Incremental Reading for Question Answering
Any system which performs goal-directed continual learning must not only learn incrementally but process and absorb information incrementally. Such a system also has to understand when its goals have been achieved. In this paper, we consider these issues in the context of question answering. Current state-of-the-art question answering models reason over an entire passage, not incrementally. As we will show, naive approaches to incremental reading, such as restriction to unidirectional language models in the model, perform poorly. We present extensions to the DocQA [2] model to allow incremental reading without loss of accuracy. The model also jointly learns to provide the best answer given the text that is seen so far and predict whether this best-so-far answer is sufficient.
Code (0)
등록된 구현이 없습니다.
Tasks
Continual LearningQuestion AnsweringSimilar Papers 제목 키워드 기반
How to Pre-Train Your Model? Comparison of Different Pre-Training Models for Biomedical Question Answering
Using deep learning models on small scale datasets would result in overfitting. To overcome this problem, the process of pre-training a model and fine-tuning it to the small scale dataset has been used extensively in dom…
Open-Domain Question AnsweringQuestion AnsweringReading ComprehensionEvoWiki: Incremental State Overwriting and Traceable Question Answering for Cross-Meeting Knowledge Evolution
In long-term collaboration spanning multiple meetings, factual states such as decisions and risks are continually revised, overturned, and replaced. Existing long-context methods typically stack the entire history, while…
Question AnsweringMedical Exam Question Answering with Large-scale Reading Comprehension
Reading and understanding text is one important component in computer aided diagnosis in clinical medicine, also being a major research problem in the field of NLP. In this work, we introduce a question-answering task ca…
MedQAQuestion AnsweringReading ComprehensionBridging Information-Seeking Human Gaze and Machine Reading Comprehension
In this work, we analyze how human gaze during reading comprehension is conditioned on the given reading comprehension question, and whether this signal can be beneficial for machine reading comprehension. To this end, w…
Machine Reading ComprehensionMultiple-choiceQuestion AnsweringReading ComprehensionNeural Arabic Question Answering
This paper tackles the problem of open domain factual Arabic question answering (QA) using Wikipedia as our knowledge source. This constrains the answer of any question to be a span of text in Wikipedia. Open domain QA f…
ArticlesInformation RetrievalMachine Reading ComprehensionMachine Translation+5