A rg U : A Controllable Factual Argument Generator (2023)

Abstract: Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. For example, persuading someone to reconsider smoking might involve empathetic, well founded arguments based on facts and expert opinions about its ill-effects and the consequences on one’s family. However, the automatic generation of high-quality factual arguments can be challenging. Addressing existing controllability issues can make the recent advances in computational models for argument generation a potential solution. In this paper, we introduce ArgU: a neural argument generator capable of producing factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure using Walton’s argument scheme-based control codes. Unfortunately, computational argument generation is a relatively new field and lacks datasets conducive to training. Hence, we have compiled and released an annotated corpora of 69,428 arguments spanning six topics and six argument schemes, making it the largest publicly available corpus for identifying argument schemes; the paper details our annotation and dataset creation framework. We further experiment with an argument generation strategy that establishes an inference strategy by generating an “argument template” before actual argument generation. Our results demonstrate that it is possible to automatically generate diverse arguments exhibiting different inference patterns for the same set of facts by using control codes based on argument schemes and stance.

## introduction although arguing is an innate human quality, formulating convincing arguments is an art. a successful narrative aiming to persuade someone should be rhetorically appealing, trustworthy, factually correct, and logically consistent, which makes formulating good arguments challenging. incorporating neural language models, the relatively new field of computational argument generation has shown promise in assisting with argument synthesis. argument generators like project debater @xcite have successfully formulated convincing arguments across different domains including legal, politics, education, etc., and can potentially find new argumentative connections. however, lacking explicit control mechanisms, neural argument generators often render illogical and inappropriate arguments, reducing their trustworthiness and applicability for practical use. furthermore, training such models requires a considerable amount of quality data, which is hard to collect and annotate. hence, we propose argu, a controllable neural argument generator trained on a curated and quality-controlled corpus of annotated argument texts from abortion, minimum wage, nuclear energy, gun control, the death penalty and school uniform. argu strives to enable effective, scalable and appealing argument generation. as depicted in figure 1 , it takes as input worldly knowledge and concepts as fact variables and coherently combines them to generate an argument that exhibits the desired pro/con stance and inference structure. using control codes to regulate argument stance and reasoning, argu generates a variety of argument texts for the same set of facts 1 , thus providing diverse response options. internally argu implements a 2step generation process, where it first generates an "argument template", which depicts the structure of the final argument based on the control codes, and finally yields the argument text by modifying the template to include the augmented input fact variables. we ground our work on prominent theoretical foundations, where the inference structurebased control codes derive from six walton's argument schemes: "means for goal", "goal from means", "from consequence", "source knowledge", "source authority", and "rule or principle". since human annotation is expensive and timeconsuming, we devise a multi-phased annotation framework for systematically leveraging human and automatic annotation mechanisms to yield a curated dataset of 69,428 examples for controllable argument synthesis. we release our curated corpus to facilitate further research; an example constitutes an argument text, a set of real-world concepts and knowledge from which the argument derives, and the stance and argument scheme of the text. we further detail and analyze our annotation framework and share variants of topic-independent computational models for automatically annotating factual spans from argument text and identifying the asserted argument schemes. we share our datasets and codebase here: https://github.com/sougataub/argu-generator and summarize our contributions below: to our knowledge, this is the largest available corpora that identify argument schemes from argument text (sec. 3.2.4). ## related work argument schemes are typical inference patterns found in arguments. walton provided an in-depth study of argument schemes @xcite and defined 60 such schemes prevalent in daily argument text. based on walton's argumentation schemes, @xcite proposed representing the reasoning structure of arguments using bayesian networks and defined abstract network fragments termed idioms, which we use here. advances in neural methods for language modelling have enabled the field of computational argument generation. @xcite introduced a factual argument generator that generates opposite stance arguments by yielding a set of talking point key phrases, followed by a separate decoder to produce the final argument text. @xcite proposed candela, a framework for counterargument generation similar to @xcite , which also controls for the style. @xcite introduced arg-ctrl: a language model for generating sentence-level arguments using