Multitask Instruction-based Prompting for Fallacy Recognition (2022)

Abstract: Fallacies are used as seemingly valid arguments to support a position and persuade the audience about its validity. Recognizing fallacies is an intrinsically difficult task both for humans and machines. Moreover, a big challenge for computational models lies in the fact that fallacies are formulated differently across the datasets with differences in the input format (e.g., question-answer pair, sentence with fallacy fragment), genre (e.g., social media, dialogue, news), as well as types and number of fallacies (from 5 to 18 types per dataset). To move towards solving the fallacy recognition task, we approach these differences across datasets as multiple tasks and show how instruction-based prompting in a multitask setup based on the T5 model improves the results against approaches built for a specific dataset such as T5, BERT or GPT-3. We show the ability of this multitask prompting approach to recognize 28 unique fallacies across domains and genres and study the effect of model size and prompt choice by analyzing the per-class (i.e., fallacy type) results. Finally, we analyze the effect of annotation quality on model performance, and the feasibility of complementing this approach with external knowledge.

## introduction a fallacious argument is one that seems valid but it is not @xcite . theoretical work in argumentation has introduced various typologies of fallacies. for example, @xcite consider fallacies that occur when an argument violates the ten rules of a critical discussion, while tindale (2007) categorizes fallacies into 4 categories: structural fallacies, related to the number and structure of arguments; fallacies from diversion, drawing from the (un)intentional diversion of the attention from the issue at hand; logical fallacies, related to the argument scheme at play and question-answering dialog moves in argotario: has anyone been on the moon? the moon is so far away, we should focus on our society. fallacy: red herring propaganda techniques in news: the ability to build an untraceable, unregistered gun is definitely a game changer. fallacy: loaded language educational website on fallacies: she is the best because she is better than anyone else fallacy: circular reasoning fact-checked news: says joe biden has said 150 million americans died from guns and another 120 million from covid-19. fallacy: cherry picking work in computational models for fallacy recognition is still in its infancy, with a limited set of relatively small datasets such as argotario @xcite , which consists of question and answer dialog moves; name-calling in social media debates @xcite , fallacies as propaganda techniques in news @xcite ; logical fallacies from educational websites @xcite , and fallacies used for misinformation in social media and news around covid-19 @xcite . previous work on fallacy recognition has tackled just one dataset at a time. for example, work on detecting propaganda techniques use fine-tuning of different pre-trained transformers with embeddingbased or handcrafted features @xcite as well as lstms and transformers for sequence tagging of propaganda fragments @xcite @xcite , while @xcite propose a structure-aware classifier to detect logical fallacies. fallacy recognition is a challenging task for three main reasons: i) the number of classification labels (fallacy types) and class imbalance in existing datasets is often very high; ii) existing datasets cover varying genres and are typically very small in size due to annotation challenges; and iii) models trained on individual data sets often show poor out of distribution generalization. a recent line of work @xcite relies on the intuition that most natural language processing tasks can be described via natural language instructions and models trained on these instructions in a multitask framework show strong zero-shot performance on new tasks. based on this success, we propose a unified model based on multitask instruction-based prompting using t5 @xcite to solve the above challenges for fallacy recognition (section 3). this approach allows us to unify all the existing datasets and a newly introduced dataset (section 2) by converting 28 fallacy types across 5 different datasets into natural language instructions. in particular, we address the following research questions: i) can we have a unified framework for fallacy recognition across domains, genres, and annotation schemes? ii) are fallacy types expressed differently across datasets? iii) what are the effects of model size and prompt choice on the per-class performance for the fallacy recognition task? experimental evidence shows that our multitask fine-tuned models outperform task specific models trained on a single dataset by an average margin of 16% as well as beat strong few-shot and zeroshot baselines by average margins of 25% and 40%, respectively in macro f1 scores across five datasets (section 4.1). to further deepen our understanding towards the task of fallacy recognition we analyze the performance of our models for each fallacy type across datasets, model size and prompt choice (section 4.2). we further analyze the effect of annotation quality on the model performance, and the feasibility of complementing this approach with external knowledge (section 4.3). we make all datasets, code and models publicly available. ## data we experiment with five datasets (4 existing and a new dataset) that cover 28 unique fallacy types in multiple domains (e.g., covid-19, climate change, politics) and genres (e.g. news articles, qa turns in dialog, social media). ## multitask instruction-based prompting recently, @xcite ; @xcite leverage the intuition that nlp tasks can be described via natural language instructions, such as "is the sentiment of this movie review positive or negative?" or "translate 'how are you' into chinese.". they then take a pre-trained language model and perform instruction tuning -fine-tuning the model on several nlp datasets expressed via natural language instructions. such an approach has several benefits, with the most important one being able to have a unified model for several tasks. finally, training tasks spanning diverse datasets in a massively multitask fashion improves inference time performance especially for smaller datasets. following the success of multitask instructionbased prompting we approach different formulations of fallacies across datasets as different tasks with a generic prompting framework in a single model. we use t5 @xcite as the backbone model for training on all five fallacy datasets that have different number and types of fallacies. we hypothesize that when a model is able to learn to recognize fallacy types from multiple datasets, it is more likely able to learn generic traits of fallacy types rather than learning characteristics specific to a single dataset. a sample list of instructions for each dataset is shown in figure 1 (full list in appendix c). all instructions start with an n-gram (e.g. 