Employing Argumentation Knowledge Graphs for Neural Argument Generation (2021)

Abstract: Generating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines. Motivated by the effectiveness of utilizing knowledge graphs for supporting general text generation tasks, this paper investigates the usage of argumentation-related knowledge graphs to control the generation of arguments. In particular, we construct and populate three knowledge graphs, employing several compositions of them to encode various knowledge into texts of debate portals and relevant paragraphs from Wikipedia. Then, the texts with the encoded knowledge are used to fine-tune a pre-trained text generation model, GPT-2. We evaluate the newly created arguments manually and automatically, based on several dimensions important in argumentative contexts, including argumentativeness and plausibility. The results demonstrate the positive impact of encoding the graphs’ knowledge into debate portal texts for generating arguments with superior quality than those generated without knowledge.

## introduction arguments are our means to build stances on controversial topics, to persuade others, or to negotiate. automatic argument generation has the potential to effectively support such tasks: it may not only regenerate known arguments but also uncover new facets of a topic. existing argument generation approaches work either in an end-to-end fashion @xcite or they are controlled with respect to the argument's topic, aspects, or stance @xcite . in contrast, no approach integrates external knowledge into the generation process so far, even though knowledge graphs have been shown to be useful for supporting text generation models in other areas @xcite . previous research has proposed argumentation knowledge graphs (akgs) that model supporting and attacking interactions between concepts @xcite . such an akg may assist argument generation models in different ways. for example, meaningful prompts on controversial topics can be constructed from an akg with simple hand-defined rules, such as 'geoengineering reduces atmospheric greenhouse gas' for generating an argument on 'geoengineering.' alternatively, an akg may be employed to control the generation, making arguments adhere to knowledge covered in the graph. we hypothesize this to be particularly beneficial for the quality of arguments in terms of factuality, the richness of evidence, and similar. this paper concentrates on such controlled argument generation, investigating for the first time the ability to generate high-quality and content-rich arguments by integrating knowledge from akgs into standard neural-based generation models. to this end, we exploit multiple manually and automatically created knowledge graphs, devoting particular attention to causal knowledge @xcite . causality plays a major role in argumentation due to its frequent usage in real-life discussions; argument from cause to effect and argument from consequences are frequently used argumentation schemes @xcite . to utilize akgs for argument generation, we collect argumentative texts from diverse sources such as online debate portals. in these texts, we find arguments that contain instances of the knowledge covered in the graphs. we encode this knowledge as keyphrases in the arguments. unlike @xcite and @xcite , our keyphrases cover multiple aspects and stances related to the same topic. the resulting texts are used to finetune a transformer-based generation model, gpt-2 @xcite . the underlying hypothesis is geoengineering is good for society because... ## knowledge graphs for argumentation we use knowledge graphs (kgs) to plan the content of an argument to be generated and to control its talking points. a talking point is a specific aspect related to a given discussion topic. for instance, 1 https://github.com/webis-de/acl-21 "health" is a talking point related to "smoking." in this section, we describe the construction of three graphs related to argumentation: (1) a groundtruth argumentation knowledge graph, which is utilized based on al-khatib et al. ( 2020 ), (2) a generated argumentation knowledge graph, which is newly constructed from a set of argumentative texts, and (3) a causality graph, which is built upon @xcite . @xcite propose a graph model that encodes the knowledge contained in arguments as relations (identified as the graph's edges) between concepts (identified as the graph's nodes). a concept is a noun phrase that represents an entity, an event, or an abstract idea. a relation represents the positive or negative effect that a concept has on another one. a relation is positive if concept a promotes/causes/increases concept b, and it is negative if concept a suppresses/prevents/stops concept b. a concept has two types of attributes: (1) groundings, which link concepts to the corresponding entries in a knowledge base such as wikidata, (2) consequences, stating whether a concept is viewed as predominantly good or bad. ## neural argument generation we now present our approach to integrate the argumentation knowledge graphs such as those described above into a neural text generation model. stability of a country bank system positive -→ economic stability raise oil price negative -→ world oil industry legalizing marijuana positive -→ tourism industry online social vigilantism negative -→ insulting behavimy economic growth positive -→ global warming human parainfluenza viruse positive -→ viral pneumonium ## experiments and results in this section, we report on the manual and automatic evaluation of our approach from section 3 to employ the three argumentation knowledge graphs from section 2 for neural argument generation: a. the ground-truth graph b. the generated graph c. the causality graph ## related work in this section, we outline related studies on argument generation, argumentation knowledge graphs, and graph-to-text generation. argument generation different approaches to the generation of arguments, or of components thereof, have been proposed in the last years. to create new claims, @xcite recomposed predicates from existing claims with new topics. el @xcite composed complete arguments from given claims following specific rhetorical strategies based on the theoretical model of @xcite . unlike these approaches, we make use of neural language models. @xcite built a sequenceto-sequence model to rewrite claims into opposing claims, and @xcite presented a sophisticated approach that, given a stance on a controversial topic, combines retrieval with neural generation techniques to create full arguments with the opposite stance. @xcite developed a transformer-based pipeline to generate coherent and plausible claims, whereas @xcite proposed a language model that controls argument generation on a fine-grained level for a given topic, stance, and aspect. lastly, @xcite generated belief-based claims, encoding the beliefs via conditional language models. most similar to our work are the studies of @xcite and @xcite . like us, the former also exploits the power of gpt-2, adding context to the model's training data. the latter is comparable in that it attempts to steer the generation towards aspect-specific arguments. to the best of our knowledge, however, our approach is the first to employ external knowledge from knowledge graphs for the task of argument generation. argumentation knowledge graphs besides the argumentation knowledge graph of @xcite created an expert stance graph to support stance classification. @xcite encoded the relations between segments of an argument into a graph and demonstrated the graph's effectiveness for argument mining. in our work, we utilize one of the available graphs, among others, using its knowledge to control the argument generation process. closely related to argumentation knowledge, causality graphs gained some attention recently. while general knowledge bases such as concept-net @xcite contain causal knowledge, the causality graph of @xcite that we utilized is the largest source of causal knowledge, exceeding others by orders of magnitude. ## conclusion this paper tackles argument generation through the use of argumentation knowledge graphs. we have discussed how to take advantage of different manually and automatically created knowledge graphs to encode knowledge in argumentative texts, and how to utilize these texts to fine-tune gpt-2. our approach is able to generate high-quality arguments for various inputs, including complex relational knowledge. besides, we proposed a simple method for evaluating arguments automatically, with results correlating to those observed in the manual evaluation. in our future research, we plan to leverage more sources and evaluate other knowledge encoding methods. moreover, we will study different directions to illuminate the possible social bias in argument generation methods.