Generative Question Answering

7 papers with code • 2 benchmarks • 2 datasets

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Most implemented papers

Unified Language Model Pre-training for Natural Language Understanding and Generation

microsoft/unilm NeurIPS 2019

This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.

ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation

PaddlePaddle/ERNIE 26 Jan 2020

Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.

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.

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation

alibaba/AliceMind 14 Apr 2020

An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

Neural Generative Question Answering

jxfeb/Generative_QA WS 2016

Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base.

KPQA: A Metric for Generative Question Answering Using Keyphrase Weights

hwanheelee1993/KPQA NAACL 2021

To evaluate our metric, we create high-quality human judgments of correctness on two GenQA datasets.

General-Purpose Question-Answering with Macaw

allenai/macaw 6 Sep 2021

Despite the successes of pretrained language models, there are still few high-quality, general-purpose QA systems that are freely available.