64,025 research outputs found
The DistilBERT Model: A Promising Approach to Improve Machine Reading Comprehension Models
Machine Reading Comprehension (MRC) is a challenging task in the field of Natural Language Processing (NLP), where a machine is required to read a given text passage and answer a set of questions based on it. This paper provides an overview of recent advances in MRC and highlights some of the key challenges and future directions of this research area. It also evaluates the performance of several baseline models on the dataset, evaluates the challenges that the dataset poses for existing MRC models, and introduces the DistilBERT model to improve the accuracy of the answer extraction process. The supervised paradigm for training machine reading and comprehension models represents a practical path forward for creating comprehensive natural language understanding systems. To enhance the DistilBERT basic model's functionality, we have experimented with a variety of question heads that differ in the number of layers, activation function, and general structure. DistilBERT is a model for question-resolution tasks that is successful and delivers state-of-the-art performance while requiring less computational resources than large models like BERT, according to the presented technique. We could enhance the model's functionality and obtain a better understanding of how the model functions by investigating other question head architectures. These findings could serve as a foundation for future study on how to make question-and-answer systems and other tasks connected to the processing of natural languages.  
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension
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
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