Hybrid data combining both tabular and textual content (eg, financial reports) are quite
pervasive in the real world. However, Question Answering (QA) over such hybrid data is …

Question answering over knowledge graph (QA-KG) aims to use facts in the knowledge
graph (KG) to answer natural language questions. It helps end users more efficiently and …

We consider open-domain queston answering (QA) where answers are drawn from either a
corpus, a knowledge base (KB), or a combination of both of these. We focus on a setting in …

Open Domain Question Answering (QA) is evolving from complex pipelined systems to end-
to-end deep neural networks. Specialized neural models have been developed for …

Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples
into natural text, focused on domain-specific benchmark datasets. In this paper, however, we …

Typical techniques for video captioning follow the encoder-decoder framework, which can
only focus on one source video being processed. A potential disadvantage of such design is …

We present a novel way of injecting factual knowledge about entities into the pretrained
BERT model (Devlin et al., 2019): We align Wikipedia2Vec entity vectors (Yamada et al …

Answering complex natural language questions on knowledge graphs (KGQA) is a
challenging task. It requires reasoning with the input natural language questions as well as …

This paper introduces a new framework for open-domain question answering in which the
retriever and the reader iteratively interact with each other. The framework is agnostic to the …

When answering natural language questions over knowledge bases (KBs), different
question components and KB aspects play different roles. However, most existing …