Papers with PageRank
PR-XAI: PageRank-Based Feature Attribution for Transformers (2026.acl-long)
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| Challenge: | Existing feature attribution methods for transformer models suffer from limitations that undermine their efficacy. |
| Approach: | They propose a feature attribution method for transformer models based on PageRank . they propose attribution methods that apply PageRank to attention-derived graphs . |
| Outcome: | The proposed method outperforms state-of-the-art methods in faithfulness and classification metrics with significant gains on long-form text. |
Enhancing Descriptive Image Captioning with Natural Language Inference (2021.acl-short)
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| Challenge: | Existing captioning models tend to generate generic captions, but generating descriptive captions is important. |
| Approach: | They propose a novel approach to encourage captioning models to produce more detailed captions using natural language inference. |
| Outcome: | The proposed method outperforms baseline models on MSCOCO metrics on descriptiveness and descriptiveness. |
Facts That Matter (D18-1)
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| Challenge: | Existing methods to discover facts from natural language text are based on relation extraction and open information extraction. |
| Approach: | They propose a task of generating a machine-readable representation of the most prominent information in a text document as a set of facts. |
| Outcome: | The proposed system outperforms baselines and text summarizers in a supervised evaluation of salience tasks. |
HiFi: High-Information Attention Heads Hold for Parameter-Efficient Model Adaptation (2023.acl-long)
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| Challenge: | Existing paradigm to fine-tune parameters of pre-trained language models poses problems in data-scarce and resource-limited scenarios. |
| Approach: | They propose a parameter-efficient fine-tuning method HiFi that fine-tails only the highly informative and strongly correlated attention heads for the specific task. |
| Outcome: | The proposed method obtains state-of-the-art over the prior benchmarks on the GLUE benchmark. |
Linking, Searching, and Visualizing Entities in Wikipedia (L18-1)
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| Challenge: | Existing systems to extract, index, search, and visualize entities in Wikipedia are not strings, but unique identifiers from Wikidata. |
| Approach: | They propose a system to extract, index, search, and visualize entities in Wikipedia . they use a document model to store linguistic annotations and a string matching engine . |
| Outcome: | The proposed system achieves CEAFm scores of 70.0 on English, 64.4 on Chinese, and 66.5 on Spanish. |
Varying Vector Representations and Integrating Meaning Shifts into a PageRank Model for Automatic Term Extraction (2020.lrec-1)
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| Challenge: | a comparative study for automatic term extraction from domain-specific language using a PageRank graph algorithm with different edge-weighting methods. |
| Approach: | They propose to use a PageRank algorithm to extract automatic terms from domain-specific language using different edge-weighting methods. |
| Outcome: | The proposed model is compared with a PageRank model with different edge-weighting methods. |
Hedwig: A Named Entity Linker (2020.lrec-1)
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| Challenge: | Named entity linking is the task of identifying mentions of named things in text . e.g., "Barack Obama" or "New York" are examples of named entities . |
| Approach: | They propose an end-to-end named entity linker that uses BILSTM models for mention detection and a PageRank algorithm for entity linking. |
| Outcome: | The proposed named entity linker performs better than the previous generation, and is trilingually better. |