Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension
This work addresses reading comprehension in NLP, offering a method to enhance model efficiency and data usage, though it appears incremental as it builds on existing transfer learning approaches.
The paper tackles reading comprehension by transferring knowledge from lower-level language tasks (e.g., textual entailment, named entity recognition) into a neural network model, resulting in significant performance improvements with fewer training steps and effectiveness with small datasets.
Reading comprehension is a challenging task in natural language processing and requires a set of skills to be solved. While current approaches focus on solving the task as a whole, in this paper, we propose to use a neural network `skill' transfer approach. We transfer knowledge from several lower-level language tasks (skills) including textual entailment, named entity recognition, paraphrase detection and question type classification into the reading comprehension model. We conduct an empirical evaluation and show that transferring language skill knowledge leads to significant improvements for the task with much fewer steps compared to the baseline model. We also show that the skill transfer approach is effective even with small amounts of training data. Another finding of this work is that using token-wise deep label supervision for text classification improves the performance of transfer learning.