no code implementations • EMNLP 2021 • Zhuoyi Wang, Saurabh Gupta, Jie Hao, Xing Fan, Dingcheng Li, Alexander Hanbo Li, Chenlei Guo
Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e. g. users’ implicit feedback).
no code implementations • EMNLP 2021 • Kaize Ding, Dingcheng Li, Alexander Hanbo Li, Xing Fan, Chenlei Guo, Yang Liu, Huan Liu
In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data.
1 code implementation • ACL 2021 • Alexander Hanbo Li, Patrick Ng, Peng Xu, Henghui Zhu, Zhiguo Wang, Bing Xiang
However, a large amount of world's knowledge is stored in structured databases, and need to be accessed using query languages such as SQL.
3 code implementations • 18 Dec 2020 • Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira dos santos, Bing Xiang
Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).
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no code implementations • 21 Apr 2020 • Alexander Hanbo Li, Yaqing Wang, Changyou Chen, Jing Gao
Effective inference for a generative adversarial model remains an important and challenging problem.
no code implementations • 8 Jan 2020 • Alexander Hanbo Li, Jelena Bradic
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases.
no code implementations • 26 Nov 2019 • Alexander Hanbo Li, Abhinav Sethy
In this way, $F$ serves as a feature extractor that maps the input to high-level representation and adds systematical noise using dropout.
no code implementations • 31 Aug 2019 • Alexander Hanbo Li, Abhinav Sethy
Neural network models have been very successful at achieving high accuracy on natural language inference (NLI) tasks.
1 code implementation • 8 Feb 2019 • Alexander Hanbo Li, Jelena Bradic
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases.
no code implementations • ICML 2017 • Alexander Hanbo Li, Andrew Martin
This paper introduces a new general framework for forest-type regression which allows the development of robust forest regressors by selecting from a large family of robust loss functions.
no code implementations • 5 Oct 2015 • Alexander Hanbo Li, Jelena Bradic
Along with the Arch Boosting framework, the non-convex losses lead to the new class of boosting algorithms, named adaptive, robust, boosting (ARB).