Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event Extraction Article Swipe
Jiaqi Li
,
Chuanyi Zhang
,
Miaozeng Du
,
Dehai Min
,
Yongrui Chen
,
Guilin Qi
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.emnlp-main.103
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2023.emnlp-main.103
Text-video based multimodal event extraction refers to identifying event information from the given text-video pairs. Existing methods predominantly utilize video appearance features (VAF) and text sequence features (TSF) as input information. Some of them employ contrastive learning to align VAF with the event types extracted from TSF. However, they disregard the motion representations in videos and the optimization of contrastive objective could be misguided by the background noise from RGB frames. We observe that the same event triggers correspond to similar motion trajectories, which are hardly affected by the background noise. Moviated by this, we propose a Three Stream Multimodal Event Extraction framework (TSEE) that simultaneously utilizes the features of text sequence and video appearance, as well as the motion representations to enhance the event extraction capacity. Firstly, we extract the optical flow features (OFF) as motion representations from videos to incorporate with VAF and TSF. Then we introduce a Multi-level Event Contrastive Learning module to align the embedding space between OFF and event triggers, as well as between event triggers and types. Finally, a Dual Querying Text module is proposed to enhance the interaction between modalities. Experimental results show that TSEE outperforms the state-of-the-art methods, which demonstrates its superiority.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2023.emnlp-main.103
- https://aclanthology.org/2023.emnlp-main.103.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 54
- Related Works
- 10
- OpenAlex ID
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- DOI
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https://doi.org/10.18653/v1/2023.emnlp-main.103Digital Object Identifier
- Title
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Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event ExtractionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
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Jiaqi Li, Chuanyi Zhang, Miaozeng Du, Dehai Min, Yongrui Chen, Guilin QiList of authors in order
- Landing page
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https://doi.org/10.18653/v1/2023.emnlp-main.103Publisher landing page
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https://aclanthology.org/2023.emnlp-main.103.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://aclanthology.org/2023.emnlp-main.103.pdfDirect OA link when available
- Concepts
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Computer science, Event (particle physics), Artificial intelligence, Embedding, Motion (physics), Feature extraction, Computer vision, Natural language processing, Pattern recognition (psychology), Speech recognition, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| publication_date | 2023-01-01 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W4289860834, https://openalex.org/W2963524571, https://openalex.org/W3174564426, https://openalex.org/W3196642073, https://openalex.org/W2972811696, https://openalex.org/W2560474170, https://openalex.org/W2250575108, https://openalex.org/W4304086159, https://openalex.org/W4385572694, https://openalex.org/W4312921869, https://openalex.org/W3035027743, https://openalex.org/W4385573172, https://openalex.org/W4229024390, https://openalex.org/W3096682293, https://openalex.org/W2997585029, https://openalex.org/W3034318565, https://openalex.org/W4367694280, https://openalex.org/W2187089797, https://openalex.org/W764651262, https://openalex.org/W4385573653, https://openalex.org/W4226151448, https://openalex.org/W4224038173, https://openalex.org/W2475245295, https://openalex.org/W4205157616, https://openalex.org/W3182809356, https://openalex.org/W4281488715, https://openalex.org/W2984582583, https://openalex.org/W3176032431, https://openalex.org/W4324333799, https://openalex.org/W4288089799, https://openalex.org/W4387967963, https://openalex.org/W2971036674, https://openalex.org/W3211570770, https://openalex.org/W4319299902, https://openalex.org/W4312407537, https://openalex.org/W2963782415, https://openalex.org/W2766863698, https://openalex.org/W4221166835, https://openalex.org/W3104597568, https://openalex.org/W4385153915, https://openalex.org/W4287812705, https://openalex.org/W2896457183, https://openalex.org/W3181951703, https://openalex.org/W4287614078, https://openalex.org/W2423576022, https://openalex.org/W3157035436, https://openalex.org/W4365808273, https://openalex.org/W3147875291, https://openalex.org/W3034184697, https://openalex.org/W2963346996, https://openalex.org/W3109908659, https://openalex.org/W3188030217, https://openalex.org/W4280599434, https://openalex.org/W4388144313 |
| referenced_works_count | 54 |
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| abstract_inverted_index.(VAF) | 22 |
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| citation_normalized_percentile.value | 0.48423454 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |