Search Results for author:
Found 9 papers, 7 papers with code
FastSeq: Make Sequence Generation Faster
Transformer-based models have made tremendous impacts in natural language generation.
EL-Attention: Memory Efficient Lossless Attention for Generation
Transformer model with multi-head attention requires caching intermediate results for efficient inference in generation tasks.
ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation
ProphetNet is a pre-training based natural language generation method which shows powerful performance on English text summarization and question generation tasks.
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
In this paper, we propose BANG, a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation.
GLGE: A New General Language Generation Evaluation Benchmark
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP).
ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training
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.
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
In this paper, we propose a novel data augmentation method, referred to as Controllable Rewriting based Question Data Augmentation (CRQDA), for machine reading comprehension (MRC), question generation, and question-answering natural language inference tasks.
RikiNet: Reading Wikipedia Pages for Natural Question Answering
The representations are then fed into the predictor to obtain the span of the short answer, the paragraph of the long answer, and the answer type in a cascaded manner.
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
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 #6 on
Question Generation
on SQuAD1.1
(using extra training data)
