| Challenge: | a growing number of research libraries, museums, and archives are embracing Web Archiving as a mechanism to collect born-digital material made available via the Web. |
| Approach: | They propose to use dynamic fusion models to find the model that performs best on a variety of document types. |
| Outcome: | The proposed model outperforms individual models and other ensemble methods on three datasets. |
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| Challenge: | Several methods have been proposed for classifying long textual documents using Transformers, but there is a lack of consensus on a benchmark to enable a fair comparison among different approaches. |
| Approach: | They propose to use a dataset to evaluate the relative efficacy of various models for long document classification using Transformers. |
| Outcome: | The proposed models outperform simple baseline models and yield inconsistent performance across datasets. |
A Web Scale Entity Extraction System (2021.findings-emnlp)
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| Challenge: | Existing systems for large-scale entity extraction are limited by the scale and variety of data available on internet platforms. |
| Approach: | They propose to build an entity extraction system for multiple document types at large scale using multi-modal Transformers. |
| Outcome: | The proposed system extracts multiple types of entities from multiple document types at large scale using multi-modal Transformers. |
A supervised approach to taxonomy extraction using word embeddings (L18-1)
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| Challenge: | a recent evaluation of a method for organizing texts into a hierarchy showed that it did not outperform a baseline. |
| Approach: | They propose a method that uses supervised learning to combine multiple features with a support vector machine classifier including the baseline features. |
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Dynamic Context Extraction for Citation Classification (2022.aacl-main)
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| Challenge: | Prior studies have focused on the application of fixed-size contiguous citation contexts or manually curated citation contextual contexts. |
| Approach: | They propose an automated unsupervised approach for the selection of a dynamic-size and potentially non-contiguous citation context based on transformer-based document representations and embedding similarities. |
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Dynamic Memory Induction Networks for Few-Shot Text Classification (2020.acl-main)
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| Challenge: | Recent studies have shown that models can benefit from query-aware methods for few-shot text classification. |
| Approach: | They propose a dynamic memory-based network for few-short text classification that uses static memory to adapt to unseen classes. |
| Outcome: | The proposed model improves on the miniRCV1 and ODIC datasets by 24% . Detailed analysis is performed to show how the proposed network achieves the new performance. |
CoEvo: Coevolution of LLM and Retrieval Model for Domain-Specific Information Retrieval (2025.emnlp-main)
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Ang Li, Yiquan Wu, Yinghao Hu, Lizhi Qing, Shihang Wang, Chengyuan Liu, Tao Wu, Adam Jatowt, Ming Cai, Fei Wu, Kun Kuang
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| Approach: | They propose a framework that facilitates the coevolution of large language models and retrieval models. |
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QuadrupletBERT: An Efficient Model For Embedding-Based Large-Scale Retrieval (2021.naacl-main)
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| Challenge: | Existing methods for large-scale query-document retrieval are expensive and require sparse handcrafted features. |
| Approach: | They propose a quadrupletBERT model for effective and efficient retrieval using pre-trained language models like BERT. |
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MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs (2025.naacl-long)
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Yuhang Zhou, Giannis Karamanolakis, Victor Soto, Anna Rumshisky, Mayank Kulkarni, Furong Huang, Wei Ai, Jianhua Lu
| Challenge: | State-of-the-art methods for merging expert models with different architectures do not address parameter interference and require extensive fine-tuning to restore performance. |
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Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference (2021.findings-acl)
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| Challenge: | Existing approaches to document-to-document similarity ranking are limited to relatively short documents or lack similarity labels. |
| Approach: | They propose a self-supervised method for document similarity ranking that can be applied to documents of arbitrary length. |
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Dynamic Data Mixing Maximizes Instruction Tuning for Mixture-of-Experts (2025.naacl-long)
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| Challenge: | Mixture-of-Experts (MoE) models are constrained by their fixed model capacities when the number of tasks grows in instruction tuning. |
| Approach: | They propose to combine all training tasks and apply fixed sampling weights without considering the importance of different tasks as the model training state changes. |
| Outcome: | The proposed method can be used on knowledge & reasoning tasks and open-ended queries with limited training budget. |