Reasoning over tabular data requires both table structure understanding and a broad set of
table reasoning skills. Current models with table-specific architectures and pre-training …

Conversational Question Generation (CQG) is a critical task for machines to assist humans
in fulfilling their information needs through conversations. The task is generally cast into two …

Question answering (QA) has demonstrated impressive progress in answering questions
from customized domains. Nevertheless, domain adaptation remains one of the most elusive …

This literature review gives an overview of current approaches to perform domain adaptation
in a low-resource and approaches to perform multilingual semantic search in a low-resource …

The field of Question Answering (QA) has made remarkable progress in recent years, thanks
to the advent of large pre-trained language models, newer realistic benchmark datasets with …

Neural ranking (NR) has become a key component for open-domain question-answering in
order to access external knowledge. However, training a good NR model requires …

There have been growing concerns regarding the out-of-domain generalization ability of
natural language processing (NLP) models, particularly in question-answering (QA) tasks …

Most previous unsupervised domain adaptation (UDA) methods for question answering (QA)
require access to source domain data while fine-tuning the model for the target domain …

Question answering (QA) has recently shown impressive results for answering questions
from customized domains. Yet, a common challenge is to adapt QA models to an unseen …

Abstract Existing Question Answering (QA) systems are limited in their ability to answer
questions from unseen domains or any out-of-domain distributions, making them less …