Papers with MIR
Variable Typing: Assigning Meaning to Variables in Mathematical Text (N18-1)
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| Challenge: | Scientific documents rely on mathematics to communicate ideas and results . textual contexts are strong domainspecific conventions governing how content is presented . |
| Approach: | They introduce a task of assigning one mathematical type to each variable in a sentence . they also introduce 'variable typing' task that focuses on assignment of meaning to variables . |
| Outcome: | The proposed model is the best performing model on an extrinsic task, the authors show . their model is compared to a formula index only containing raw symbols . |
Evaluating Token-Level and Passage-Level Dense Retrieval Models for Math Information Retrieval (2022.findings-emnlp)
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| Challenge: | a recent study has shown that dense retrieval methods are suboptimal for capturing contextual similarities in complex data. |
| Approach: | They propose to combine a structure search method and efficient bi-encoder dense retrieval models to capture contextual similarities. |
| Outcome: | The proposed model improves on token-level and passage-level dense retrieval tasks. |
Rust-doctor: Enhanced Feature for Rust Ownership and Lifetime Repair with Balanced Training Data Generation (2025.findings-emnlp)
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| Challenge: | Rust has gained popularity due to its safety features during compilation. |
| Approach: | They propose to use a training dataset to generate a balanced training dataset and leverage the Mid-level Intermediate Representation (MIR) as enhanced features for Rust’s unique compilation error repair. |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of Pass@K and Acc@K. |
Learning to Describe Implicit Changes: Noise-robust Pre-training for Image Difference Captioning (2025.findings-emnlp)
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Zixin Guo, Jiayang Sun, Tzu-Jui Julius Wang, Abduljalil Radman, Selen Pehlivan, Min Cao, Jorma Laaksonen
| Challenge: | Large Multimodal Models (LMMs) are used to capture subtle differences between images but are noisy and coarse summaries. |
| Approach: | They propose a noise-robust approach to image difference capture using large multimodal models . they use LMMs with structured prompts to generate fine-grained change descriptions . |
| Outcome: | The proposed model outperforms streamlined architectures and improves inference efficiency. |
Development of a Medical Incident Report Corpus with Intention and Factuality Annotation (2020.lrec-1)
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| Challenge: | Medical incident reports are documents that record what happened in a medical incident. |
| Approach: | They propose to annotate medical incident reports with annotations of intention and factuality and medication entities and their relations. |
| Outcome: | The proposed method combines the definition of medication entities and the method to annotate the relations between entities and extracts important information from the unstructured part. |
A Survey on Multi-modal Intent Recognition: Recent Advances and New Frontiers (2025.findings-emnlp)
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| Challenge: | Multi-modal intent recognition (MIR) requires integrating non-verbal cues from real-world contexts to enhance human intention understanding. |
| Approach: | They present a comprehensive review of multi-modal intent recognition . they provide a survey of the field covering textual, visual, and acoustic signals . |
| Outcome: | The present survey summarises the current state of multi-modal intent recognition . it includes a comprehensive taxonomy and advanced methods . |
MaRF: Leveraging Representation-Level Fusion of Formula Semantics for Mathematical Information Retrieval (2026.findings-acl)
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| Challenge: | Mathematical information retrieval (MIR) relies on combining textual content with mathematical expressions. |
| Approach: | They propose a dual-encoder representation-level fusion framework for MIR that integrates formula semantics into context-aware dense retrieval. |
| Outcome: | The proposed framework outperforms baselines on the ARQMath-3 benchmark. |
AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels (2025.findings-emnlp)
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| Challenge: | Effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data. |
| Approach: | They propose a framework that leverages large language models to generate hypothetical documents . they also propose 'CMIRB' to provide a rigorous evaluation suite . |
| Outcome: | The proposed framework outperforms HyDE in retrieval accuracy and generalization . it leverages large language models to generate hypothetical documents conditioned on a query . |
MIR: Methodology Inspiration Retrieval for Scientific Research Problems (2025.acl-long)
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Aniketh Garikaparthi, Manasi Patwardhan, Aditya Sanjiv Kanade, Aman Hassan, Lovekesh Vig, Arman Cohan
| Challenge: | Existing methods for generating ideas rely on grounding the discovery process within the literature, but their effectiveness varies significantly with the quality and nature of the retrieved literature. |
| Approach: | They construct a methodological inspiration retrieval task using a citation-based methodology adjacency graph and embed an "intuitive prior'' into dense retrievers. |
| Outcome: | The proposed method achieves significant gains in Recall@3 and mAP over strong baselines. |