Combining Transformers with Natural Language Explanations
This work addresses the need for interpretable models in NLP applications, offering a domain-specific solution that is incremental in nature.
The paper tackles the problem of interpretability in transformer models by integrating external natural language explanations to explain classification outputs, achieving relevant explanations while maintaining or improving classification performance on legal text analysis and argument mining tasks.
Many NLP applications require models to be interpretable. However, many successful neural architectures, including transformers, still lack effective interpretation methods. A possible solution could rely on building explanations from domain knowledge, which is often available as plain, natural language text. We thus propose an extension to transformer models that makes use of external memories to store natural language explanations and use them to explain classification outputs. We conduct an experimental evaluation on two domains, legal text analysis and argument mining, to show that our approach can produce relevant explanations while retaining or even improving classification performance.