Improving Document-Level Relation Extraction with Distant Supervision

In recent years, the task of finding and understanding relationships between different pieces of information in documents has gained attention. This task, known as Document-level Relation Extraction, is challenging because it involves looking at many pieces of information at once rather than just one sentence at a time. Traditional methods often rely on data that is carefully labeled by humans, but this approach can be slow and expensive.

On the other hand, there is a method called Distant Supervision, where data can be automatically labeled based on existing information. While this method can provide a lot of data quickly, it often comes with errors because the labeling might not always be accurate. Our goal is to improve how we use this distant supervision data efficiently while also addressing the noise that often comes with it.

#The Challenge of Document-Level Relation Extraction

Document-level relation extraction, or DocRE, aims to identify relationships between different entities mentioned in a document. Unlike traditional methods that focus only on sentence-level relationships, DocRE must consider multiple entities and their relationships throughout an entire document. This can involve many facts and relationships, making the task more complicated.

One major issue with DocRE is that obtaining human-annotated data is expensive. As a result, there is a limited amount of such data available for Training Models. Distant supervision helps address this problem by automatically generating labels based on existing knowledge databases. However, the downside is that the data obtained this way can often be noisy or contain inaccuracies, which can confuse the models.

#Utilizing Distant Supervision Effectively

Distant supervision can greatly increase the amount of data available for training models. In sentence-level relation extraction, this approach has already shown potential, but it has not been as effective for document-level tasks due to the complications involved.

To make better use of distant supervision in DocRE, we propose a new approach that involves two main steps. First, we identify and select the most informative documents from the distant supervision dataset. Instead of training models on all the data, which can be inefficient, we focus on the subset that is likely to provide the most useful information.

The second step is to train the models using a new loss function that takes into account multiple sources of supervision. This means combining information from the distant supervision labels, predictions made by an expert model, and self-generated predictions from the training model itself. By integrating these different sources, we aim to reduce the negative impact of any inaccurate noisy labels present in the distant supervision data.

#Document Informativeness Ranking

To find the most informative documents among the distant supervision data, we use a method called Document Informativeness Ranking, or DIR. This method assesses the quality of the information within each document based on its reliability and value.

We categorize the relationship classes identified in the documents into three groups. The first group includes agreements, meaning that both the distant supervision labels and the expert model predictions match. The second group includes recommendations, where either the labels or predictions suggest a relationship, but not both. Finally, the third group consists of relationships that are neither indicated by the distant supervision labels nor by expert predictions.

By using the DIR method, we can rank the documents based on their informativeness. This helps us select a smaller set of documents that contain higher quality information, ultimately leading to more effective training of the model.

#Multi-Supervision Ranking-Based Loss

Our training process uses a new loss function called Multi-Supervision Ranking-based Loss, or MSRL. This method builds upon previous loss functions but adds the ability to draw on multiple sources of information.

The MSRL focuses on pushing the agreements above a certain threshold while keeping the others below it. For the recommendations, it allows them to be positioned flexibly without strict ranking rules. This way, we prioritize learning from the more reliable information while still gathering insights from the recommendations.

This multi-supervision approach allows us to adjust how we weigh the different labels during training, which helps in reducing the effects of noisy labels from the distant supervision. The MSRL is a significant advancement over previous methods that typically relied on a single supervision source, making it more robust against inaccuracies.

#Experimental Setup

To demonstrate the effectiveness of our proposed method, we conducted experiments using the DocRED dataset, which is a popular benchmark for document-level relation extraction. This dataset consists of documents sourced from Wikipedia and contains both human-annotated data and distant supervision data.

We compared our approach with several existing methods that also aim to utilize distant supervision. Our goal was to assess how well our method performed in terms of accuracy while also keeping an eye on the time required for training.

#Results and Analysis

The results of our experiments indicate that using distant supervision data can significantly improve the performance of document-level relation extraction models. Even when using just a small portion of the distant supervision data, our method showed promising results.

For example, when we applied our method to retrieve a subset of the most informative documents, we achieved better accuracy compared to methods that used all the distant supervision data. This was accomplished while keeping the time costs lower, which is a crucial factor for real-world applications.

Our approach not only improves model performance but also makes the training process more efficient. By focusing on the most useful data and employing multi-supervision, we can effectively counter the issues caused by inaccurate labels.

#Case Study

To illustrate the success of our method, we examined a specific case where our Document Informativeness Ranking identified a document containing instances with varying degrees of informativeness. By analyzing the logit values, we could see how our approach enabled the model to learn from the most relevant labels while also adjusting its understanding based on the context of the document.

This case study further demonstrates that the combination of distant supervision, expert predictions, and self-predictions allows the model to learn in a more adaptable and thorough manner.

#Conclusion

Our research introduces an innovative approach to improve the efficiency and effectiveness of document-level relation extraction using distant supervision. By focusing on the most informative documents and employing a new multi-supervision loss function, we can enhance model performance while minimizing time costs.

Despite the progress made, we acknowledge certain limitations. The quality of the expert model is essential for our method's success, and the information present in documents can still be sparse. Additionally, while our approach shows promise, further research is needed to explore its compatibility with various model architectures.

In summary, our method provides a pathway for better utilizing distant supervision data in document-level relation extraction tasks, paving the way for more efficient and accurate information retrieval in complex documents.