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 …

Deep learning approaches have achieved great success in the field of Natural Language
Processing (NLP). However, directly training deep neural models often suffer from overfitting …

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 …

Multi-task learning (MTL) has become increasingly popular in natural language processing
(NLP) because it improves the performance of related tasks by exploiting their …

Abstract Recently, reinforcement learning (RL)-based methods have achieved remarkable
progress in both effectiveness and interpretability for complex question answering over …

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 …

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

Question Answering (QA) is a longstanding challenge in natural language processing.
Existing QA works mostly focus on specific question types, knowledge domains, or …

The rapid development of conversational assistants accelerates the study on conversational
question answering (QA). However, the existing conversational QA systems usually answer …

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 …