topic, stance, and aspect-based control codes @xcite . @xcite constructed argumentation-related knowledge graphs and experimented with using them to control argument generation. @xcite explored a novel pipelined approach to generating counterarguments that first identifies a weak premise and then attacks it with a neurally generated counterargument. hypothesizing that the impact of an argument is strongly affected by prior beliefs and morals, @xcite studied the feasibility of the automatic generation of morally framed argument text and proposed an argument generator that follows the moral foundation theory. @xcite introduced the task of generating informative conclusions from arguments. they compiled argument text and conclusion pairs and experimented with extractive and abstractive models for conclusion generation using control codes. @xcite experimented with argument text re-framing for positive effects. they created a suitable corpus and trained a controllable generator with a post-decoding entailment component for re-framing polarizing and fearful arguments such that it can reduce the fear quotient. our work best aligns with arg-ctrl and candela, where we use control codes to regulate argument generation and implement a multi-step decoding pipeline to generate the final argument. however, unlike arg-ctrl, we control for the argument scheme, and unlike candela, our multistep decoding utilizes an argument template as an intermediate step. most argumentation datasets identify argumen-tative components (claims, premises, etc.), making them better suited for argument-mining tasks @xcite @xcite @xcite . further, existing argument scheme annotated corpora are either very restricted in domain and size @xcite @xcite @xcite or only provide guidelines and tools for annotations @xcite . hence, we use the basn dataset @xcite , which contains sizeable examples spanning six topics and identify argument schemes. ## argument generation corpus training a factual argument generator controlled for the stance and argument scheme requires examples that identify such features from the text: such a corpus is lacking. hence, we introduce a twophased annotation framework that yields a corpus of 69,428 examples which (i) identify argument schemes and factual spans from argument text and (ii) grounds the spans to a knowledge base (kb). in the first phase, we employ human annotators to identify factual spans from a subset of an existing dataset of 2,990 arguments which already identifies argument schemes. we further train computational models to annotate the remaining corpus for factual spans and perform extensive quality checks. in the second phase, we train models from the resultant phase 1 dataset to automatically annotate a larger parallel corpus for both argument scheme and factual spans, yielding an annotated corpus of 69,428 arguments for training argument generators. @xcite introduced the basn dataset comprising 2,990 pairs of arguments and abstract network fragments derived from six walton's argumentation schemes: "means for goal", "goal from means", "from consequence", "source knowledge", "source authority", "rule or principle", and "others". they utilized a knowledge base (kb) of 205 facts (termed as variables) spanning the topics of abortion, minimum wage, nuclear energy, gun control, the death penalty and school uniform to define the idioms. figure 2 illustrates an example from the basn dataset where variables from the kb formulate a pro-stance argument following the "means for goals" argument scheme. we perform two annotation tasks in p1: (i) span detection: annotate arguments by identifying (highlighting) non-overlapping factual spans from argument text. (ii) span grounding: ground the identified factual spans to the available kb variables, or "others" if the span is unrelated to any available variables. we annotate 1,153 randomly sampled examples spanning all six topics and train a model for automatically annotating the remaining examples. we further perform human evaluations to determine the correctness of the automatic annotations. ## controllable argument generation arguments based on similar facts but structured differently might lead to dissimilar consequences by exerting different perlocutionary effects. for example, consider argument a: "reproductive rights advocates say enabling access to abortion is important towards reproductive rights", which exhibits the "from source authority" argument scheme, and b: "access to abortion is important towards reproductive rights", which expresses "from consequence". although both arguments share the same view regarding the role of abortion in reproductive rights, backed by reproductive rights advocates who are experts, argument a might lead to a favourable outcome in a situation that demands authority. to assist the formulation of arguments exhibiting heterogeneous viewpoints and reasoning, we experiment with bart-based @xcite neural argument generators capable of generating factual argument text with distinct stances and argument schemes using control codes. ## conclusion here we propose argu: a neural factual argument generator that systematically generates arguments following a specified stance and argument scheme. we devise a multi-step annotation framework to yield two golden and silver standard annotated datasets that we further use to train multiple argu variants. implementing automatic and human evaluation, we thoroughly analyze argu's generation capabilities. our findings indicate argu's applicability for aiding users to formulate situationspecific arguments by controlling the argument stance and scheme using control codes.