Machine Reading Comprehension (MRC) is a challenging Natural Language Processing
(NLP) research field with wide real-world applications. The great progress of this field in …

Machine Reading Comprehension (MRC) is a challenging task and hot topic in Natural
Language Processing. The goal of this field is to develop systems for answering the …

Building models that can be rapidly adapted to novel tasks using only a handful of annotated
examples is an open challenge for multimodal machine learning research. We introduce …

Large-scale pretrained foundation models have been an emerging paradigm for building
artificial intelligence (AI) systems, which can be quickly adapted to a wide range of …

This paper is concerned with the task of multi-hop open-domain Question Answering (QA).
This task is particularly challenging since it requires the simultaneous performance of textual …

Machine reading comprehension (MRC) aims to teach machines to read and comprehend
human languages, which is a long-standing goal of natural language processing (NLP) …

Large language models can produce fluent dialogue but often hallucinate factual
inaccuracies. While retrieval-augmented models help alleviate this issue, they still face a …

This study tackles generative reading comprehension (RC), which consists of answering
questions based on textual evidence and natural language generation (NLG). We propose a …

In the ITS (Intelligent Tutoring System) services, personalized question recommendation is a
critical function in which the key challenge is to predict the difficulty of each question. Given …

Multi-choice Machine Reading Comprehension (MRC) requires models to decide the correct
answer from a set of answer options when given a passage and a question. Thus, in …