Conversational Question Answering
35 papers with code • 0 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Conversational Question Answering
Most implemented papers
CoQA: A Conversational Question Answering Challenge
Humans gather information by engaging in conversations involving a series of interconnected questions and answers.
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
Pre-training models have been proved effective for a wide range of natural language processing tasks.
Attentive History Selection for Conversational Question Answering
First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose.
Ditch the Gold Standard: Re-evaluating Conversational Question Answering
In this work, we conduct the first large-scale human evaluation of state-of-the-art conversational QA systems, where human evaluators converse with models and judge the correctness of their answers.
Evaluating Natural Language Understanding Services for Conversational Question Answering Systems
Conversational interfaces recently gained a lot of attention.
Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base
We present an approach to map utterances in conversation to logical forms, which will be executed on a large-scale knowledge base.
BERT with History Answer Embedding for Conversational Question Answering
One of the major challenges to multi-turn conversational search is to model the conversation history to answer the current question.
An Empirical Study of Content Understanding in Conversational Question Answering
However, to best of our knowledge, two important questions for conversational comprehension research have not been well studied: 1) How well can the benchmark dataset reflect models' content understanding?