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DUBLIN: Visual Document Understanding By Language-Image Network.

, , , , , , , , , and . EMNLP (Industry Track), page 693-706. Association for Computational Linguistics, (2023)

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MS MARCO: A Human Generated MAchine Reading COmprehension Dataset, , , , , , , , , and 5 other author(s). (2016)cite arxiv:1611.09268.Generic Intent Representation in Web Search., , , , , , and . CoRR, (2019)An Axiomatic Approach to Regularizing Neural Ranking Models., , , , , and . SIGIR, page 981-984. ACM, (2019)Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model., , , , , , , , , and 10 other author(s). CoRR, (2022)Leading Conversational Search by Suggesting Useful Questions., , , , , , and . WWW, page 1160-1170. ACM / IW3C2, (2020)Towards Language Agnostic Universal Representations., , and . ACL (1), page 4033-4041. Association for Computational Linguistics, (2019)Invariant Language Modeling., , , , , , , , and . EMNLP, page 5728-5743. Association for Computational Linguistics, (2022)METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals., , , , , , , , , and . CoRR, (2022)Towards Language Agnostic Universal Representations., , and . CoRR, (2018)Knowledge-Aware Language Model Pretraining., , , , , and . CoRR, (2020)