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Neural End-to-End Learning for Computational Argumentation Mining

Abstract:

We investigate neural techniques for end-to-end computational argumentation min-ing (AM). We frame AM both as a token-based dependency parsing and as a token-based sequence tagging problem, including a multi-task learning setup. Contrary to models that operate on the argument component level, we find that framing AM as dependency parsing leads to subpar performance results. In contrast, less complex (local) tagging models based on BiL-STMs perform robustly across classification scenarios, being able to catch long-range dependencies inherent to the AM problem. Moreover, we find that jointly learning ‘natural’ subtasks, in a multi-task learning setup, improves performance.

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# Title Author Topic Medium Score
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16 Recurrent Neural Networks Tutorial, Part 1 - Introduction to RNNs Denny Britz 741 tutorial 129.71
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75 Python TensorFlow Tutorial – Build a Neural Network Andy Thomas 731 tutorial 96.58
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