Improving Argument Clarity with Discourse Markers

Argument Mining is a field that deals with figuring out and organizing arguments from written text. One important part of this task is the use of discourse markers, which are words or phrases that help to signal the relationship between parts of an argument. These markers can play a critical role in making the argument clearer and easier to understand.

In recent years, there has been a lot of interest in whether Language Models, which are computer programs designed to understand and generate human language, can automatically add these discourse markers to text. This can help improve the quality of the arguments that are produced from the text. In this article, we will examine how discourse markers can be added to text automatically and how this can impact the performance of argument mining systems.

#The Role of Discourse Markers

Discourse markers are essential in written communication because they guide the reader through the text. They provide signals that indicate the relationship between different parts of the argument, such as whether one statement supports another, contradicts it, or introduces a new idea. Examples of discourse markers include words like "however," "therefore," "for instance," and "in conclusion." These markers help clarify the writer's intent and make the structure of the argument more apparent.

When discourse markers are missing, the task of understanding the argument becomes more challenging. Readers may find it harder to grasp how different ideas are connected, which can lead to confusion. Consequently, argument mining systems that rely on identifying these connections may perform poorly if the necessary markers are absent. Therefore, enhancing text with appropriate discourse markers can help improve the effectiveness of these systems.

#The Problem with Current Language Models

Despite the advancements in language models, many of them struggle to add discourse markers effectively when given text without them. These models often do not recognize when a discourse marker is needed, resulting in text that lacks clarity and coherence. Initial tests with popular language models showed disappointing results in automatically inserting these markers into text.

To tackle this issue, researchers proposed Fine-tuning these language models on new Datasets that include both real examples and synthetic ones. By training them on a diverse range of texts, it is possible to improve their ability to predict and add discourse markers appropriately.

#Methodology

To investigate the effectiveness of language models in adding discourse markers, a specific methodology is employed. The process starts with creating a dataset designed to test the models' capabilities in this area. This dataset includes various examples of arguments, with some texts containing discourse markers and others lacking them. This allows for a systematic evaluation of the models' performance.

The language models are then trained using this dataset, focusing on their ability to predict the correct discourse marker for each argument. Different models are tested, including popular variants like BERT, T5, and BART, to see which performs best at adding the necessary markers.

#Training Datasets

The training datasets used in this research comprise various text corpora that cover different genres and styles. Some of these datasets include student essays, short responses to questions, and reviews from hotel guests. Each dataset is carefully annotated to identify discourse markers and the argument components they relate to.

To create a comprehensive collection of examples, synthetic datasets are also generated. These synthetic examples are formed using templates that outline the structure of arguments, allowing for the consistent insertion of discourse markers.

#Fine-tuning Language Models

Fine-tuning is a necessary step to ensure that language models can effectively add discourse markers. By using the specialized datasets mentioned earlier, the models learn to recognize patterns in the text that indicate where markers should be inserted.

Fine-tuning involves training the models on a combination of tasks that require them not only to fill in the missing discourse markers but also to understand the context in which these markers are used. This training enhances their capability to generate coherent and grammatically correct text.

#Evaluation of Model Performance

After training the models, their performance is evaluated on a separate set of texts. This evaluation assesses how well the models can fill in the missing discourse markers when given new examples. The focus is on both accuracy and the quality of the added markers.

Metrics used to measure performance include checking whether the markers correctly signal the intended relationship between the components of the argument. This evaluation helps identify which models excel at this task and which may still need improvement.

#Results and Findings

The results of the tests show that fine-tuned language models significantly outperform their zero-shot counterparts, which are models that have not undergone any specific training for this task. This demonstrates the importance of fine-tuning in enhancing the performance of language models for adding discourse markers.

While some models perform exceptionally well, others still struggle with certain types of discourse markers or specific contexts. The findings indicate that while progress has been made, there remain areas where improvements can be made to enhance the models' robustness across different text types.

#Implications for Argument Mining

The ability to automatically add discourse markers has important implications for argument mining. By enhancing the text with these markers, the structure of arguments becomes clearer and more accessible. This, in turn, can lead to improved performance in downstream tasks that rely on accurate argument identification and classification.

Furthermore, by employing language models that can fill in the missing markers effectively, researchers and practitioners can work towards creating more efficient argument mining systems. This could benefit various applications, including educational tools, writing assistants, and automated analysis of argumentative texts.

#Challenges Ahead

Despite the advancements made in this field, several challenges still remain. For instance, ensuring that models can handle a wide range of genres and contexts is crucial for their generalizability. Different types of text may require different discourse markers or have varying expectations for clarity and organization.

Additionally, there is the ongoing challenge of improving the models' ability to discern the subtle nuances of language that may influence the choice of discourse markers. Researchers will need to continue exploring ways to enhance these models and expand their training datasets to include more diverse examples.

#Conclusion

In conclusion, adding discourse markers to text using language models presents a promising avenue for improving argument mining systems. Fine-tuning these models on carefully curated datasets enhances their ability to predict and insert relevant discourse markers, making the argument structure clearer.

While significant progress has been made, further research is needed to address the remaining challenges and continue improving the robustness of these models. As technology advances, the potential for integrating automatic discourse marker addition into real-world applications will undoubtedly increase, leading to more effective communication and understanding of arguments in written text.

#Future Directions

Looking ahead, there are several potential directions for future research in this area. Exploring the use of larger and more sophisticated language models could lead to more accurate predictions of discourse markers. Additionally, expanding the range of training datasets to encompass a broader spectrum of writing styles and contexts may improve the models' generalizability.

The investigation into the relationship between discourse markers and specific types of argument structures is another promising avenue. Understanding which markers are most effective for specific argument types can guide the development of more nuanced models.

Moreover, integrating these models into practical applications, such as automated writing feedback tools or educational platforms, will further demonstrate the utility of discourse marker augmentation in enhancing communication.

In summary, the automatic addition of discourse markers represents a significant advancement in the field of argument mining. By leveraging the capabilities of modern language models and fine-tuning them on targeted datasets, we can create systems that make written arguments clearer and more effective, ultimately benefiting readers and writers alike.