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 …

Interpretable multi-hop reading comprehension (RC) over multiple documents is a
challenging problem because it demands reasoning over multiple information sources and …

Question answering (QA) using textual sources for purposes such as reading
comprehension (RC) has attracted much attention. This study focuses on the task of …

Multi-choice reading comprehension is a challenging task to select an answer from a set of
candidate options when given passage and question. Previous approaches usually only …

Moment retrieval in videos is a challenging task that aims to retrieve the most relevant video
moment in an untrimmed video given a sentence description. Previous methods tend to …

Multi-hop question answering requires models to gather information from different parts of a
text to answer a question. Most current approaches learn to address this task in an end-to …

Evidence retrieval is a critical stage of question answering (QA), necessary not only to
improve performance, but also to explain the decisions of the corresponding QA method. We …

This paper presents a systematic review of benchmarks and approaches for explainability in
Machine Reading Comprehension (MRC). We present how the representation and …

With the increasing number of clinical trial reports generated every day, it is becoming hard
to keep up with novel discoveries that inform evidence-based healthcare recommendations …

Neural models have achieved great success on machine reading comprehension (MRC),
many of which typically consist of two components: an evidence extractor and an answer …