Papers with UMR
UMR-Writer: A Web Application for Annotating Uniform Meaning Representations (2021.emnlp-demo)
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| Challenge: | Uniform Meaning Representations (UMRs) are graph-based semantic representations that can be used to annotate text. |
| Approach: | They present a web-based application for annotating Uniform Meaning Representations (UMR) they propose to use a graph-based cross-linguistically applicable semantic representation to annotate sentences and documents. |
| Outcome: | The proposed tool is based on a graph-based, cross-linguistically applicable semantic representation that can be used to annotate text. |
Can Uniform Meaning Representation Help GPT-4 Translate from Indigenous Languages? (2025.acl-short)
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| Challenge: | In this study, we examine the downstream utility of Uniform Meaning Representation (UMR) for low-resource languages. |
| Approach: | They explore the utility of Uniform Meaning Representation (UMR) for low-resource languages by incorporating it into GPT-4 prompts. |
| Outcome: | The proposed model performs better than existing models in Navajo, Arápaho, and Kukama with and without demonstrations and annotations. |
Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling (2024.findings-eacl)
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| Challenge: | Existing sparse retrieval methods often yield inferior performance in multilingual retrieval, requiring a large amount of paired data, which is costly. |
| Approach: | They propose an Unsupervised Multilingual dense Retriever trained without paired data which iteratively improves performance of multilingual retrievers. |
| Outcome: | The proposed framework outperforms supervised baselines on two benchmark datasets and shows that iterative training improves the performance. |
Annotate Chinese Aspect with UMR——a Case Study on the Liitle Prince (2024.lrec-main)
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| Challenge: | Uniform Meaning Representation (UMR) is a graphbased cross-linguistically applicable semantic representation that allows for deep semantic analysis. |
| Approach: | They propose to use an aspectual lattice to adapt to different languages and design values that encompass both viewpoint aspect and situation aspect. |
| Outcome: | The proposed representations are based on the Chinese version of The Little Prince and are compared with other representations. |
MiMIC: Mitigating Visual Modality Collapse in Universal Multimodal Retrieval While Avoiding Semantic Misalignment (2026.findings-acl)
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| Challenge: | Existing UMR methods can be broadly divided into two categories: early-fusion approaches, such as Marvel, which projects visual features into the language model space for integrating with text modality, and late-fusion methods, such UniVL-DR, which encode visual and textual inputs using separate encoders and obtain fused embeddings through addition. |
| Approach: | They propose to map different modalities into a shared embedding space for multi-modal retrieval. |
| Outcome: | Experiments on the WebQA+ and EVQA+ datasets show that MiMIC outperforms both early- and late-fusion approaches. |
Bootstrapping UMR Annotations for Arapaho from Language Documentation Resources (2024.lrec-main)
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| Challenge: | Uniform Meaning Representation (UMR) is a graph-based semantic labeling system . it is based on the AMR family and is designed to be uniformly applicable to typologically diverse languages. |
| Approach: | They propose methods for bootstrapping UMR annotations for a given language from existing resources and typical language documentation products. |
| Outcome: | The proposed method generates enough basic structure in UMR graphs to automate labeling to a significant extent. |
Building a Broad Infrastructure for Uniform Meaning Representations (2024.lrec-main)
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Julia Bonn, Matthew J. Buchholz, Jayeol Chun, Andrew Cowell, William Croft, Lukas Denk, Sijia Ge, Jan Hajič, Kenneth Lai, James H. Martin, Skatje Myers, Alexis Palmer, Martha Palmer, Claire Benet Post, James Pustejovsky, Kristine Stenzel, Haibo Sun, Zdeňka Urešová, Rosa Vallejos, Jens E. L. Van Gysel, Meagan Vigus, Nianwen Xue, Jin Zhao
| Challenge: | This paper reports the first release of the UMR data set for six languages . it includes annotations for six different languages that vary greatly in terms of their linguistic properties and resource availability. |
| Approach: | They report the first release of the UMR data set for six languages . they describe on-going efforts to enlarge the data set and extend it to other languages - including Navajo, Navájo, and Sanapaná . |
| Outcome: | The first release of the UMR data set includes annotations for six languages . the language dataset is available for free and can be extended to other languages if needed . |
Uncertainty-Guided Modal Rebalance for Hateful Memes Detection (2024.acl-long)
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| Challenge: | Existing methods for integrating hate information from different modalities ignore the modality uncertainty caused by the contribution degree of each modality to hate sentiment. |
| Approach: | They propose an Uncertainty-guided Modal Rebalance framework for hateful memes detection . they propose to combine cross-modal fusion features with unimodal features . |
| Outcome: | The proposed framework produces state-of-the-art performance on four widely-used datasets. |
AnCast++: Document-Level Evaluation of Graph-based Meaning Representations (2025.findings-acl)
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| Challenge: | Abstract Meaning Representation (UMR) is a cross-lingual document-level graph-based representation that extends it to document- level semantic annotations. |
| Approach: | They propose an evaluation metric that unifies evaluation of four distinct sub-structures of UMR. |
| Outcome: | The proposed metric is made available on Github. |