b'Shuohang Wang'

research

∙ 12/19/2022

APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning

Logical reasoning of text is an important ability that requires understa...

0 Soumya Sanyal, et al. ∙

research

∙ 11/15/2022

Empowering Language Models with Knowledge Graph Reasoning for Question Answering

Answering open-domain questions requires world knowledge about in-contex...

0 Ziniu Hu, et al. ∙

research

∙ 10/23/2022

Retrieval Augmentation for Commonsense Reasoning: A Unified Approach

A common thread of retrieval-augmented methods in the existing literatur...

0 Wenhao Yu, et al. ∙

research

∙ 10/17/2022

Prompting GPT-3 To Be Reliable

Large language models (LLMs) show impressive abilities via few-shot prom...

0 Chenglei Si, et al. ∙

research

∙ 09/21/2022

Generate rather than Retrieve: Large Language Models are Strong Context Generators

Knowledge-intensive tasks, such as open-domain question answering (QA), ...

0 Wenhao Yu, et al. ∙

research

∙ 05/22/2022

Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners

The goal of this work is to build flexible video-language models that ca...

11 Zhenhailong Wang, et al. ∙

research

∙ 01/13/2022

CLIP-Event: Connecting Text and Images with Event Structures

Vision-language (V+L) pretraining models have achieved great success in ...

6 Manling Li, et al. ∙

research

∙ 12/08/2021

MLP Architectures for Vision-and-Language Modeling: An Empirical Study

We initiate the first empirical study on the use of MLP architectures fo...

2 Yixin Nie, et al. ∙

research

∙ 12/06/2021

Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention

Most of today's AI systems focus on using self-attention mechanisms and ...

3 Yichong Xu, et al. ∙

research

∙ 11/04/2021

Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models

Large-scale pre-trained language models have achieved tremendous success...

0 Boxin Wang, et al. ∙

research

∙ 11/03/2021

An Empirical Study of Training End-to-End Vision-and-Language Transformers

Vision-and-language (VL) pre-training has proven to be highly effective ...

0 Zi-Yi Dou, et al. ∙

research

∙ 10/16/2021

Leveraging Knowledge in Multilingual Commonsense Reasoning

Commonsense reasoning (CSR) requires the model to be equipped with gener...

0 Yuwei Fang, et al. ∙

research

∙ 10/13/2021

Dict-BERT: Enhancing Language Model Pre-training with Dictionary

Pre-trained language models (PLMs) aim to learn universal language repre...

0 Wenhao Yu, et al. ∙

research

∙ 10/08/2021

KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering

Current Open-Domain Question Answering (ODQA) model paradigm often conta...

0 Donghan Yu, et al. ∙

research

∙ 04/23/2021

Playing Lottery Tickets with Vision and Language

Large-scale transformer-based pre-training has recently revolutionized v...

11 Zhe Gan, et al. ∙

research

∙ 04/01/2021

UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training

Vision-and-language pre-training has achieved impressive success in lear...

2 Mingyang Zhou, et al. ∙

research

∙ 03/30/2021

The Elastic Lottery Ticket Hypothesis

Lottery Ticket Hypothesis raises keen attention to identifying sparse tr...

0 Xiaohan Chen, et al. ∙

research

∙ 03/16/2021

LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval

Multimodal pre-training has propelled great advancement in vision-and-la...

13 Siqi Sun, et al. ∙

research

∙ 12/31/2020

EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets

Deep, heavily overparameterized language models such as BERT, XLNet and ...

0 Xiaohan Chen, et al. ∙

research

∙ 10/12/2020

Counterfactual Variable Control for Robust and Interpretable Question Answering

Deep neural network based question answering (QA) models are neither rob...

0 Sicheng Yu, et al. ∙

research

∙ 10/07/2020

Cross-Thought for Sentence Encoder Pre-training

In this paper, we propose Cross-Thought, a novel approach to pre-trainin...

0 Shuohang Wang, et al. ∙

research

∙ 10/06/2020

Multi-Fact Correction in Abstractive Text Summarization

Pre-trained neural abstractive summarization systems have dominated extr...

0 Yue Dong, et al. ∙

research

∙ 10/05/2020

InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective

Large-scale language models such as BERT have achieved state-of-the-art ...

0 Boxin Wang, et al. ∙

research

∙ 09/29/2020

Contrastive Distillation on Intermediate Representations for Language Model Compression

Existing language model compression methods mostly use a simple L2 loss ...

0 Siqi Sun, et al. ∙

research

∙ 09/13/2020

Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding

Transformer has become ubiquitous in the deep learning field. One of the...

0 Shuohang Wang, et al. ∙

research

∙ 09/10/2020

Accelerating Real-Time Question Answering via Question Generation

Existing approaches to real-time question answering (RTQA) rely on learn...

0 Yuwei Fang, et al. ∙

research

∙ 09/10/2020

FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding

Large-scale cross-lingual language models (LM), such as mBERT, Unicoder ...

0 Yuwei Fang, et al. ∙

research

∙ 01/20/2020

Multi-level Head-wise Match and Aggregation in Transformer for Textual Sequence Matching

Transformer has been successfully applied to many natural language proce...

0 Shuohang Wang, et al. ∙

research

∙ 11/09/2019

Hierarchical Graph Network for Multi-hop Question Answering

In this paper, we present Hierarchical Graph Network (HGN) for multi-hop...

0 Yuwei Fang, et al. ∙

research

∙ 10/28/2019

What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?

Multiple-Choice Reading Comprehension (MCRC) requires the model to read ...

0 Chenglei Si, et al. ∙

research

∙ 06/11/2019

Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks

Many state-of-the-art neural models for NLP are heavily parameterized an...

0 Yi Tay, et al. ∙

research

∙ 05/26/2019

Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives

This paper tackles the problem of reading comprehension over long narrat...

8 Yi Tay, et al. ∙

research

∙ 04/03/2019

Unsupervised Deep Structured Semantic Models for Commonsense Reasoning

Commonsense reasoning is fundamental to natural language understanding. ...

0 Shuohang Wang, et al. ∙

research

∙ 06/11/2018

A Co-Matching Model for Multi-choice Reading Comprehension

Multi-choice reading comprehension is a challenging task, which involves...

0 Shuohang Wang, et al. ∙

research

∙ 11/14/2017

Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering

A popular recent approach to answering open-domain questions is to first...

0 Shuohang Wang, et al. ∙

research

∙ 08/31/2017

R^3: Reinforced Reader-Ranker for Open-Domain Question Answering

In recent years researchers have achieved considerable success applying ...

0 Shuohang Wang, et al. ∙

research

∙ 11/06/2016

A Compare-Aggregate Model for Matching Text Sequences

Many NLP tasks including machine comprehension, answer selection and tex...

0 Shuohang Wang, et al. ∙

research

∙ 08/29/2016

Machine Comprehension Using Match-LSTM and Answer Pointer

Machine comprehension of text is an important problem in natural languag...

0 Shuohang Wang, et al. ∙

research

∙ 12/30/2015

Learning Natural Language Inference with LSTM

Natural language inference (NLI) is a fundamentally important task in na...

0 Shuohang Wang, et al. ∙

Shuohang Wang

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