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Found 7 papers, 0 papers with code
Distractor Generation for Multiple-Choice Questions: A Survey of Methods, Datasets, and Evaluation
Distractors are important in learning evaluation.
Learning to Select the Relevant History Turns in Conversational Question Answering
In this paper, we propose a framework, DHS-ConvQA (Dynamic History Selection in Conversational Question Answering), that first generates the context and question entities for all the history turns, which are then pruned on the basis of similarity they share in common with the question at hand.
Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering
However, these sequential questions are sometimes left implicit and thus require the resolution of some natural language phenomena such as anaphora and ellipsis.
Conversational Question Answering: A Survey
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 languages.
BERT-CoQAC: BERT-based Conversational Question Answering in Context
On one hand, we introduce a framework based on a publically available pre-trained language model called BERT for incorporating history turns into the system.
A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLP
Building a dialogue system that can communicate naturally with humans is a challenging yet interesting problem of agent-based computing.
Deep Conversational Recommender Systems: A New Frontier for Goal-Oriented Dialogue Systems
In recent years, the emerging topics of recommender systems that take advantage of natural language processing techniques have attracted much attention, and one of their applications is the Conversational Recommender System (CRS).
