Neural End-to-End Learning for Computational Argumentation Mining (1704.06104v2)

Abstract: We investigate neural techniques for end-to-end computational argumentation mining (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 BiLSTMs 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.

Neural End-to-End Learning for Computational Argumentation Mining

The paper "Neural End-to-End Learning for Computational Argumentation Mining" explores neural methods for end-to-end argumentation mining (AM). AM involves identifying argument structures in text, which includes tasks like segmenting argumentative units, classifying them into types (e.g., "Premise" or "Claim"), finding relationships between these components, and classifying relationships as types like "Support" or "Attack". The research investigates AM as both a token-based dependency parsing and sequence tagging problem, and examines a multi-task learning (MTL) setup.

Key Findings and Methodologies

1. Improvement over Feature-Based Approaches: The authors demonstrate that their neural approaches, without handcrafted features or constraints, outperform traditional models that rely heavily on manual feature engineering and integer linear programming (ILP) for structure enforcement. This marks a significant advancement in achieving robust AM without labor-intensive data-specific preprocessing.
2. Dependency Parsing Framing: The paper tests AM framed as a dependency parsing problem, a method which traditionally captures tree-like structures corresponding to argumentation structures. However, the experiments reveal that token-based dependency parsing yields suboptimal results compared to sequence-based approaches, likely because of data sparsity and model complexity issues.
3. Sequence Tagging Framing: Contrastingly, framing AM as a sequence tagging problem—utilizing BiLSTM-CRF-CNN models—provides robust results in identifying both argument components and their relations, particularly when paired with a straightforward label set that encodes component and relational information.
4. Multi-Task Learning: The paper finds that incorporating MTL, where subtasks such as component identification filter into the main task, further enhances performance. The results suggest that leveraging related tasks can lead to beneficial parameter sharing, thus improving overall AM precision and recall.
5. LSTM-ER Model: The authors evaluate an LSTM-ER model that decouples entity and relational identification but models them jointly. While this model shows promise on paragraph-level performance, capturing both components and relations effectively, it underperforms on the essay level for relation identification, likely due to an increased complexity and number of potential relations in longer texts.
6. Empirical Results: The paper presents empirical results using a dataset of student essays. The neural models outperform the prior state-of-the-art ILP method, especially on paragraph-level text. The authors conduct a detailed error analysis, showing specific challenges in disambiguating component types like claims and premises.
7. Implications for Future Research: This paper's findings suggest several implications for future AI developments. The success of neural models without handcrafted features indicates potential for automated argument mining tools in educational, legal, and other domains. Moreover, the contrast between sequence tagging and parsing methods might inform future model selection approaches for similar tasks. Lastly, the modest yet noticeable impact of MTL highlights an area ripe for further exploration in model efficiency and predictive performance.

In summary, this research offers valuable insights into neural approaches for AM, presenting a comprehensive evaluation of different framings and architectures. It advances the understanding of end-to-end neural methods, setting a new benchmark in the field by showcasing the effectiveness of sequence tagging and multi-task learning in overcoming the limitations of dependency parsing and feature-based models.