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 …

Question answering (QA) systems provide a way of querying the information available in
various formats including, but not limited to, unstructured and structured data in natural …

Conversational information seeking (CIS) is concerned with a sequence of interactions
between one or more users and an information system. Interactions in CIS are primarily …

CQA systems aim to create interactive search systems that effectively retrieve information by
interacting with users. To replicate human-to-human conversations, existing work uses …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Early studies mainly focused on answering simple questions over KBs and …

Conversational question answering (QA) requires the ability to correctly interpret a question
in the context of previous conversation turns. We address the conversational QA task by …

Conversational question answering (ConvQA) tackles sequential information needs where
contexts in follow-up questions are left implicit. Current ConvQA systems operate over …

The rise of personal assistants has made conversational question answering (ConvQA) a
very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA …

In conversational question answering, users express their information needs through a
series of utterances with incomplete context. Typical ConvQA methods rely on a single …

This paper addresses the task of (complex) conversational question answering over a
knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with …