Predicting the Presence of Reasoning Markers in Argumentative Text (2022)

Abstract: This paper proposes a novel task in Argument Mining, which we will refer to as Reasoning Marker Prediction. We reuse the popular Persuasive Essays Corpus (Stab and Gurevych, 2014). Instead of using this corpus for Argument Structure Parsing, we use a simple heuristic method to identify text spans which we can identify as reasoning markers. We propose baseline methods for predicting the presence of these reasoning markers automatically, and make a script to generate the data for the task publicly available.

## introduction one key task within the field of argument mining (am) is the generation of textual summaries of arguments @xcite . significant work has been done on automatic extraction of argument components from argumentative text (see @xcite for a survey). however, research is still needed on how to use these extracted argument components to generate a fluent and readable textual summary. one means to improve the coherence, and hence readability, of an argument summary is for the selected components which express the content of the argument to be connected using reasoning markers, rather than simply placing them adjacent to each other. reasoning markers are words and phrases such as "because", "therefore" or "in conclusion" which can be used to structure an argumentative piece of text, acting as the "glue" to hold a text together and make it more intelligible. figure 1 indicates how we might envision reasoning marker prediction being used in an argument summarisation pipeline. such a pipeline could consist of argumentative components being extracted from a text, followed by selecting and ordering the most relevant components to form a summary. we concentrate on the final step of a proposed system like this; deciding where to insert reasoning markers to connect the selected argumentative components and produce a fluent text. ## corpus creation we use a simple heuristic method to identify rms in an already existing corpus, taking advantage of existing annotations. ## task and baselines here we describe the task and the implementation of several pretrained language model baselines. ## results we evaluate our results using precision, recall and f1-score macro-averaged between the two classes. the random baseline, as might be anticipated, achieved an f1-score of 0.50. as ## conclusions and future work we have presented a new task which we believe is a useful subtask for generating summaries of argumentative text: reasoning marker prediction. we have released a script that can be used to generate our derived corpus from pec, which supports this task. additionally, we have shown it is possible to predict the presence or absence of an rm between two argumentative components at an above-chance level. our baseline scores show this is a challenging task, with much room for improvement. of course we want not only to predict that an rm should occur but what the rm should be. in the future, we aim to work on using end-to-end models to generate an appropriate rm for a given context, instead of simply predicting whether or not an rm should appear. another aspect of this task which we have not explored is the sub-categorization of rms. multiple taxonomies of dms have been developed that could be used for this task. see @xcite taxonomy, and the development in @xcite . however, it is likely that this would be a nontrivial task and require some expert labelling, due to the fact that there is not a one-to-one correspondence between dms and their functions. a simple dm like "so" for example, has many different functions and can be used to provide justifications, for sequencing, or for expressing a purpose. nonetheless, since, as noted above, there are many rms in this dataset that are more-or-less interchangeable, it may be sufficient to predict the category that a potential rm should belong to rather than attempting to generate one directly.