30 citations · 165 across the 20 of their papers we have counts for
19 papers · 1 filter
i-Code Studio: A Configurable and Composable Framework for Integrative AI
Yuwei Fang, Mahmoud Khademi, Chenguang Zhu +8
Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities. Int…
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
Ziyi Yang, Mahmoud Khademi, Yichong Xu +16
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encod…
MACSum: Controllable Summarization with Mixed Attributes
Yusen Zhang, Yang Liu, Ziyi Yang +6
Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, exis…
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach
Wenhao Yu, Chenguang Zhu, Zhihan Zhang +4
A common thread of retrieval-augmented methods in the existing literature focuses on retrieving encyclopedic knowledge, such as Wikipedia, which facilitates well-defined entity and…
Task Compass: Scaling Multi-task Pre-training with Task Prefix
Zhuosheng Zhang, Shuohang Wang, Yichong Xu +6
Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language mod…
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data
Shuohang Wang, Yichong Xu, Yuwei Fang +5
Retrieval-based methods have been shown to be effective in NLP tasks via introducing external knowledge. However, the indexing and retrieving of large-scale corpora bring considera…
Leveraging Knowledge in Multilingual Commonsense Reasoning
Yuwei Fang, Shuohang Wang, Yichong Xu +4
Commonsense reasoning (CSR) requires the model to be equipped with general world knowledge. While CSR is a language-agnostic process, most comprehensive knowledge sources are in fe…
Dict-BERT: Enhancing Language Model Pre-training with Dictionary
Wenhao Yu, Chenguang Zhu, Yuwei Fang +5
Pre-trained language models (PLMs) aim to learn universal language representations by conducting self-supervised training tasks on large-scale corpora. Since PLMs capture word sema…
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
Donghan Yu, Chenguang Zhu, Yuwei Fang +6
Current Open-Domain Question Answering (ODQA) model paradigm often contains a retrieving module and a reading module. Given an input question, the reading module predicts the answe…
Does Knowledge Help General NLU? An Empirical Study
Ruochen Xu, Yuwei Fang, Chenguang Zhu +1
It is often observed in knowledge-centric tasks (e.g., common sense question and answering, relation classification) that the integration of external knowledge such as entity repre…
RetGen: A Joint framework for Retrieval and Grounded Text Generation Modeling
Yizhe Zhang, Siqi Sun, Xiang Gao +5
Recent advances in large-scale pre-training such as GPT-3 allow seemingly high quality text to be generated from a given prompt. However, such generation systems often suffer from…
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
Siqi Sun, Yen-Chun Chen, Linjie Li +3
Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow infer…
Cross-Thought for Sentence Encoder Pre-training
Shuohang Wang, Yuwei Fang, Siqi Sun +4
In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks…
Contrastive Distillation on Intermediate Representations for Language Model Compression
Siqi Sun, Zhe Gan, Yu Cheng +3
Existing language model compression methods mostly use a simple L2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one. Although wi…
Cluster-Former: Clustering-based Sparse Transformer for Long-Range Dependency Encoding
Shuohang Wang, Luowei Zhou, Zhe Gan +5
Transformer has become ubiquitous in the deep learning field. One of the key ingredients that destined its success is the self-attention mechanism, which allows fully-connected con…
Accelerating Real-Time Question Answering via Question Generation
Yuwei Fang, Shuohang Wang, Zhe Gan +3
Although deep neural networks have achieved tremendous success for question answering (QA), they are still suffering from heavy computational and energy cost for real product deplo…
FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding
Yuwei Fang, Shuohang Wang, Zhe Gan +2
Large-scale cross-lingual language models (LM), such as mBERT, Unicoder and XLM, have achieved great success in cross-lingual representation learning. However, when applied to zero…
Hierarchical Graph Network for Multi-hop Question Answering
Yuwei Fang, Siqi Sun, Zhe Gan +3
In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a hierarchical grap…
Stochastic Answer Networks for SQuAD 2.0
Xiaodong Liu, Wei Li, Yuwei Fang +3
This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is…