Papers with IDF
NEAT-IR: Neural Explainable Analysis Tool for Information Retrieval (2026.acl-srw)
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| Challenge: | Neural IR models achieve strong performance but remain difficult to interpret. |
| Approach: | They propose a black-box analysis framework that explains ColBERT’s ranking behavior using 26 classical IR features. |
| Outcome: | The proposed framework preserves ColBERT’s rankings nearly perfectly, yet only explain R2 0.28 of score variance. |
Semantic Frame Forecast (2021.naacl-main)
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| Challenge: | Prior work focused on predicting the immediate future of a story, such as one to a few sentences ahead. |
| Approach: | They propose a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. |
| Outcome: | The proposed model outperforms random, prior, and replay baselines when the block size is over 150 sentences. |
The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks (2020.coling-main)
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| Challenge: | Contextual embeddings have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity. |
| Approach: | They propose to integrate transformer-based neural language models into their models to achieve relative improvements of up to 36% on granular tasks. |
| Outcome: | The proposed model outperforms baselines for more granular tasks while outperforming TF-IDF for more complex tasks. |
Effective Contrastive Weighting for Dense Query Expansion (2023.acl-long)
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| Challenge: | Verbatim queries that do not adequately express the user's search intent are often lexical inadequacies. |
| Approach: | They propose a contrastive weighting model that learns to select the most useful expansion embeddings for semantic search. |
| Outcome: | The proposed model outperforms existing methods while maintaining its efficiency. |
NASH: Numerically Aware Scoring Heuristic for Robust Semantic Similarity (2026.findings-acl)
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| Challenge: | Numerical precision is critical in financial NLP, yet embedding-based semantic similarity metrics exhibit numerical blindness. |
| Approach: | They propose a model-agnostic metric that decouples numerical verification from textual semantic evaluation. |
| Outcome: | The proposed metric improves numerical sensitivity while maintaining general semantic performance. |
CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics (2026.acl-long)
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| Challenge: | Using a semantic memory, we score each utterance along three interpretable dimensions: Novelty, Relevance, and Implication Scope. |
| Approach: | They propose a framework for Conversational Information Gain that evaluates each utterance in terms of how it advances collective understanding of the target topic. |
| Outcome: | The proposed framework evaluates each utterance in terms of how it advances collective understanding of the target topic. |