Improving Relation Extraction by Mitigating Over-Dependency on Entities through Adversarial Training

Core Concepts

Current relation extraction models excessively rely on entities, making them vulnerable to adversarial attacks and limiting their generalization. An adversarial training method is proposed to address this issue by introducing both sequence- and token-level perturbations and a probabilistic strategy to encourage the model to leverage relational patterns in the context.

Abstract

The paper analyzes the performance of state-of-the-art relation extraction (RE) models under adversarial attacks and finds that they exhibit an over-dependency on entities, making them vulnerable to attacks and limiting their generalization. To address this issue, the authors propose a novel adversarial training method called READ (improving Relation Extraction from an ADversarial perspective).
Key highlights:

Adversarial attacks reveal that current RE models rely excessively on entities, with entities being more frequently targeted and more vulnerable to attacks compared to context.
READ introduces both sequence- and token-level perturbations to the RE samples during training, using separate perturbation vocabularies for entities and context.
READ also employs a probabilistic strategy to leave some context tokens unperturbed, encouraging the model to leverage relational patterns in the context.
Extensive experiments on three RE datasets show that READ significantly improves the accuracy and robustness of the model compared to various adversarial training methods, especially in low-resource scenarios.
In-depth analyses provide further insights into the effectiveness of READ's design choices.

Stats

The paper reports the following key metrics:

Clean accuracy (the model accuracy on clean examples)
Accuracy under attack (the model accuracy on adversarial examples)
Number of queries (the average number of queries the attacker required to perform successful attacks)
Entity Freq (how frequently the entity is attacked)
Entity Ratio (the proportion of the perturbed entity in all perturbed tokens)
Entity AS (the attack success rate of entity)
Context AS (the attack success rate of context)

Quotes

"Our adversarial attack experiments show that these works excessively rely on entities, making their generalization capability questionable."
"To address this issue, we propose an adversarial training method specifically designed for RE."
"Extensive experiments show that compared to various adversarial training methods, our method significantly improves both the accuracy and robustness of the model."

Key Insights Distilled From

READ

by Dawei Li,Wil... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.02931.pdf

Deeper Inquiries

How can the proposed adversarial training method be extended to other NLP tasks beyond relation extraction

The proposed adversarial training method can be extended to other NLP tasks beyond relation extraction by adapting the approach to suit the specific requirements of the task at hand. For tasks like sentiment analysis, text classification, or named entity recognition, the adversarial training can be modified to focus on the key elements relevant to those tasks. For example, in sentiment analysis, the adversarial training can target words or phrases that are crucial for determining sentiment polarity. Similarly, in named entity recognition, the training can be tailored to perturb tokens related to entities to enhance the model's robustness in identifying named entities accurately. By customizing the adversarial training process to the specific characteristics of different NLP tasks, the method can be effectively applied to a wide range of tasks beyond relation extraction.

What are the potential drawbacks or limitations of the probabilistic clean token leaving strategy, and how can it be further improved

The probabilistic clean token leaving strategy, while effective in improving the model's robustness and generalization, may have some potential drawbacks or limitations. One limitation could be the selection of the clean tokens to leave untouched during training. The randomness in choosing which tokens to mask may not always result in the most informative tokens being preserved. To address this limitation, the strategy could be enhanced by incorporating a more sophisticated token selection mechanism based on token importance or relevance to the task. Additionally, the clean token leaving probability could be dynamically adjusted during training based on the model's performance, allowing for adaptive fine-tuning of the strategy. By refining the token selection process and introducing adaptive mechanisms, the strategy can be further improved to enhance the model's learning from the context.

Given the observed over-dependency on entities, how can the model be encouraged to learn more from the context in a principled way beyond adversarial training

To encourage the model to learn more from the context in a principled way beyond adversarial training, additional techniques can be employed. One approach could involve incorporating explicit mechanisms during training that emphasize the importance of contextual information. For instance, introducing attention mechanisms that prioritize contextual tokens during relation extraction can guide the model to focus on relational patterns embedded in the context. Furthermore, leveraging reinforcement learning techniques to reward the model for correctly utilizing contextual information can incentivize the model to rely less on entities and more on the surrounding context. By integrating these principled approaches into the training process, the model can be encouraged to learn more effectively from the context and reduce its over-dependency on entities.