'given a text segment') followed by a list of fallacy types with or without their definitions. the complete set of fallacies and definitions are shown in appendix b. the final component of the instruction is specific to each dataset (e.g., question-answer pair for argotario, sentence-fragment or sentence only for propaganda). the generation target during training and test is one of the fallacies types that are permissible for each dataset. in addition, we ask the model to generate the fragment that contains the fallacy (propaganda dataset only) during training to increase the diversity of prompts and instructions during training. since the overall objective of this work is to have a generic classifier for fallacy and to compare with other classification methods, evaluating the model's ability to correctly generate the fallacious fragment is beyond the scope of this paper. during inference time, we use greedy decoding and select the generated target as the prediction of fallacy type. the evaluation is done using strict string match with the gold fallacy. model hyperparameters are shown in appendix a. ## evaluation setup and results given the high imbalance nature of all fallacy datasets, we report both accuracy (equivalent to micro f1 as we do not include multi-label instances) and macro f1. baselines. we consider the following three models as our baselines: i) zero-shot classification using unifiedqa @xcite ; ii) few-shot instruction-tuning of gpt-3 @xcite ; and iii) full-shot fine-tuning of bert @xcite . unifiedqa is a question-answering model that is trained on 20 question-answering datasets in different formats and showed generalization capability to unseen data. we use its recent version unifiedqa-v2 (3b size) @xcite to test the ability of such model to recognize fallacies in zero-shot settings. we also do fewshot instruction-tuning of gpt-3 as many fallacy datasets are of small size, which poses the need for models that can perform well using few-shot training. we setup the instructions in a similar fashion to the ones used for t5 (i.e. list prompt in figure 1 ). additionally, we setup instructions with explanations where each few-shot example has a text segment, a fallacy label, and a sentence explaining why the fallacy label is suitable for the text, which is shown to improve the results of few-shot learning (lampinen et al., 2022). 2 constrained by the length allowed in the prompt, we use 2-shots per the five fallacy types for the argotario dataset, and 1-shot per the nine-to-fifteen fallacy types for the other datasets. given the high number of fallacy types, it is not feasible to instruction-tune gpt-3 on the 28 unique fallacy types that exist in all five datasets combined. finally, we fine-tune bert for 3 epochs on each dataset separately to test its ability to do fallacy recognition. all model hyperparameter details are shown in appendix a. we also use a t5-large model trained on each dataset separately using the instructions shown in figure 1 as a baseline in order to compare the results of single-dataset with multi-dataset training. ## related work fallacy there are various typologies of fallacies that address informal logic traditions or rules of ideal critical discussion @xcite @xcite @xcite . this intersects with propaganda techniques that focus on faulty reasoning and emotional appeals to accomplish persuasion @xcite @xcite . computational work on fallacy recognition includes fallacies in dialogue @xcite , argument sufficiency @xcite , name calling on reddit @xcite , nonsequitur fallacy in legal text (nakpih and santini, 2020), logical fallacies @xcite , fallacies in misinformation @xcite , as well as propaganda techniques @xcite . however, all previous work is limited to one fallacy scheme while we develop a model that detects fallacies in five datasets across four fallacy schemes. prompting using prompts has emerged as a generic framework to train natural language processing models on multiple tasks using prefix text @xcite , and few-shot prompt-tuning of gpt-3 @xcite . this was followed by multiple studies that use prompts on smaller size models using few and full shots on tasks such as natural language inference @xcite ), text classification @xcite , relation extraction @xcite , and using instruction prompts for mul-tiple tasks @xcite . we follow a similar setup by training a t5 model using instruction prompts for different formulations of fallacy recognition approached as multiple tasks. ## conclusion we introduced a unified model using multitask instruction-based prompting for solving the challenges faced by the fallacy recognition task. we could unify all the datasets by converting 28 fallacy types across 5 different datasets into natural language instructions. we showed that our unified model is better than training on a single dataset. we analyzed the effect of model size and prompt choice on the detection of specific fallacy types that could require additional knowledge better captured by bigger models (e.g., diversion fallacies such as red herring), and the distinction between similar fallacies better detected by more comprehensive prompts that include definitions of fallacy types (e.g., doubt vs. whataboutism). we analyzed the differences of fallacy types that appear in multiple fallacy schemes across the five datasets and showed that one fallacy type could have multiple meanings which further increases the complexity of this task (e.g., irrelevant authority). we conducted a thorough error analysis and released a new fallacy dataset for fact-checked content in the climate change domain.