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...
share
research
∙
11/15/2022
Empowering Language Models with Knowledge Graph Reasoning for Question Answering
Answering open-domain questions requires world knowledge about in-contex...
research
∙
10/23/2022
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach
A common thread of retrieval-augmented methods in the existing literatur...
research
∙
10/17/2022
Prompting GPT-3 To Be Reliable
Large language models (LLMs) show impressive abilities via few-shot prom...
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), ...
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...
research
∙
01/13/2022
CLIP-Event: Connecting Text and Images with Event Structures
Vision-language (V+L) pretraining models have achieved great success in ...
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...
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 ...
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...
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 ...
research
∙
10/16/2021
Leveraging Knowledge in Multilingual Commonsense Reasoning
Commonsense reasoning (CSR) requires the model to be equipped with gener...
research
∙
10/13/2021
Dict-BERT: Enhancing Language Model Pre-training with Dictionary
Pre-trained language models (PLMs) aim to learn universal language repre...
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...
research
∙
04/23/2021
Playing Lottery Tickets with Vision and Language
Large-scale transformer-based pre-training has recently revolutionized v...
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...
research
∙
03/30/2021
The Elastic Lottery Ticket Hypothesis
Lottery Ticket Hypothesis raises keen attention to identifying sparse tr...
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...
research
∙
12/31/2020
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets
Deep, heavily overparameterized language models such as BERT, XLNet and ...
research
∙
10/12/2020
Counterfactual Variable Control for Robust and Interpretable Question Answering
Deep neural network based question answering (QA) models are neither rob...
research
∙
10/07/2020
Cross-Thought for Sentence Encoder Pre-training
In this paper, we propose Cross-Thought, a novel approach to pre-trainin...
research
∙
10/06/2020
Multi-Fact Correction in Abstractive Text Summarization
Pre-trained neural abstractive summarization systems have dominated extr...
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 ...
research
∙
09/29/2020
Contrastive Distillation on Intermediate Representations for Language Model Compression
Existing language model compression methods mostly use a simple L2 loss ...
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...
research
∙
09/10/2020
Accelerating Real-Time Question Answering via Question Generation
Existing approaches to real-time question answering (RTQA) rely on learn...
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 ...
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...
research
∙
11/09/2019
Hierarchical Graph Network for Multi-hop Question Answering
In this paper, we present Hierarchical Graph Network (HGN) for multi-hop...
research
∙
10/28/2019
What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?
Multiple-Choice Reading Comprehension (MCRC) requires the model to read ...
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...
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...
research
∙
04/03/2019
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning
Commonsense reasoning is fundamental to natural language understanding. ...
research
∙
06/11/2018
A Co-Matching Model for Multi-choice Reading Comprehension
Multi-choice reading comprehension is a challenging task, which involves...
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...
research
∙
08/31/2017
R^3: Reinforced Reader-Ranker for Open-Domain Question Answering
In recent years researchers have achieved considerable success applying ...
research
∙
11/06/2016
A Compare-Aggregate Model for Matching Text Sequences
Many NLP tasks including machine comprehension, answer selection and tex...
research
∙
08/29/2016
Machine Comprehension Using Match-LSTM and Answer Pointer
Machine comprehension of text is an important problem in natural languag...
research
∙
12/30/2015