31 citations · 127 across the 9 of their papers we have counts for
9 papers · 1 filter
SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images
Ryota Tanaka, Kyosuke Nishida, Kosuke Nishida +3
Visual question answering on document images that contain textual, visual, and layout information, called document VQA, has received much attention recently. Although many datasets…
Towards Interpretable and Reliable Reading Comprehension: A Pipeline Model with Unanswerability Prediction
Kosuke Nishida, Kyosuke Nishida, Itsumi Saito +1
Multi-hop QA with annotated supporting facts, which is the task of reading comprehension (RC) considering the interpretability of the answer, has been extensively studied. In this…
Abstractive Summarization with Combination of Pre-trained Sequence-to-Sequence and Saliency Models
Itsumi Saito, Kyosuke Nishida, Kosuke Nishida +1
Pre-trained sequence-to-sequence (seq-to-seq) models have significantly improved the accuracy of several language generation tasks, including abstractive summarization. Although th…
Length-controllable Abstractive Summarization by Guiding with Summary Prototype
Itsumi Saito, Kyosuke Nishida, Kosuke Nishida +5
We propose a new length-controllable abstractive summarization model. Recent state-of-the-art abstractive summarization models based on encoder-decoder models generate only one sum…
Unsupervised Domain Adaptation of Language Models for Reading Comprehension
Kosuke Nishida, Kyosuke Nishida, Itsumi Saito +2
This study tackles unsupervised domain adaptation of reading comprehension (UDARC). Reading comprehension (RC) is a task to learn the capability for question answering with textual…
A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension
Yasuhito Ohsugi, Itsumi Saito, Kyosuke Nishida +2
Conversational machine comprehension (CMC) requires understanding the context of multi-turn dialogue. Using BERT, a pre-training language model, has been successful for single-turn…
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction
Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata +4
Question answering (QA) using textual sources for purposes such as reading comprehension (RC) has attracted much attention. This study focuses on the task of explainable multi-hop…
Multi-style Generative Reading Comprehension
Kyosuke Nishida, Itsumi Saito, Kosuke Nishida +4
This study tackles generative reading comprehension (RC), which consists of answering questions based on textual evidence and natural language generation (NLG). We propose a multi-…
Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension
Kyosuke Nishida, Itsumi Saito, Atsushi Otsuka +2
This study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the information retrieval (IR) task of finding relevant passages…