While RST parsers are valuable for extracting discourse structures from text, they come with certain limitations that can impact the performance of models like RST-LoRA. Some potential drawbacks of relying on an RST parser include:
Parser Accuracy: The accuracy of the RST parser can significantly impact the quality of the extracted discourse structures. Inaccuracies or errors in parsing can lead to incorrect or incomplete representations of the text's rhetorical relationships.
Parser Dependency: Relying on an external RST parser introduces an additional dependency in the model pipeline. Changes or updates to the parser could affect the overall performance of the system.
Computational Cost: Parsing text to extract RST structures can be computationally expensive, especially for large documents or datasets. This could impact the scalability and efficiency of the model.
To address these limitations and improve the model's performance, several strategies can be implemented:
Parser Improvement: Continuously refining and enhancing the RST parser to improve accuracy and robustness. This could involve fine-tuning the parser on domain-specific data or incorporating more advanced parsing techniques.
Error Handling: Implementing mechanisms to handle errors or inconsistencies in the parser output, such as incorporating uncertainty estimates or fallback strategies when parsing results are unreliable.
End-to-End Training: Exploring end-to-end training approaches where the model learns to extract discourse structures directly from the text without relying on an external parser. This could potentially reduce dependency on the parser and improve overall performance.
By addressing these limitations and optimizing the integration of RST structures into the model, the RST-LoRA approach can achieve more accurate and reliable results in long document summarization and other NLP tasks.