Yuejian Fang


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A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
Kunbo Ding | Weijie Liu | Yuejian Fang | Weiquan Mao | Zhe Zhao | Tao Zhu | Haoyan Liu | Rong Tian | Yiren Chen
Proceedings of the 29th International Conference on Computational Linguistics

Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages. To disengage from these dependencies, researchers have explored training multilingual models on English-only resources and transferring them to low-resource languages. However, its effect is limited by the gap between embedding clusters of different languages. To address this issue, we propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embeddings without semantic loss, thereby improving cross-lingual transferability. Experimental results on mBERT and XLM-R demonstrate that our method significantly outperforms previous works on the zero-shot cross-lingual text classification task and can obtain a better multilingual alignment.

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Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
Kunbo Ding | Weijie Liu | Yuejian Fang | Zhe Zhao | Qi Ju | Xuefeng Yang | Rong Tian | Zhu Tao | Haoyan Liu | Han Guo | Xingyu Bai | Weiquan Mao | Yudong Li | Weigang Guo | Taiqiang Wu | Ningyuan Sun
Findings of the Association for Computational Linguistics: NAACL 2022

Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks. However, the student model needs to be large in this operation. Otherwise, its performance will drop sharply, thus making it impractical to be deployed to memory-limited devices. To address this issue, we delve into cross-lingual knowledge distillation and propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model. In our framework, contrastive learning, bottleneck, and parameter recurrent strategies are delicately combined to prevent performance from being compromised during the compression process. The experimental results demonstrate that our method can compress the size of XLM-R and MiniLM by more than 50%, while the performance is only reduced by about 1%.

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Interactive Latent Knowledge Selection for E-Commerce Product Copywriting Generation
Zeming Wang | Yanyan Zou | Yuejian Fang | Hongshen Chen | Mian Ma | Zhuoye Ding | Bo Long
Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

As the multi-modal e-commerce is thriving, high-quality advertising product copywriting has gain more attentions, which plays a crucial role in the e-commerce recommender, advertising and even search platforms.The advertising product copywriting is able to enhance the user experience by highlighting the product’s characteristics with textual descriptions and thus to improve the likelihood of user click and purchase. Automatically generating product copywriting has attracted noticeable interests from both academic and industrial communities, where existing solutions merely make use of a product’s title and attribute information to generate its corresponding description.However, in addition to the product title and attributes, we observe that there are various auxiliary descriptions created by the shoppers or marketers in the e-commerce platforms (namely human knowledge), which contains valuable information for product copywriting generation, yet always accompanying lots of noises.In this work, we propose a novel solution to automatically generating product copywriting that involves all the title, attributes and denoised auxiliary knowledge.To be specific, we design an end-to-end generation framework equipped with two variational autoencoders that works interactively to select informative human knowledge and generate diverse copywriting.