29 citations · 130 across the 17 of their papers we have counts for
11 papers · 1 filter
Dual-Alignment Pre-training for Cross-lingual Sentence Embedding
Ziheng Li, Shaohan Huang, Zihan Zhang +7
Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. However, our…
Pre-training Transformers for Knowledge Graph Completion
Sanxing Chen, Hao Cheng, Xiaodong Liu +3
Learning transferable representation of knowledge graphs (KGs) is challenging due to the heterogeneous, multi-relational nature of graph structures. Inspired by Transformer-based p…
Efficient Long Sequence Modeling via State Space Augmented Transformer
Simiao Zuo, Xiaodong Liu, Jian Jiao +4
Transformer models have achieved superior performance in various natural language processing tasks. However, the quadratic computational cost of the attention mechanism limits its…
PromptBERT: Improving BERT Sentence Embeddings with Prompts
Ting Jiang, Jian Jiao, Shaohan Huang +7
We propose PromptBERT, a novel contrastive learning method for learning better sentence representation. We firstly analyze the drawback of current sentence embedding from original…
Taming Sparsely Activated Transformer with Stochastic Experts
Simiao Zuo, Xiaodong Liu, Jian Jiao +5
Sparsely activated models (SAMs), such as Mixture-of-Experts (MoE), can easily scale to have outrageously large amounts of parameters without significant increase in computational…
KFCNet: Knowledge Filtering and Contrastive Learning Network for Generative Commonsense Reasoning
Haonan Li, Yeyun Gong, Jian Jiao +3
Pre-trained language models have led to substantial gains over a broad range of natural language processing (NLP) tasks, but have been shown to have limitations for natural languag…
Mask Attention Networks: Rethinking and Strengthen Transformer
Zhihao Fan, Yeyun Gong, Dayiheng Liu +6
Transformer is an attention-based neural network, which consists of two sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN). Existing research explores t…
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining
Weizhen Qi, Yeyun Gong, Jian Jiao +9
In this paper, we propose BANG, a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation. AR and NAR generation can be uniforml…
An Enhanced Knowledge Injection Model for Commonsense Generation
Zhihao Fan, Yeyun Gong, Zhongyu Wei +6
Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts. Digging the relationship of concepts from scratch is non-triv…
GLGE: A New General Language Generation Evaluation Benchmark
Dayiheng Liu, Yu Yan, Yeyun Gong +15
Multi-task benchmarks such as GLUE and SuperGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP). These benchmarks mostly focus…
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
Sanxing Chen, Xiaodong Liu, Jianfeng Gao +3
This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical T…