Feature-augmented Machine Reading Comprehension with Auxiliary Tasks
While most successful approaches for machine reading comprehension rely on single training objective, it is assumed that the encoder layer can learn great representation through the loss function we define in the predict layer, which is cross entropy in most of time, in the case that we first use neural networks to encode the question and paragraph, then directly fuse the encoding result of them. However, due to the distantly loss backpropagating in reading comprehension, the encoder layer cannot learn effectively and be directly supervised. Thus, the encoder layer can not learn the representation well at any time. Base on this, we propose to inject multi granularity information to the encoding layer. Experiments demonstrate the effect of adding multi granularity information to the encoding layer can boost the performance of machine reading comprehension system. Finally, empirical study shows that our approach can be applied to many existing MRC models.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine Reading ComprehensionReading ComprehensionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension
Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these mo…
Machine Reading ComprehensionQuestion AnsweringReading ComprehensionTriviaQAEffective Character-augmented Word Embedding for Machine Reading Comprehension
Machine reading comprehension is a task to model relationship between passage and query. In terms of deep learning framework, most of state-of-the-art models simply concatenate word and character level representations, w…
Machine Reading ComprehensionReading ComprehensionForget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehension
The creation of large-scale open domain reading comprehension data sets in recent years has enabled the development of end-to-end neural comprehension models with promising results. To use these models for domains with l…
Domain AdaptationReading ComprehensionCross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation
Cross-lingual Machine Reading Comprehension (CLMRC) remains a challenging problem due to the lack of large-scale annotated datasets in low-source languages, such as Arabic, Hindi, and Vietnamese. Many previous approaches…
Knowledge DistillationMachine Reading ComprehensionReading ComprehensionTranslationSubword-augmented Embedding for Cloze Reading Comprehension
Representation learning is the foundation of machine reading comprehension. In state-of-the-art models, deep learning methods broadly use word and character level representations. However, character is not naturally the …
Machine Reading ComprehensionReading ComprehensionRepresentation Learning