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 …

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

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 in a …

Conversational machine comprehension (CMC) requires understanding the context of multi-turn
dialogue. Using BERT, a pre-training language model, has been successful for single-…

This study focuses on acquisition of commonsense knowledge. A previous study proposed
a commonsense knowledge base completion (CKB completion) method that predicts a …

Pre-trained sequence-to-sequence (seq-to-seq) models have significantly improved the
accuracy of several language generation tasks, including abstractive summarization. Although …

Having consistent personalities is important for chatbots if we want them to be believable.
Typically, many question-answer pairs are prepared by hand for achieving consistent …

We explored the gaze behavior towards the end of utterances and dialogue act (DA), ie,
verbal-behavior information indicating the intension of an utterance, during turn-keeping/…

We propose a model to automatically generate whole body motions accompanying
utterances at appropriate times, similar to humans, by using various types of natural-language-…

We propose a new length-controllable abstractive summarization model. Recent state-of-the-art
abstractive summarization models based on encoder-decoder models generate only …