Enhancing Biomedical Natural Language Inference Robustness through Data Augmentation with Generative Models and Domain Knowledge

Conceitos Básicos

A novel data augmentation approach leveraging generative models and biomedical knowledge graphs to improve the robustness of natural language inference models for analyzing clinical trial reports.

Resumo

This paper presents a novel data augmentation technique to enhance the robustness of natural language inference (NLI) models for analyzing biomedical content, particularly clinical trial reports (CTRs). The key highlights are:

1. Numerical Question-Answering Task Generation: The authors leverage GPT-3.5 to generate synthetic data for a numerical question-answering task, aiming to improve the model's capabilities in numerical and quantitative reasoning.

2. Semantic Perturbation: The authors use GPT-3.5 to generate both semantically-preserving and semantically-altering variants of the original entailed statements, expanding the diversity of the training data.

3. Vocabulary Replacement: The authors employ biomedical knowledge graphs and statistical methods to identify and replace domain-specific keywords in the original statements, further enhancing the model's understanding of the biomedical vocabulary.

4. Multi-Task Learning: The authors combine the main NLI task with the numerical question-answering task, leveraging the complementary strengths of these objectives to improve the model's overall performance and robustness.

5. Evaluation: The authors evaluate their approach on the NLI4CT 2024 dataset, demonstrating significant improvements in faithfulness and consistency compared to the original DeBERTa models. Their best-performing model ranked 12th in terms of faithfulness and 8th in terms of consistency out of 32 participants.

The authors discuss the trade-offs between improving robustness to interventions and maintaining strong performance on the original data, and propose future directions to address these challenges.

Estatísticas

57% of patients in cohort 1 of the primary trial had pathological complete response rates at surgery.
The NLI4CT 2024 dataset contains 1,700 training samples, 2,142 validation samples, and 5,500 test samples.

Citações

"Safe and reliable natural language inference is critical for extracting insights from clinical trial reports but poses challenges due to biases in large pre-trained language models."
"By generating synthetic examples through semantic perturbations and domain-specific vocabulary replacement and adding a new task for numerical and quantitative reasoning, we introduce greater diversity and reduce shortcut learning."

Principais Insights Extraídos De

DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness

by Yuqi Wang,Ze... às arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09206.pdf

Perguntas Mais Profundas

How can the quality of the generated augmented data be further improved to minimize the trade-off between robustness and performance on the original data?

To enhance the quality of the generated augmented data and reduce the trade-off between robustness and performance on the original data, several strategies can be implemented:

Fine-tuning Generative Models: Fine-tuning the generative models used for data augmentation can help improve the quality of the generated samples. By training the models on domain-specific data or incorporating feedback mechanisms to refine the generated outputs, the relevance and coherence of the augmented data can be enhanced.

Adversarial Filtering: Implementing an adversarial filtering mechanism can help identify and remove noisy or irrelevant augmented samples. By training a discriminator to distinguish between original and generated data, only high-quality augmented examples that closely resemble the original data can be retained.

Human Validation: Introducing a human validation step where domain experts review and validate the augmented data can ensure its quality and relevance. Human annotators can provide feedback on the generated samples, helping to filter out inaccuracies and maintain the integrity of the augmented dataset.

Diverse Augmentation Techniques: Incorporating a diverse range of augmentation techniques beyond semantic perturbations and vocabulary replacement can introduce more varied and meaningful data. Techniques such as paraphrasing, summarization, or knowledge graph integration can offer different perspectives and enrich the augmented dataset.

Balancing Quantity and Quality: Striking a balance between generating a sufficient quantity of augmented data and ensuring its quality is crucial. Prioritizing the generation of high-quality, contextually relevant samples over sheer volume can help minimize the trade-off between robustness and performance on the original data.

How can the proposed techniques be extended to other healthcare-related natural language processing tasks beyond natural language inference?

The techniques proposed in the context of enhancing natural language inference for clinical trial reports can be extended to other healthcare-related NLP tasks by considering the following approaches:

Task-Specific Data Augmentation: Tailoring data augmentation techniques to suit the requirements of specific healthcare NLP tasks can enhance model performance. For tasks like medical entity recognition, relation extraction, or summarization, task-specific augmentation strategies can be devised to generate relevant training data.

Domain-Specific Knowledge Integration: Beyond biomedical knowledge graphs, incorporating other domain-specific knowledge sources such as medical ontologies, clinical guidelines, or electronic health records can enrich the model's understanding of healthcare data. Integrating structured knowledge with unstructured text data can provide comprehensive insights for various NLP tasks.

Multi-Task Learning: Extending the multi-task learning approach to incorporate multiple healthcare-related NLP tasks can leverage shared representations and improve model generalization. By jointly training models on tasks like diagnosis prediction, symptom extraction, and treatment recommendation, the model can learn diverse aspects of healthcare data processing.

Transfer Learning: Leveraging pre-trained language models fine-tuned on a broad range of healthcare NLP tasks can expedite model training and enhance performance. Transfer learning techniques can enable the transfer of knowledge learned from one task to another, facilitating efficient learning across different healthcare NLP domains.

Evaluation and Validation: Rigorous evaluation and validation of the proposed techniques on diverse healthcare NLP tasks are essential to assess their effectiveness and generalizability. Conducting thorough experiments on tasks like medical question answering, clinical document classification, or patient outcome prediction can validate the scalability and applicability of the techniques beyond natural language inference in clinical trials.