Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks
The paper "Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks" by Christian Stab, Tristan Miller, and Iryna Gurevych explores the development of an automated approach for argument mining (AM) capable of functioning across diverse and controversial topics. The authors propose a novel annotation scheme combined with advanced neural network models to enhance the effectiveness of argument mining from heterogeneous text sources on the web.
Key Contributions
One of the central challenges the paper addresses is the diversity of argument representation across various sources and topics, a common limitation of existing AM approaches. These approaches often rely on models trained on specific text types or domains, which struggle when generalized to novel topics or heterogeneous datasets. In response, the authors present three major contributions:
- Annotation Scheme: The paper introduces a sentential annotation scheme that non-experts can reliably apply to diverse web texts. This scheme focuses on classifying sentences as "supporting argument," "opposing argument," or "no argument" concerning a particular topic. The authors demonstrate that crowd workers can use this scheme effectively, achieving a Cohen's κ of 0.723, comparable to expert annotators.
- Corpus Creation: Using this scheme, the authors compiled a corpus including over 25,000 annotated sentences across eight controversial topics. Each topic consists of texts from assorted sources such as news reports, editorials, blogs, debate forums, and encyclopedia articles. This dataset provides a platform for evaluating cross-topic argument mining models.
- Neural Network Models: The authors propose several neural network architectures for AM, specifically, a bidirectional long short-term memory network (BiLSTM), a BiLSTM enhanced with topic similarity features, and an inner-attention BiLSTM focusing on weighing sentence parts relevant to the topic. Their approach using an inner-attention BiLSTM model demonstrated superior generalization capabilities. The model performed well across unseen topics, registering a 6% improvement in accuracy and an 11% increase in F-score over vanilla BiLSTM models in cross-topic scenarios.
Results and Discussion
The results from the models articulated that incorporating topic relevance and attention mechanisms significantly improves the ability of the system to detect and classify arguments in sentences. More precisely, the inner-attention BiLSTM model showed notable advantages by identifying topic-relevant parts of sentences and boosting cross-topic robustness.
Among practical implications, such models could revolutionize argument retrieval and classification tasks in domains like legal reasoning, persuasive writing, and decision-making processes where arguments are derived from varied text sources. Theoretically, the methodology emphasizes the importance of attention mechanisms and contextual embedding in neural networks for AM within diverse textual environments.
Future Directions
The paper opens several avenues for further exploration in the domain of automated AM. Future work could delve into refining these models to handle even broader and more complex argument structures. Additionally, incorporating contextual embeddings and advanced pre-trained models, such as transformer-based architectures, may further improve performance across new topics. There is also potential for exploring multi-modal texts that include both visual data and arguments in natural language.
In summary, this paper contributes significantly to the field of argument mining by developing a crowd-sourced annotation scheme and implementing attention-based neural networks, enhancing performance beyond existing model capabilities across heterogeneous and novel text topics. The implications span both practical applications and theoretical advancements, offering foundational insights for subsequent research in automated argumentation systems.