no code implementations • EMNLP 2020 • Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou
In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks.
no code implementations • 18 Apr 2024 • Spencer Carmichael, Rahul Agrawal, Ram Vasudevan, Katherine A. Skinner
Recognizing places from an opposing viewpoint during a return trip is a common experience for human drivers.
no code implementations • 30 Nov 2020 • Shreya Malani, Dinesh Gaurav, Anoop Vallabhajosyula, Rahul Agrawal
In the second approach, we show that the optimization problem reduces to minimizing a quadratic form over an l1-ball.
2 code implementations • 3 Apr 2020 • Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou
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 evaluate their performance across a diverse set of cross-lingual tasks.
no code implementations • 30 Nov 2018 • Nikit Begwani, Shrutendra Harsola, Rahul Agrawal
In this paper, we discuss the impact of incorporating or disregarding the long tail pairs in the training set.