M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction | Paper Citation Record

Observation 8593755e-5d6b-433e-baff-aa8db76bb587 · outbound

This paper cites DiffusionNER: Boundary diffusion for named entity recognition,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction DiffusionNER: Boundary diffusion for named entity recognition,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:57.098637Z

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Observation 46c8358c-55d4-491d-87e7-4dafacc56f73 · outbound

This paper cites Dual cache for long document neural coreference resolution,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Dual cache for long document neural coreference resolution,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:57.082147Z

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Observation 4a8f2460-1eaa-4815-850b-1764e609ff6b · outbound

This paper cites An autoregressive text-to-graph framework for joint entity and relation extraction,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction An autoregressive text-to-graph framework for joint entity and relation extraction,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:57.064499Z

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Observation 35ec8c6f-d5fe-4053-ad91-9bf35c1e0844 · outbound

This paper cites Event extraction as question generation and answering,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Event extraction as question generation and answering,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:57.047700Z

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Observation daa359ee-d090-4e94-80c5-e864f9b48fd6 · outbound

This paper cites Rethinking boundaries: End-to-end recognition of discontinuous mentions with pointer networks,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Rethinking boundaries: End-to-end recognition of discontinuous mentions with pointer networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:57.029864Z

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Observation 0b73483d-7035-4c59-aa5b-37e6ccfba00c · outbound

This paper cites A span-based model for joint overlapped and discontinuous named entity recognition,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction A span-based model for joint overlapped and discontinuous named entity recognition,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:57.010404Z

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Observation fac3f90b-679e-4034-aa33-1d6960dc460e · outbound

This paper cites Unified named entity recognition as word-word relation classification,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Unified named entity recognition as word-word relation classification,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.991325Z

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Observation 085f8686-478d-456b-9cb0-116e5f2da0b2 · outbound

This paper cites Knowledge enhanced coreference resolution via gated attention,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Knowledge enhanced coreference resolution via gated attention,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.972872Z

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Observation 7dd0163b-1bad-41fc-835e-eaa662995bae · outbound

This paper cites Double graph based reasoning for document-level relation extraction,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Double graph based reasoning for document-level relation extraction,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.953381Z

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Observation 22b73191-308e-44e7-8b50-839af3353d3a · outbound

This paper cites Coreference resolution without span representations,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Coreference resolution without span representations,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.933021Z

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Observation f7f6b36d-0c67-497c-abdf-afe1d63d9955 · outbound

This paper cites A sequence-to-sequence approach for document-level relation extraction,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction A sequence-to-sequence approach for document-level relation extraction,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.915822Z

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Observation 20c8838f-bcaa-4d4a-a2b0-2e48930fb651 · outbound

This paper cites Visual attention model for name tagging in multimodal social media,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Visual attention model for name tagging in multimodal social media,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.899191Z

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Observation 6d315406-ac50-46b1-aaea-0ba94cd620c1 · outbound

This paper cites Adaptive co-attention network for named entity recognition in tweets,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Adaptive co-attention network for named entity recognition in tweets,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.882580Z

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Observation 127096dd-19e9-442e-b007-70ea11d91e36 · outbound

This paper cites A large-scale chinese multimodal ner dataset with speech clues,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction A large-scale chinese multimodal ner dataset with speech clues,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.866161Z

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Observation 21f13c5b-8483-4977-b277-ce850b99b0a7 · outbound

This paper cites Who are you referring to? coreference resolution in image narrations,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Who are you referring to? coreference resolution in image narrations,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.831366Z

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Observation d366b976-7698-4089-944c-07597330d6f4 · outbound

This paper cites Mnre: A challenge multimodal dataset for neural relation extraction with visual evidence in social media posts,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Mnre: A challenge multimodal dataset for neural relation extraction with visual evidence in social media posts,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.811261Z

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Observation ee792f39-f53b-48f1-8a22-1e680a79554c · outbound

This paper cites A hierarchical network for multimodal document-level relation extraction,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction A hierarchical network for multimodal document-level relation extraction,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.794259Z

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Observation 64464a7c-2ce7-4c67-b625-b49e06889e5a · outbound

This paper cites Grounded multimodal named entity recognition on social media,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Grounded multimodal named entity recognition on social media,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.849101Z

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Observation 3b718cd1-8fc2-412e-be63-439595997b9c · outbound

This paper cites Semi-supervised multimodal coreference resolution in image narrations,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Semi-supervised multimodal coreference resolution in image narrations,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.775857Z

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Observation f55bcf04-1a0f-4fb6-869e-cfbbf9ab1d60 · outbound

This paper cites Joint multimodal entity-relation extraction based on edge-enhanced graph alignment network and word- pair relation tagging,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Joint multimodal entity-relation extraction based on edge-enhanced graph alignment network and word- pair relation tagging,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.759051Z

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Observation 3bff049b-ab28-422b-b1af-03f0c210678b · outbound

