Argumentation Mining on Essays at Multi Scales (2020)

Abstract: Argumentation mining on essays is a new challenging task in natural language processing, which aims to identify the types and locations of argumentation components. Recent research mainly models the task as a sequence tagging problem and deal with all the argumentation components at word level. However, this task is not scale-independent. Some types of argumentation components which serve as core opinions on essays or paragraphs, are at essay level or paragraph level. Sequence tagging method conducts reasoning by local context words, and fails to effectively mine these components. To this end, we propose a multi-scale argumentation mining model, where we respectively mine different types of argumentation components at corresponding levels. Besides, an effective coarse-to-fine argumentation fusion mechanism is proposed to further improve the performance. We conduct a serial of experiments on the Persuasive Essay dataset (PE2.0). Experimental results indicate that our model outperforms existing models on mining all types of argumentation components.

## introduction argumentation mining (am) is a challenging task in natural language processing @xcite . recent research mainly involves independent sentences @xcite @xcite @xcite and also essays @xcite @xcite @xcite @xcite . in this paper, we focus on argumentation mining on essays. argumentation mining on essays aims to identify the types and locations of argumentation components from essay text @xcite . typically, there are three argumentation types, namely major claims (mc), claim (c) and premises (p). previous research @xcite takes sentences as the smallest argumentative unit, and handles this task in a rough way. they firstly split the essay into several sentences, and adopt a sentence classification model to select and reserve sentences which may be promising to contain argumentation components. then they further identify the exact boundaries of argumentation components in those sentences. these pipeline approaches fail to conduct effective argumentation mining, since they ignore the argumentation structure of the essay and only handle the task at sentence level. recent research @xcite focuses on end-to-end neural models. they boil the task down to a sequence tagging problem, and handle it at word level instead of sentence level. typically, neural network is employed as encoder for text representation, and conditional random field (crf) is employed as decoder to make final prediction. this word-level sequence tagging method can simultaneously identify the types and locations of all argumentation components. however, as shown in figure 1 , it can be observed that different types of argumentation components are at different levels: international tourism is now more common than ever before the last decade has seen an increasing number of tourists traveling to visit natural wonder sights, ancient heritages and different cultures around the world. while some people might think that this international tourism has negative effects on the destination countries, i would contend that it has contributed to the economic development as well as preserved the culture and environment of the tourist destinations @xcite . firstly, international tourism promotes many aspects of the destination country's economy in order to serve various demands of tourists[p]. take cambodia for example, a large number of visitors coming to visit the angkowat ancient temple need services like restaurants, hotels, souvenir shops and other stores @xcite . these demands trigger related business in the surrounding settings which in turn create many jobs for local people improve infrastructure and living standard @xcite . therefore tourism has clearly improved lives in the tourist country @xcite . secondly • • • to conclude, as far as i am concerned, international tourism has both triggered economic development and maintained cultural and environment values of the tourist countries @xcite . in addition, the authorities should adequately support these sustainable developments. • claims serve specific paragraphs as the core statements. they can appear anywhere in a paragraph, either proposed at the beginning, or summarized in the end, and also given in the middle. they are at paragraph level. in this paper, we propose a multi-scale argumentation mining model. in order to mine major claims, we design essay-level argumentation extraction submodule based on multi-span extraction strategy. besides, to mine claims, we design paragraph-level argumentation extraction submodule based on randomized extraction strategy. as for mining premises, we follow the word-level sequence tagging method. finally, a coarse-to-fine argumentation fusion mechanism is proposed to further improve the performance. we carry out a serial of experiments on the persuasive essays dataset (pe 2.0) @xcite . the experimental results indicate that our model can significantly improve the performance as compared to state-of-the-art models, where our model respectively achieves 8.92% absolute improvement on overall performance, 14.89% absolute improvement on mining major claims, and 11.05% absolute improvement on mining claims. moreover, we compare the performance of (i) multi-span extraction and randomized extraction (ii) argumentation extraction and argumentation tagging, which allow us to validate the effectiveness of our strategies of processing different types of argumentation components at their corresponding levels. the organization of this paper is as follows. firstly we give a detailed explanation to our multi-scale argumentation mining model in section 2. then in section 3, we introduce our experiments. the detailed experimental results are displayed and analyzed in section 4. in section 5, we give a brief overview of related work about argumentation mining on essays. finally we draw our conclusion in section 6. ## multi-scale argumentation mining model an overview of our multi-scale argumentation mining model is shown in figure 2 . for major claims, we design an essay-level argumentation extraction submodule based on multi-span extraction strategy in section 2.1, where the whole essay is taken as the input of bert encoder, and a pointer network is utilized to score each word and thus score all the candidate spans. by these scores and a set of reasonable rules, we rank and filter the candidate spans to select result spans. for claims, we design a paragraphlevel argumentation extraction submodule based on randomized extraction strategy in section 2.2, where each paragraph is respectively taken as the input of bert encoder to mine result spans, and result spans of each paragraph are gathered as the result spans of the corresponding essay. for premises, we design a word-level argumentation tagging submodule in section 2.3, where the whole essay is taken as the input of bert encoder, and crf is utilized as decoder to obtain the tag sequence with the highest sequence score. finally, a coarse-to-fine argumentation fusion mechanism in section 2.4 is utilized to obtain the final results, since there may exist some overlaps on result spans of different argumentation types. ## experiments in this section, at first we introduce the dataset we utilize and show our experiment setup. then we introduce the evaluation metrics. finally we list the baselines that we adopt for comparison. ## conclusion we propose a multi-scale argumentation mining model for argumentation mining on essays. our model respectively mines different types of argumentation components at corresponding levels. moreover, a coarse-to-fine argumentation fusion mechanism is adopted to further improve the results. the experimental results on pe 2.0 dataset indicate that our model obtains the state-of-the-art performance, where the model obtains significantly improved performance on mining major claims and claims. the results reveal the importance of argumentation mining at different levels on different argumentation types. in the future, we will try to mine different argumentation types with multi-task learning method.