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12/28/2022
Learning to Detect Noisy Labels Using Model-Based Features
Label noise is ubiquitous in various machine learning scenarios such as ...
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11/15/2022
A Universal Discriminator for Zero-Shot Generalization
Generative modeling has been the dominant approach for large-scale pretr...
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11/09/2022
Zero-Label Prompt Selection
Natural language prompts have been shown to facilitate cross-task genera...
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11/08/2022
Prompt-Based Metric Learning for Few-Shot NER
Few-shot named entity recognition (NER) targets generalizing to unseen l...
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10/31/2022
GPS: Genetic Prompt Search for Efficient Few-shot Learning
Prompt-based techniques have demostrated great potential for improving t...
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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...
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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...
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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...
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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...
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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...
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06/01/2021
Distribution Matching for Rationalization
The task of rationalization aims to extract pieces of input text as rati...
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03/24/2021
FastMoE: A Fast Mixture-of-Expert Training System
Mixture-of-Expert (MoE) presents a strong potential in enlarging the siz...
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03/19/2021
Controllable Generation from Pre-trained Language Models via Inverse Prompting
Large-scale pre-trained language models have demonstrated strong capabil...
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03/18/2021
GPT Understands, Too
While GPTs with traditional fine-tuning fail to achieve strong results o...
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03/18/2021
All NLP Tasks Are Generation Tasks: A General Pretraining Framework
There have been various types of pretraining architectures including aut...
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06/19/2019
XLNet: Generalized Autoregressive Pretraining for Language Understanding
With the capability of modeling bidirectional contexts, denoising autoen...
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01/09/2019
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Transformer networks have a potential of learning longer-term dependency...
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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...
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08/29/2018
Neural Cross-Lingual Named Entity Recognition with Minimal Resources
For languages with no annotated resources, unsupervised transfer of natu...
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06/14/2018
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
Modern deep transfer learning approaches have mainly focused on learning...
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04/16/2018
Neural Models for Reasoning over Multiple Mentions using Coreference
Many problems in NLP require aggregating information from multiple menti...
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11/21/2017
Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Contrary to most natural language processing research, which makes use o...
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11/10/2017
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model
We formulate language modeling as a matrix factorization problem, and sh...
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05/27/2017
Good Semi-supervised Learning that Requires a Bad GAN
Semi-supervised learning methods based on generative adversarial network...
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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...
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03/07/2017
Linguistic Knowledge as Memory for Recurrent Neural Networks
Training recurrent neural networks to model long term dependencies is di...
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02/27/2017
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
We study the problem of learning probabilistic first-order logical rules...
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02/23/2017
A Probabilistic Framework for Location Inference from Social Media
We study the extent to which we can infer users' geographical locations ...
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02/07/2017
Semi-Supervised QA with Generative Domain-Adaptive Nets
We study the problem of semi-supervised question answering----utilizing ...
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11/06/2016
Words or Characters? Fine-grained Gating for Reading Comprehension
Previous work combines word-level and character-level representations us...
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06/05/2016
Gated-Attention Readers for Text Comprehension
In this paper we study the problem of answering cloze-style questions ov...
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05/25/2016
Review Networks for Caption Generation
We propose a novel extension of the encoder-decoder framework, called a ...
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03/20/2016
Multi-Task Cross-Lingual Sequence Tagging from Scratch
We present a deep hierarchical recurrent neural network for sequence tag...
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08/04/2015