67 citations · 90 across the 4 of their papers we have counts for
8 papers
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
Yaobo Liang, Nan Duan, Yeyun Gong +21
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evalu…
Pre-training Text Representations as Meta Learning
Shangwen Lv, Yuechen Wang, Daya Guo +10
Pre-training text representations has recently been shown to significantly improve the state-of-the-art in many natural language processing tasks. The central goal of pre-training…
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
Xiaodong Liu, Yu Wang, Jianshu Ji +8
We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upo…
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Ruize Wang, Duyu Tang, Nan Duan +6
We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models whe…
TableQnA: Answering List Intent Queries With Web Tables
Kaushik Chakrabarti, Zhimin Chen, Siamak Shakeri +2
The web contains a vast corpus of HTML tables. They can be used to provide direct answers to many web queries. We focus on answering two classes of queries with those tables: those…
Open Domain Question Answering Using Web Tables
Kaushik Chakrabarti, Zhimin Chen, Siamak Shakeri +1
Tables extracted from web documents can be used to directly answer many web search queries. Previous works on question answering (QA) using web tables have focused on factoid queri…