ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training

Abstract

This paper presents a new sequence-tosequence pre-training model called Prophet-Net, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.Instead of optimizing one-stepahead prediction in the traditional sequenceto-sequence model, the ProphetNet is optimized by n-step ahead prediction that predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large-scale dataset (160GB), respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that Prophet-Net achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.

Automatic question-answer pairs generation and question similarity mechanism in question answering system

“…To address the limitations of the Question Answering System, we propose a Question Similarity mechanism. The possible generated questions are from the state-of-the-art question generation system called ProphetNet [29] and the Question posed is from the SQuAD 2.0 dataset. The Question Similarity mechanism calculates the cosine similarity between the possible generated questions from the given paragraph and the question posed.…”

“…In recent years, several works are proposed to tackle world knowledge by combining search factors based on bigram hashing, TF-IDF matching [7] and machine reading comprehension [22,29]. It brought the Question Answering System a good beginning.…”

“…Even though these papers provide a good result, these works lack capturing long-term dependencies [19,22]. The work proposed by Qi et al [29] has a future n-gram as a training objective, thus providing excellent results in question generation tasks.…”

“…The automatic question-answer pairs generation system uses pre-trained weights of a state-of-the-art question generation system called ProphetNet [29] to generate the questions, and BERT [11] model to generate the answers for the generated questions.…”

“…The question generation system, ProphetNet [1] is used to generate all possible questions G 1 to G 28 from the given passage 2. The pre-trained weights of state-of-the-art Question Generation System called ProphetNet [29] to generate the questions for a given paragraph. 3.…”