Zengxuan Wen
2022
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
Zhoujin Tian
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Chaozhuo Li
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Shuo Ren
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Zhiqiang Zuo
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Zengxuan Wen
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Xinyue Hu
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Xiao Han
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Haizhen Huang
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Denvy Deng
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Qi Zhang
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Xing Xie
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the distances between words in the aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates. In addition, the mapping function is globally shared by all words, whose performance might be hindered by the deviations in the distributions of different languages. In this work, we propose a novel ranking-oriented induction model RAPO to learn personalized mapping function for each word. RAPO is capable of enjoying the merits from the unique characteristics of a single word and the cross-language isomorphism simultaneously. Extensive experimental results on public datasets including both rich-resource and low-resource languages demonstrate the superiority of our proposal. Our code is publicly available in https://github.com/Jlfj345wf/RAPO.
2021
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search
Shuxian Bi
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Chaozhuo Li
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Xiao Han
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Zheng Liu
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Xing Xie
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Haizhen Huang
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Zengxuan Wen
Findings of the Association for Computational Linguistics: EMNLP 2021
Recently, sponsored search has become one of the most lucrative channels for marketing. As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value. Most existing methods solely rely on the query-keyword pairs. However, keywords are usually short texts with scarce semantic information, which may not precisely reflect the underlying advertising intents. In this paper, we investigate the novel problem of advertiser-aware relevance modeling, which leverages the advertisers’ information to bridge the gap between the search intents and advertising purposes. Our motivation lies in incorporating the unsupervised bidding behaviors as the complementary graphs to learn desirable advertiser representations. We further propose a Bidding-Graph augmented Triple-based Relevance model BGTR with three towers to deeply fuse the bidding graphs and semantic textual data. Empirically, we evaluate the BGTR model over a large industry dataset, and the experimental results consistently demonstrate its superiority.
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Co-authors
- Chaozhuo Li 2
- Xiao Han 2
- Xing Xie 2
- Haizhen Huang 2
- Shuxian Bi 1
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