Conversational Question Answering
35 papers with code • 0 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Conversational Question Answering
CoQA
QuAC
CANARD
Most implemented papers
CoQA: A Conversational Question Answering Challenge
stanfordnlp/coqa-baselines
•
•
TACL 2019
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
Microsoft/SDNet
•
•
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
PaddlePaddle/Research
•
•
ACL 2020
Pre-training models have been proved effective for a wide range of natural language processing tasks.
Attentive History Selection for Conversational Question Answering
prdwb/attentive_history_selection
•
•
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
alibaba/EasyNLP
•
•
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
princeton-nlp/evalconvqa
•
•
ACL 2022
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
guoday/Dialog-to-Action
•
•
NeurIPS 2018
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
prdwb/bert_hae
•
•
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
MiuLab/CQA-Study
•
•
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?