This paper cites Multimodal relation extraction with efficient graph alignment,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Multimodal relation extraction with efficient graph alignment,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.740778Z

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Observation 1700b069-affd-4949-a99c-3afae2238976 · outbound

This paper cites Docred: A large-scale document-level relation extraction dataset,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Docred: A large-scale document-level relation extraction dataset,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.724088Z

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Observation ce4a9515-a6dd-414c-a1f1-7c77d4244199 · outbound

This paper cites Improving multimodal named entity recognition via entity span detection with unified multimodal transformer,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Improving multimodal named entity recognition via entity span detection with unified multimodal transformer,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.706299Z

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Observation dcea31c6-3b17-4c7d-8673-8791ac23cdef · outbound

This paper cites A span-based multimodal variational autoencoder for semi-supervised multimodal named entity recognition,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction A span-based multimodal variational autoencoder for semi-supervised multimodal named entity recognition,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.688592Z

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Observation a925a4fa-ce05-4b19-b1dd-99d624be8349 · outbound

This paper cites Entity- level interaction via heterogeneous graph for multimodal named entity recognition,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Entity- level interaction via heterogeneous graph for multimodal named entity recognition,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.670543Z

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Observation 0b949fab-1e68-48aa-b1b8-73d0f3058adc · outbound

This paper cites Prompt- ing chatgpt in mner: Enhanced multimodal named entity recognition with auxiliary refined knowledge,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Prompt- ing chatgpt in mner: Enhanced multimodal named entity recognition with auxiliary refined knowledge,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.650947Z

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Observation 6b13dd77-f9ea-4701-b6d7-a924b15827e6 · outbound

This paper cites Gravl-bert: graphical visual-linguistic representations for multimodal coreference resolution,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Gravl-bert: graphical visual-linguistic representations for multimodal coreference resolution,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.632958Z

Source-reported events for the cited work

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Observation 6a183391-336c-4d56-8247-c8f194b86873 · outbound

This paper cites Good visual guidance make a better extractor: Hierarchical visual prefix for multimodal entity and relation extraction,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Good visual guidance make a better extractor: Hierarchical visual prefix for multimodal entity and relation extraction,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.615185Z

Source-reported events for the cited work

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Observation 289e4c81-903e-449d-9171-41c4d900c578 · outbound

This paper cites Rethinking multimodal entity and relation extraction from a translation point of view,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Rethinking multimodal entity and relation extraction from a translation point of view,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.597643Z

Source-reported events for the cited work

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Observation aba6abb6-d9f3-43dd-b520-2b7b4884d727 · outbound

This paper cites Information screening whilst exploiting! multimodal relation extraction with feature denoising and multimodal topic modeling,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Information screening whilst exploiting! multimodal relation extraction with feature denoising and multimodal topic modeling,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.579968Z

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Observation 59b3563e-06c6-444f-be96-14b66d40ae54 · outbound

This paper cites Transvg: End-to-end visual grounding with transformers,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Transvg: End-to-end visual grounding with transformers,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T21:53:55.812633Z

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Observation 10d8a44e-499c-4f42-9bad-5b21ba5e3538 · outbound

This paper cites Shifting more attention to visual backbone: Query-modulated refinement networks for end-to-end visual grounding,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Shifting more attention to visual backbone: Query-modulated refinement networks for end-to-end visual grounding,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.549750Z

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Observation b6a867e3-aab2-4351-b594-4ae5589fcb64 · outbound

This paper cites You only look once: Unified, real-time object detection,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction You only look once: Unified, real-time object detection,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T21:53:55.823763Z

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This paper cites Ssd: Single shot multibox detector,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Ssd: Single shot multibox detector,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.519786Z

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Observation 70f282fc-3a62-43f4-821c-dbd7404185a1 · outbound

This paper cites Ref-nms: Breaking proposal bottlenecks in two-stage referring expression grounding,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Ref-nms: Breaking proposal bottlenecks in two-stage referring expression grounding,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.500810Z

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Observation c63856b6-a7dc-4429-b84e-660cddeb2d03 · outbound

This paper cites Missing modalities imputation via cascaded residual autoencoder,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Missing modalities imputation via cascaded residual autoencoder,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.481316Z

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Observation 29585045-df22-4ae7-8611-bcf375eb6047 · outbound

This paper cites Lrmm: Learning to recommend with missing modalities,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Lrmm: Learning to recommend with missing modalities,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.463516Z

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Observation 3d5846a8-68ac-426e-9596-808564db30f7 · outbound

This paper cites Dealing with missing modalities in the visual question answer-difference prediction task through knowledge distillation,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Dealing with missing modalities in the visual question answer-difference prediction task through knowledge distillation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.445404Z

Source-reported events for the cited work

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Observation 93c3b3cb-0854-4ddc-807a-2ed9696d3f8a · outbound

