Papers with live demo
ELEVANT: A Fully Automatic Fine-Grained Entity Linking Evaluation and Analysis Tool (2022.emnlp-demos)
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| Challenge: | a tool for fine-grained evaluation of entity linkers on benchmarks is presented . a typical evaluation shows that overall precision and recall are poor . particular benchmarks often require very particular skills from an entity linker . |
| Approach: | They propose a tool for fine-grained evaluation of entity linkers on benchmarks . they use a graph-based tool to analyze performance of a set of entity links . |
| Outcome: | The proposed tool provides an automatic breakdown of the performance by error categories and by entity type. |
RepGraph: Visualising and Analysing Meaning Representation Graphs (2021.emnlp-demo)
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| Challenge: | Graph-based meaning representations provide rich semantic annotations, but visualising them clearly is more challenging than for fully lexicalized representations. |
| Approach: | They propose to use RepGraph to visualise, manipulate and analyse semantically parsed graph data in a JSON-based serialisation format. |
| Outcome: | The proposed visualisation and analysis tool supports DMRS, EDS, PTG, UCCA, and AMR semantic frameworks. |
AlignFix: A Tool for Parallel Corpora Augmentation and Refinement (2026.eacl-demo)
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| Challenge: | High-quality datasets are crucial for training effective state of the art machine translation systems, but they can be noisy and degrade performance. |
| Approach: | They propose an open-source tool for augmenting data, identifying and correcting errors in parallel corpora. |
| Outcome: | The tool extracts consistent phrase pairs, enabling targeted replacements that can improve the dataset quality. |
Multilingual Entity, Relation, Event and Human Value Extraction (N19-4)
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| Challenge: | Existing systems that extract knowledge elements from multiple languages and documents do not aggregate knowledge from multiple documents and languages. |
| Approach: | They propose a multilingual knowledge extraction system that performs entity discovery and linking, relation extraction, event extraction, and coreference. |
| Outcome: | The proposed system performs entity discovery and linking, relation extraction, event extraction, and coreference. |
IrEne-viz: Visualizing Energy Consumption of Transformer Models (2021.emnlp-demo)
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Yash Kumar Lal, Reetu Singh, Harsh Trivedi, Qingqing Cao, Aruna Balasubramanian, Niranjan Balasubramanian
| Challenge: | IrEne is an energy prediction system that accurately predicts inference energy consumption of transformer-based NLP models. |
| Approach: | They present an online platform for visualizing and exploring energy consumption of transformer-based NLP models. |
| Outcome: | The proposed system predicts energy consumption of transformer-based models and their components. |
TabGenie: A Toolkit for Table-to-Text Generation (2023.acl-demo)
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| Challenge: | TabGenie enables researchers to explore, preprocess, and analyze data-to-text generation datasets. |
| Approach: | They present TabGenie, a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets. |
| Outcome: | The toolkit provides an interactive mode for debugging table-to-text generation, side-by-side comparison of generated system outputs, and easy exports for manual analysis. |
LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) struggle with hallucinations, handling domain-specific data effectively, and integrating experimental workflows. |
| Approach: | They propose a hierarchical multi-agent framework to emulate the materials science research workflow by combining a new uncertainty and confidence estimate to evaluate the self-consistency of responses from LLaMP and baseline methods. |
| Outcome: | The proposed framework performs better than existing methods in material property retrieval, crystal structure editing, and annealing molecular dynamics simulations. |