b'Zhilin Yang'

research

∙ 01/18/2022

ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-Shot Generalization

We propose a multitask pretraining approach ZeroPrompt for zero-shot gen...

Hanwei Xu, et al. ∙

research

∙ 11/07/2021

NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework

Pretrained language models have become the standard approach for many NL...

Xingcheng Yao, et al. ∙

research

∙ 10/14/2021

P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Prompt tuning, which only tunes continuous prompts with a frozen languag...

Xiao Liu, et al. ∙

research

∙ 09/27/2021

FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding

The few-shot natural language understanding (NLU) task has attracted muc...

Yanan Zheng, et al. ∙

research

∙ 08/13/2021

FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning

Most previous methods for text data augmentation are limited to simple t...

Jing Zhou, et al. ∙

research

∙ 06/01/2021

Distribution Matching for Rationalization

The task of rationalization aims to extract pieces of input text as rati...

Yongfeng Huang, et al. ∙

research

∙ 03/24/2021

FastMoE: A Fast Mixture-of-Expert Training System

Mixture-of-Expert (MoE) presents a strong potential in enlarging the siz...

Jiaao He, et al. ∙

research

∙ 03/19/2021

Controllable Generation from Pre-trained Language Models via Inverse Prompting

Large-scale pre-trained language models have demonstrated strong capabil...

Xu Zou, et al. ∙

research

∙ 03/18/2021

GPT Understands, Too

While GPTs with traditional fine-tuning fail to achieve strong results o...

Xiao Liu, et al. ∙

research

∙ 03/18/2021

All NLP Tasks Are Generation Tasks: A General Pretraining Framework

There have been various types of pretraining architectures including aut...

Zhengxiao Du, et al. ∙

research

∙ 06/19/2019

XLNet: Generalized Autoregressive Pretraining for Language Understanding

With the capability of modeling bidirectional contexts, denoising autoen...

Zhilin Yang, et al. ∙

research

∙ 01/09/2019

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Transformer networks have a potential of learning longer-term dependency...

Zihang Dai, et al. ∙

research

∙ 09/25/2018

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Existing question answering (QA) datasets fail to train QA systems to pe...

Zhilin Yang, et al. ∙

research

∙ 08/29/2018

Neural Cross-Lingual Named Entity Recognition with Minimal Resources

For languages with no annotated resources, unsupervised transfer of natu...

Jiateng Xie, et al. ∙

research

∙ 06/14/2018

GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations

Modern deep transfer learning approaches have mainly focused on learning...

Zhilin Yang, et al. ∙

research

∙ 04/16/2018

Neural Models for Reasoning over Multiple Mentions using Coreference

Many problems in NLP require aggregating information from multiple menti...

Bhuwan Dhingra, et al. ∙

research

∙ 11/21/2017

Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent

Contrary to most natural language processing research, which makes use o...

Zhilin Yang, et al. ∙

research

∙ 11/10/2017

Breaking the Softmax Bottleneck: A High-Rank RNN Language Model

We formulate language modeling as a matrix factorization problem, and sh...

Zhilin Yang, et al. ∙

research

∙ 05/27/2017

Good Semi-supervised Learning that Requires a Bad GAN

Semi-supervised learning methods based on generative adversarial network...

Zihang Dai, et al. ∙

research

∙ 03/18/2017

Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks

Recent papers have shown that neural networks obtain state-of-the-art pe...

Zhilin Yang, et al. ∙

research

∙ 03/07/2017

Linguistic Knowledge as Memory for Recurrent Neural Networks

Training recurrent neural networks to model long term dependencies is di...

Bhuwan Dhingra, et al. ∙

research

∙ 02/27/2017

Differentiable Learning of Logical Rules for Knowledge Base Reasoning

We study the problem of learning probabilistic first-order logical rules...

Fan Yang, et al. ∙

research

∙ 02/23/2017

A Probabilistic Framework for Location Inference from Social Media

We study the extent to which we can infer users' geographical locations ...

Yujie Qian, et al. ∙

research

∙ 02/07/2017

Semi-Supervised QA with Generative Domain-Adaptive Nets

We study the problem of semi-supervised question answering----utilizing ...

Zhilin Yang, et al. ∙

research

∙ 11/06/2016

Words or Characters? Fine-grained Gating for Reading Comprehension

Previous work combines word-level and character-level representations us...

Zhilin Yang, et al. ∙

research

∙ 06/05/2016

Gated-Attention Readers for Text Comprehension

In this paper we study the problem of answering cloze-style questions ov...

Bhuwan Dhingra, et al. ∙

research

∙ 05/25/2016

Review Networks for Caption Generation

We propose a novel extension of the encoder-decoder framework, called a ...

Zhilin Yang, et al. ∙

research

∙ 03/20/2016

Multi-Task Cross-Lingual Sequence Tagging from Scratch

We present a deep hierarchical recurrent neural network for sequence tag...

Zhilin Yang, et al. ∙

research

∙ 08/04/2015

Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs

We study the extent to which online social networks can be connected to ...

Zhilin Yang, et al. ∙

Zhilin Yang

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