Papers by Marc Najork
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling (2020.findings-emnlp)
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Jiecao Chen, Liu Yang, Karthik Raman, Michael Bendersky, Jung-Jung Yeh, Yun Zhou, Marc Najork, Danyang Cai, Ehsan Emadzadeh
| Challenge: | Existing knowledge distillation models are not optimized for dealing with pairs (or tuples) of texts. |
| Approach: | They propose a framework for distilling fast and accurate models on text pair tasks using a scalable end-to-end training strategy. |
| Outcome: | Empirical studies on academic and real-world e-commerce benchmarks show the proposed framework can achieve speedups of over 350x and minimal quality drop relative to the cross-attention teacher BERT model. |
DSI++: Updating Transformer Memory with New Documents (2023.emnlp-main)
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Sanket Mehta, Jai Gupta, Yi Tay, Mostafa Dehghani, Vinh Tran, Jinfeng Rao, Marc Najork, Emma Strubell, Donald Metzler
| Challenge: | Differentiable Search Indices (DSIs) encode a corpus of documents and use the same model to map queries directly to relevant document identifiers. |
| Approach: | They propose a continual learning challenge for Differentiable Search Indices (DSIs) they propose to continuously index new documents while answering queries related to previously and newly indexed documents. |
| Outcome: | The proposed model stably memorizes more documents and improves the average Hits@10 by +21.1% over baselines. |
Creator Context for Tweet Recommendation (2023.emnlp-industry)
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Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang, Michael Bendersky, Marc Najork, Matt Colen, Sergey Levi, Vladimir Ofitserov, Tanvir Amin
| Challenge: | In tweets, people refer to the content it delivers, but also to the person behind it. |
| Approach: | They examine how creator context can be used to advance tweet understanding by recommending relevant tweets to news articles. |
| Outcome: | The proposed model can improve a news article's relevance by recommending relevant tweets to news articles. |
Representation Learning for Information Extraction from Form-like Documents (2020.acl-main)
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| Challenge: | Form-like documents like invoices, purchase orders, tax forms and insurance quotes are common in day-to-day business workflows, but current techniques for processing them largely still employ manual effort or brittle and error-prone heuristics for extraction. |
| Approach: | They propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. |
| Outcome: | The proposed system generates extraction candidates based on neighboring words in the document and is interpretable, as shown using loss cases. |