This paper presents a study on parsing the argumentative structure in English-as-foreign-language (EFL) essays, which are inherently noisy. The parsing process consists of two steps, linking related sentences and then labelling their relations. We experiment with several deep learning architectures to address each task independently. In the sentence linking task, a biaffine model performed the best. In the relation labelling task, a fine-tuned BERT model performed the best. Two sentence encoders are employed, and we observed that non-fine-tuning models generally performed better when using Sentence-BERT as opposed to BERT encoder. We trained our models using two types of parallel texts: original noisy EFL essays and those improved by annotators, then evaluate them on the original essays. The experiment shows that an end-to-end in-domain system achieved an accuracy of .341. On the other hand, the cross-domain system achieved 94% performance of the in-domain system. This signals that well-written texts can also be useful to train argument mining system for noisy texts.

Argumentative structure prediction aims to establish links between textual units and label the relationship between them, forming a structured representation for a given input text. The former task, linking, has been identified by earlier works as particularly challenging, as it requires finding the most appropriate structure out of a very large search space of possible link combinations. In this paper, we improve a state-of-the-art linking model by using multi-task and multi-corpora training strategies. Our auxiliary tasks help the model to learn the role of each sentence in the argumentative structure. Combining multi-corpora training with a selective sampling strategy increases the training data size while ensuring that the model still learns the desired target distribution well. Experiments on essays written by English-as-a-foreign-language learners show that both strategies significantly improve the model’s performance; for instance, we observe a 15.8% increase in the F1-macro for individual link predictions.

This paper introduces TIARA, a new publicly available web-based annotation tool for discourse relations and sentence reordering. Annotation tasks such as these, which are based on relations between large textual objects, are inherently hard to visualise without either cluttering the display and/or confusing the annotators. TIARA deals with the visual complexity during the annotation process by systematically simplifying the layout, and by offering interactive visualisation, including coloured links, indentation, and dual-view. TIARA’s text view allows annotators to focus on the analysis of logical sequencing between sentences. A separate tree view allows them to review their analysis in terms of the overall discourse structure. The dual-view gives it an edge over other discourse annotation tools and makes it particularly attractive as an educational tool (e.g., for teaching students how to argue more effectively). As it is based on standard web technologies and can be easily customised to other annotation schemes, it can be easily used by anybody. Apart from the project it was originally designed for, in which hundreds of texts were annotated by three annotators, TIARA has already been adopted by a second discourse annotation study, which uses it in the teaching of argumentation.

Coherence is a crucial feature of text because it is indispensable for conveying its communication purpose and meaning to its readers. In this paper, we propose an unsupervised text coherence scoring based on graph construction in which edges are established between semantically similar sentences represented by vertices. The sentence similarity is calculated based on the cosine similarity of semantic vectors representing sentences. We provide three graph construction methods establishing an edge from a given vertex to a preceding adjacent vertex, to a single similar vertex, or to multiple similar vertices. We evaluated our methods in the document discrimination task and the insertion task by comparing our proposed methods to the supervised (Entity Grid) and unsupervised (Entity Graph) baselines. In the document discrimination task, our method outperformed the unsupervised baseline but could not do the supervised baseline, while in the insertion task, our method outperformed both baselines.