This paper cites A unified self-distillation framework for multimodal sentiment analysis with uncertain missing modalities,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction A unified self-distillation framework for multimodal sentiment analysis with uncertain missing modalities,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.427616Z

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Observation 398e2ac7-d9c4-4c6b-8fef-6f8e1c49bfbf · outbound

This paper cites Missing modality imagination network for emotion recognition with uncertain missing modalities,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Missing modality imagination network for emotion recognition with uncertain missing modalities,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T21:53:55.860148Z

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Observation 3d58a6d9-b30a-42cb-a99e-9020de48ed42 · outbound

This paper cites Mitigating inconsistencies in multimodal sentiment analysis under uncertain missing modalities,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Mitigating inconsistencies in multimodal sentiment analysis under uncertain missing modalities,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T21:53:55.865429Z

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Observation 39c5209d-1341-4f87-a005-90bf62d45817 · outbound

This paper cites Multimodal prompting with missing modalities for visual recognition,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Multimodal prompting with missing modalities for visual recognition,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.386234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1f90a499-dbcd-4942-8037-355dd9c53f14 · outbound

This paper cites Multimodal prompt learning with missing modalities for sentiment analysis and emotion recognition,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Multimodal prompt learning with missing modalities for sentiment analysis and emotion recognition,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.368424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 29abe825-40e1-4009-826e-5e293f1e5201 · outbound

This paper cites Longformer: The Long-Document Transformer.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Longformer: The Long-Document Transformer

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T21:53:55.881703Z

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Observation 2fd49c31-be75-4af4-84ec-3e9a8d5c17d2 · outbound

This paper cites Visual Transformers: Token-based Image Representation and Processing for Computer Vision.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Visual Transformers: Token-based Image Representation and Processing for Computer Vision

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T21:53:55.887844Z

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Observation e36b2296-2cc4-4840-883f-2255bf452579 · outbound

This paper cites A primer in bertology: What we know about how bert works,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction A primer in bertology: What we know about how bert works,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.349562Z

Source-reported events for the cited work

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Observation 548d5047-ec8e-48f8-9686-4e80da3b10bc · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.328333Z

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Observation 9696bcfe-f93b-4c3c-aff7-4a3c73dba5d9 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.309219Z

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This paper cites Auto-encoding variational bayes,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Auto-encoding variational bayes,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.289980Z

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This paper cites Exploring universal intrinsic task subspace for few-shot learning via prompt tuning,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Exploring universal intrinsic task subspace for few-shot learning via prompt tuning,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.267587Z

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This paper cites Attention is all you need,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Attention is all you need,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.247428Z

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Observation f589483d-954d-4cfd-a5eb-b4ef49dc5c17 · outbound

This paper cites Deep residual learning for image recognition,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Deep residual learning for image recognition,

Reference 53

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This paper cites Conditional random fields: Probabilistic models for segmenting and labeling sequence data,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Conditional random fields: Probabilistic models for segmenting and labeling sequence data,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.215489Z

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This paper cites VisualBERT: A Simple and Performant Baseline for Vision and Language.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction VisualBERT: A Simple and Performant Baseline for Vision and Language

Reference 55

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This paper cites Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks,

Reference 56

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This paper cites Vivit: A video vision transformer,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Vivit: A video vision transformer,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T21:53:55.945512Z

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This paper cites Videomae: Masked autoen- coders are data-efficient learners for self-supervised video pre-training,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Videomae: Masked autoen- coders are data-efficient learners for self-supervised video pre-training,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T21:53:55.950362Z

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Observation e0ea64b8-955c-4ba8-972f-e48df9251217 · outbound

This paper cites Video-llama: An instruction-tuned audio- visual language model for video understanding,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Video-llama: An instruction-tuned audio- visual language model for video understanding,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T21:53:55.955213Z

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Observation dd061f07-0ff1-49b2-981b-b3c6017764f1 · outbound

This paper cites Video-chatgpt: Towards detailed video understanding via large vision and language models,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Video-chatgpt: Towards detailed video understanding via large vision and language models,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.132655Z

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Observation 060b76a4-32cf-41e9-a904-9eddfe4745bb · outbound

This paper cites Parallel data helps neural entity coreference resolution,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Parallel data helps neural entity coreference resolution,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.115435Z

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Observation a6c55131-0d59-431e-ba0e-6846f88eba82 · outbound

This paper cites Toe: A grid-tagging discontinuous ner model enhanced by embedding tag/word relations and more fine-grained tags,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction Toe: A grid-tagging discontinuous ner model enhanced by embedding tag/word relations and more fine-grained tags,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:53:56.095439Z

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Observation acb1842e-afd2-49d1-9f2f-3db8a1d6502e · outbound

This paper cites A fast and accurate one-stage approach to visual grounding,.

M$^{3}$D: A Multimodal, Multilingual and Multitask Dataset for Grounded Document-level Information Extraction A fast and accurate one-stage approach to visual grounding,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T21:53:55.976780Z

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