Ling Xiao Wang

Also published as: Lingxiao Wang


Variance-reduced First-order Meta-learning for Natural Language Processing Tasks
Lingxiao Wang | Kevin Huang | Tengyu Ma | Quanquan Gu | Jing Huang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

First-order meta-learning algorithms have been widely used in practice to learn initial model parameters that can be quickly adapted to new tasks due to their efficiency and effectiveness. However, existing studies find that meta-learner can overfit to some specific adaptation when we have heterogeneous tasks, leading to significantly degraded performance. In Natural Language Processing (NLP) applications, datasets are often diverse and each task has its unique characteristics. Therefore, to address the overfitting issue when applying first-order meta-learning to NLP applications, we propose to reduce the variance of the gradient estimator used in task adaptation. To this end, we develop a variance-reduced first-order meta-learning algorithm. The core of our algorithm is to introduce a novel variance reduction term to the gradient estimation when performing the task adaptation. Experiments on two NLP applications: few-shot text classification and multi-domain dialog state tracking demonstrate the superior performance of our proposed method.


An Aligned French-Chinese corpus of 10K segments from university educational material
Ruslan Kalitvianski | Lingxiao Wang | Valérie Bellynck | Christian Boitet
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)

This paper describes a corpus of nearly 10K French-Chinese aligned segments, produced by post-editing machine translated computer science courseware. This corpus was built from 2013 to 2016 within the PROJECT_NAME project, by native Chinese students. The quality, as judged by native speakers, is ad-equate for understanding (far better than by reading only the original French) and for getting better marks. This corpus is annotated at segment-level by a self-assessed quality score. It has been directly used as supplemental training data to build a statistical machine translation system dedicated to that sublanguage, and can be used to extract the specific bilingual terminology. To our knowledge, it is the first corpus of this kind to be released.


On-going Cooperative Research towards Developing Economy-Oriented Chinese-French SMT Systems with a New SMT Framework
Yidong Chen | Lingxiao Wang | Christian Boitet | Xiaodong Shi
Proceedings of TALN 2014 (Volume 2: Short Papers)


Online production of HQ parallel corpora and permanent task-based evaluation of multiple MT systems: both can be obtained through iMAGs with no added cost
Lingxiao Wang | Christian Boitet
Proceedings of the 2nd Workshop on Post-editing Technology and Practice

iMAG : MT-postediting, translation quality evaluation and parallel corpus production (iMAG : post-édition, évaluation de qualité de TA et production d’un corpus parallèle) [in French]
Lingxiao Wang | Ying Zhang
Proceedings of TALN 2013 (Volume 3: System Demonstrations)


Demo of iMAG Possibilities: MT-postediting, Translation Quality Evaluation, Parallel Corpus Production
Ling Xiao Wang | Ying Zhang | Christian Boitet | Valerie Bellynck
Proceedings of COLING 2012: Demonstration Papers