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Argumentative structure prediction aims to establish links between textualunits and label the relationship between them, forming a structuredrepresentation for a given input text. The former task, linking, has beenidentified by earlier works as particularly challenging, as it requires findingthe most appropriate structure out of a very large search space of possiblelink combinations. In this paper, we improve a state-of-the-art linking modelby using multi-task and multi-corpora training strategies. Our auxiliary taskshelp the model to learn the role of each sentence in the argumentativestructure. Combining multi-corpora training with a selective sampling strategyincreases the training data size while ensuring that the model still learns thedesired target distribution well. Experiments on essays written byEnglish-as-a-foreign-language learners show that both strategies significantlyimprove the model's performance; for instance, we observe a 15.8% increase inthe F1-macro for individual link predictions.

Multi-Task and Multi-Corpora Training Strategies to Enhance Argumentative Sentence Linking Performance