Alongside huge volumes of research on deep learning models in NLP in the recent years,
there has been much work on benchmark datasets needed to track modeling progress …

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) …

Retrieval-Augmented Generation (RAG) has become a standard architectural pattern for
incorporating domain-specific knowledge into user-facing chat applications powered by …

Visual contents, such as illustrations and images, play a big role in product manual
understanding. Existing Product Manual Question Answering (PMQA) datasets tend to …

In recent years, the need for community technical question-answering sites has increased
significantly. However, it is often expensive for human experts to provide timely and helpful …

Recent advances in Retrieval-Augmented Generation (RAG) systems have popularized
semantic chunking, which aims to improve retrieval performance by dividing documents into …

We propose a simple and effective strategy for data augmentation for low-resource machine
reading comprehension (MRC). Our approach first pretrains the answer extraction …

Answering questions asked from instructional corpora such as E-manuals, recipe books,
etc., has been far less studied than open-domain factoid context-based question answering …

Question answering (QA) is one of the most challenging and impactful tasks in natural
language processing. Most research in QA, however, has focused on the open-domain or …

A question answering (QA) system is a type of conversational AI that generates natural
language answers to questions posed by human users. QA systems often form the …