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Works85 from public data
- ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training512
A new sequence-to-sequence pre-training model called ProphetNet is presented, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism that predicts the next n tokens simultaneously based on previous context tokens at each time step.
- XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation404
A recent cross-lingual pre-trained model Unicoder is extended to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline and the base versions of Multilingual BERT, XLM and XLM-R are evaluated for comparison.
- Learning to Rank by Optimizing NDCG Measure229
This work proposes a probabilistic framework that addresses the challenge of evaluating ranking algorithms using the expectation of NDCG over all the possible permutations of documents, and proposes an algorithm that outperforms state-of-the-art ranking algorithms on several benchmark data sets.
- HittER: Hierarchical Transformers for Knowledge Graph Embeddings155
This paper proposes HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity’s neighborhood and designs a masked entity prediction task to balance information from the relational context and the source entity itself.
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- DeepIntent121
This paper proposes a novel attention network, which learns to assign attention scores to different word locations according to their intent importance, and shows that using the learned attention scores, one is able to produce sub-queries that are of better qualities than those of the state-of-the-art methods.
- TwinBERT114
TwinBERT model, a compressed multi-layer transformer encoder is proposed with special training strategies as a substitution of the original complex BERT encoder, which has met the real-time latency requirement and achieve close or on-par performance to BERT-Base model.
- Bid landscape forecasting in online ad exchange marketplace112
A divide-and-conquer approach that breaks down the campaign-level forecasting problem and can accurately forecast the bid distributions for various campaigns running on the world's largest NGD advertising exchange system, outperforming two baseline methods in term of forecasting errors.
- Exploitation and exploration in a performance based contextual advertising system112
This paper develops two novel EE strategies for online advertising that can adaptively balance the two aspects of EE by automatically learning the optimal tradeoff and incorporating confidence metrics of historical performance.
- Mask Attention Networks: Rethinking and Strengthen Transformer83
A novel understanding of SAN and FFN are presented as Mask Attention Networks (MANs) and it is shown that they are two special cases of MANs with static mask matrices, which limit the capability for localness modeling in text representation learning.
- GLGE: A New General Language Generation Evaluation Benchmark75
The General Language Generation Evaluation (GLGE), a new multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks, is presented and a leaderboard with strong baselines including MASS, BART, and ProphetNet is built.
- Multimedia features for click prediction of new ads in display advertising73
It is demonstrated that adding multimedia features can significantly improve the accuracy of click prediction for new ads, compared to a state-of-the-art baseline model.
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- ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation56
The ProphetNet family pre-training models, named ProphetNet-X, where X can be English, Chinese, Multi-lingual, and so on, are presented, and achieve new state-of-the-art performance on 10 benchmarks.
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- Generating Better Search Engine Text Advertisements with Deep Reinforcement Learning51
This work jointly train a model to minimize cross-entropy on an existing corpus of Landing Page/Text Ad pairs using typical sequence to sequence training techniques while also optimizing the expected click-through rate as predicted by an existing oracle model using SCST.
- GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification50
The GalaXC algorithm for Extreme Classification is developed, where the task is to annotate a document with the most relevant subset of labels from an extremely large label set, and was found to be up to 25% more accurate than extreme classification algorithms specifically designed for warm start settings.
- Click-Through Rate Estimation for Rare Events in Online Advertising44
This chapter develops models and methods to smoothen CTR estimation by taking advantage of the data hierarchy in nature or by clustering and data continuity in time to leverage information from data close to the events of interest.
- The impact of visual appearance on user response in online display advertising43
This paper quantitatively study the relationship between the visual appearance and performance of creatives using large scale data in the world's largest display ads exchange system, RightMedia, and presents recommendations of visual features that have the most important impact on CTR to the professional designers in order to optimize their creative design.
- Nanozymes and Their Potential Roles in the Origin of Life42
This study proposes that nanozymes, nanomaterials with enzyme‐like activities, act as efficient prebiotic catalysts in the origin of life and hopes the research on nanozymes and the origin of life will bridge the gap between inorganic precursors and biomolecules under primitive environments.
- Rare Query Expansion Through Generative Adversarial Networks in Search Advertising39
Using GAN to generate bid keywords directly from query in sponsored search ads selection, especially for rare queries, shows big potential to bring extra revenue improvement and improve the effectiveness and efficiency of query-keyword matching based ads selection in search advertising.
- A stochastic learning-to-rank algorithm and its application to contextual advertising39
This paper proposes a simple yet effective stochastic optimization algorithm to directly minimize any loss function, which can be defined on NDCG or MAP for the learning-to-rank problem, and employs Simulated Annealing along with Simplex method for its parameter search.
- Multimedia Data Mining38
This novel, self-contained book examines how the merging of multimedia and data mining research can promote the understanding and advance the development of knowledge discovery in multimedia data.
- Large-Scale Multi-Label Learning with Incomplete Label Assignments36
This paper proposes an approach, called Mpu, based upon positive and unlabeled stochastic gradient descent and stacked models that can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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