Conversational Question Answering
62 papers with code • 1 benchmarks • 9 datasets
CoQA
QuAC
CANARD
ConvFinQA
Most implemented papers
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
Microsoft/SDNet
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Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context.
CoQA: A Conversational Question Answering Challenge
stanfordnlp/coqa-baselines
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TACL 2019
Humans gather information by engaging in conversations involving a series of interconnected questions and answers.
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
PaddlePaddle/Research
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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
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First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.
Open-Domain Question Answering Goes Conversational via Question Rewriting
We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs.
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
alibaba/EasyNLP
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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
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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.
APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning
For the retriever, we adopt a number-aware negative sampling strategy to enable the retriever to be more discriminative on key numerical facts.
KL-Divergence Guided Temperature Sampling
google-research/google-research
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One common approach to mitigate hallucinations is to provide source/grounding documents and the model is trained to produce predictions that bind to and are attributable to the provided source.
PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
This paper introduces PIXIU, a comprehensive framework including the first financial LLM based on fine-tuning LLaMA with instruction data, the first instruction data with 136K data samples to support the fine-tuning, and an evaluation benchmark with 5 tasks and 9 datasets.
