NAP at S em E val-2023 Task 3: Is Less Really More? (Back-)Translation as Data Augmentation Strategies for Detecting Persuasion Techniques (2023)

Abstract: Persuasion techniques detection in news in a multi-lingual setup is non-trivial and comes with challenges, including little training data. Our system successfully leverages (back-)translation as data augmentation strategies with multi-lingual transformer models for the task of detecting persuasion techniques. The automatic and human evaluation of our augmented data allows us to explore whether (back-)translation aid or hinder performance. Our in-depth analyses indicate that both data augmentation strategies boost performance; however, balancing human-produced and machine-generated data seems to be crucial.

## introduction the semeval 2023 task 3 @xcite aims at analyzing online news by detecting genre, framing, and persuasion techniques, i.e., what is presented how using which rhetoric means. persuasion techniques detection (subtask 3) aims to identify which rhetoric means are used to influence and persuade the reader. news articles are provided in english, german, french, italian, polish, and russian. to foster the development of language-agnostic systems like our approach, the organizers additionally introduce three surprise languages -spanish, greek, and georgian foot_0 -with test data only. we build a system that successfully leverages (back-)translation as data augmentation approaches with multi-lingual transformer models to detect persuasion techniques in all 9 languages. we win the task for fr, achieve 2 nd place for ge, it and po, and 3 rd place for es, el and ka, while ranking midfield for ru and en. our main contribution consists in exploring the extent to which data augmentation via (back-)translation boosts performance for the task at hand. our findings suggest that more data does boost performance, especially for underrepresented labels. our in-depth analyses however show that less tends to be really more w.r.t balancing natural vs. augmented data, as more (augmented) data can severely hurt performance. ## background predicting a set of persuasion techniques given a piece of news text in a monolingual setting has been explored in previous shared tasks @xcite @xcite . existing successful systems usually include monolingual transformer-based models and ensemble mechanisms to optimally aggregate predictions @xcite @xcite . approaches that additionally focus on provided or external data also show improvement, using techniques such as finetuning on additional in-domain data or augmenting the training data @xcite . ## our system: approach and methods our approach combines predictions of several models in an ensemble, which differ in three main aspects: a) training data b) model architecture and c) input format to the model. we show in we report f 1 macro for completeness and show which models and their respective training data were considered in the ensemble for a given language. for example, for fr we find the best results with combining predictions of 4 different models: xlm-r-base trained with +t+bt, xlm-r-large, one trained with +bt-sl and one trained with spans and predictions by the adapters trained on +t+bt-sl. ## results and analysis subtask 3 official results and corresponding leaderboard rankings are shown in tab. 5. note that for each language we find an ensemble by computing the f1-micro score for different model combinations. the combination for each test language is listed in tab. 5, including the respective training data. we win the task for fr, achieve 2 nd places for po, it, ge and 3 rd places for the surprise languages el, gr, ka. we rank roughly around mid-field for ru and en, but manage a 3 rd place post-submission for the latter. to identify trends in the effectiveness of the various data augmentation strategies employed, we train a linear regression model to predict the f1 scores of the different persuasion labels as dependent variables. our regression model looks at the effect of training data, test language and persuasion technique, each coded as categorical independent 2 note that for ru, the span model was not included in the final ensemble due to a model error. variables (iv) 3 . regression results are presented in tab. 6. we first look at which factors play an important role in predicting different persuasion strategies effectively. we look at the amount of explained variance by each iv: the best fit (adj.r 2 = 0.86) contains each iv and the interactions between each training data with (i) test languages and (ii) persuasion techniques. very little systematic differences across test languages are observed, as there is 1% explained variance by the interaction between training data and test languages. in contrast, most variance is explained by the interaction between training data and labels (38%) indicating that performance in detecting persuasion techniques is considerably impacted by the choice of augmentation strategy. we now zoom into the extent to which the choice of augmentation strategy differs across persuasion techniques detection. we visualize the interaction term as an effect plot in fig. 6 . we select six labels of interest to show the overall trend, and present the full plots in app. c. we find the different augmented training sets on the x-axis (sorted by ascending size) and their impact on f1 scores' predictions on the y-axis. we observe that in general, data augmentation positively impacts the predicted performance, especially for the less-frequent persuasion techniques, e.g., flag waving. training on the original gold data with the relevant textual spans can have extremely positive effects (flag waving, loaded language) but this effect is not observed across all labels, indicating that some persuasion techniques' relevant textual information is particularly compressed and the context is therefore less crucial. results clearly point to backtranslation (+bt-sl) as the most robust augmentation strategy with consistent improvement of f1 scores across all labels. in contrast, translations consistently hurt the performance across all labels and is only effective if combined with back-translations (e.g., @xcite . this considerable difference in performance between injecting translations vs. back-translations, which are inherently the same processes, is surprising and not on par with our human evaluation findings (sec 3.3); we plan to conduct further analyses to investigate the phenomenon. overall, these analyses have shown that adding some more data, i.e., +bt only, does indeed improve performance but too much augmented data, i.e., +t, +t-bt tends to hinder it. ## conclusion we tackled the task of detecting persuasion techniques in online news in a multi-lingual setup. we built a system that successfully combines natural with augmented data via (back-)translation with an ensemble of sota multi-lingual models. while we showed that using augmented data, i.e., more data, generally boosts performance, our results also indicate that it might be hurt when integrating too much augmented data. in conclusion, for the task of persuasion techniques detection, more data, obtained via (back-)translation, does help overall, but less might be more when it comes to adding automatically-generated translations.