The goal of Question Generation is to generate a valid and fluent question according to a given passage and the target answer. Question Generation can be used in many scenarios, such as automatic tutoring systems, improving the performance of Question Answering models and enabling chatbots to lead a conversation.
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This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
Ranked #3 on Abstractive Text Summarization on CNN / Daily Mail (using extra training data)
We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.
Ranked #1 on Text Summarization on GigaWord-10k (using extra training data)
Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task.
We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM).
Ranked #3 on Question Generation on SQuAD1.1 (using extra training data)
This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.
Ranked #2 on Generative Question Answering on CoQA (using extra training data)
We introduce a novel method of generating synthetic question answering corpora by combining models of question generation and answer extraction, and by filtering the results to ensure roundtrip consistency.
We study automatic question generation for sentences from text passages in reading comprehension.
ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks.