Meta Answering for Machine Reading
We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment. The environment encapsulates a competitive machine reader based on BERT, providing candidate answers to questions, and possibly some context. To validate the realism of our formulation, we ask humans to play the role of a meta-answerer. With just a small snippet of text around an answer, humans can outperform the machine reader, improving recall. Similarly, a simple machine meta-answerer outperforms the environment, improving both precision and recall on the Natural Questions dataset. The system relies on joint training of answer scoring and the selection of conditioning information.
Code (0)
등록된 구현이 없습니다.
Tasks
Natural QuestionsQuestion AnsweringReading ComprehensionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering
Multiple-choice question answering (MCQA) is one of the most challenging tasks in machine reading comprehension since it requires more advanced reading comprehension skills such as logical reasoning, summarization, and a…
Domain AdaptationLogical ReasoningMachine Reading ComprehensionMeta-Learning+5Training a Ranking Function for Open-Domain Question Answering
In recent years, there have been amazing advances in deep learning methods for machine reading. In machine reading, the machine reader has to extract the answer from the given ground truth paragraph. Recently, the state-…
Information RetrievalOpen-Domain Question AnsweringQuestion AnsweringReading Comprehension+3Bridging 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 ComprehensionMedical Knowledge Graph QA for Drug-Drug Interaction Prediction based on Multi-hop Machine Reading Comprehension
Drug-drug interaction prediction is a crucial issue in molecular biology. Traditional methods of observing drug-drug interactions through medical experiments require significant resources and labor. This paper presents a…
Entity EmbeddingsGraph Neural NetworkGraph Question AnsweringMachine Reading Comprehension+2