identification model and a multi-label classification model based on pre-trained BERT. We address the multi-modal multi-label classification of memes defined in subtask 3 by utilizing a ResNet50 based image model, DistilBERT based text model, and a multi-modal architecture based on multikernel CNN+LSTM and MLP model. The outcomes illustrated the competitive performance of our systems.
We developed a system for task 6 sub-task 1 for detecting propaganda in memes. An external dataset and augmentation data-set were used to extend the official competition data-set. Data augmentation techniques were applied on the external data-set and
competition data-set to come up with the augmented data-set. We trained 5 transformers (DeBERTa, and 4 RoBERTa) and ensembled them to make the prediction. We trained 1 RoBERTa model initially on the augmented data-set for a few epochs and then fine-tuned it on the competition data-set which improved the f1-micro up to 0.1 scores. After that, another initial RoBERTa model was trained on the external data-set merged with the augmented data-set for few epochs and fine-tuned it on the competition data-set. Furthermore, we ensembled the initial models with the models after fine-tuning. For the final model in the ensemble, we trained a DeBERTa model on the augmented data-set without fine-tuning it on the competition data-set. Finally, we averaged the output of each model in the ensemble to make the prediction.
We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems. The task focused on memes and had three subtasks: (i) de
tecting the techniques in the text, (ii) detecting the text spans where the techniques are used, and (iii) detecting techniques in the entire meme, i.e., both in the text and in the image. It was a popular task, attracting 71 registrations, and 22 teams that eventually made an official submission on the test set. The evaluation results for the third subtask confirmed the importance of both modalities, the text and the image. Moreover, some teams reported benefits when not just combining the two modalities, e.g., by using early or late fusion, but rather modeling the interaction between them in a joint model.
The research presents is an attempt to study the Techniques and tactics of speech
which convince the receiver of its notions and ideas so that he takes them for granted then
it applies them to a historical text which is "the speech of Abullah Ibn Y
ehia, Head of
Ibadyyeh when ruling Yemen". the research also attempts to make a compromise clarifying
both sides of the saying : the theoretical and the practical alongside the tacticse utilized in
persuasion.
The orator\ speaker begins with emotional usages emanated from tribal thoughts that
both the receiver and the sender embrace taking them from Islamic law principles which
evoke fear in the Muslim receiver as they are ready-made arguments. Then they display
emotional mental ones that refrain from grabbing the attention of the receiver but make
him aware of the disappearance of classes' differences with the sender. This happens in
instructional strategies that enabled him to have an impact upon the receiver knowing his
intentions and aim via some data of pragmatic linguistics which rely on argumentation,
verbal acts and subtle expressionistic styles deployed in this same context.
This pragmatic might be the best when it comes to studying his speech, analyzing it,
then unraveling its tools of persuasion taking into account that is a science that studies
language in context.
