205 research outputs found
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
This paper tackles the problem of reading comprehension over long narratives
where documents easily span over thousands of tokens. We propose a curriculum
learning (CL) based Pointer-Generator framework for reading/sampling over large
documents, enabling diverse training of the neural model based on the notion of
alternating contextual difficulty. This can be interpreted as a form of domain
randomization and/or generative pretraining during training. To this end, the
usage of the Pointer-Generator softens the requirement of having the answer
within the context, enabling us to construct diverse training samples for
learning. Additionally, we propose a new Introspective Alignment Layer (IAL),
which reasons over decomposed alignments using block-based self-attention. We
evaluate our proposed method on the NarrativeQA reading comprehension
benchmark, achieving state-of-the-art performance, improving existing baselines
by relative improvement on BLEU-4 and relative improvement on
Rouge-L. Extensive ablations confirm the effectiveness of our proposed IAL and
CL components.Comment: Accepted to ACL 201
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.  
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