Challenge: Existing literature on relation classification models shows no consensus on optimal architecture .
Approach: They propose a search space for BERT based relation classification models and employ an ENAS method to find better architectures.
Outcome: The proposed method can find better architectures than baseline models on eight benchmark RC tasks.

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Challenge: Existing methods to enhance BERT with factual knowledge about entities require no additional pretraining and no changes to the encoder itself.
Approach: They propose a way to inject factual knowledge into the pretrained BERT model by aligning Wikipedia2Vec entity vectors with BERT's native wordpiece vector space and feeding the aligned entity vector into BERT as if they were wordpieces.
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Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition (D19-1)

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Challenge: Neural architecture search (NAS) is a popular approach for finding new models and freeing researchers from the hard work of designing network architectures.
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RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction (2022.findings-naacl)

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Challenge: Existing approaches to extract relations require large-scale labeled data.
Approach: They propose a Relation Contrastive Learning framework to mitigate similar relations and similar entities problems by optimizing a contrastive instance loss with a relation classification loss on seen relations.
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schuBERT: Optimizing Elements of BERT (2020.acl-main)

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Challenge: Recent Transformer based models have achieved state-of-the-art performance for many natural language processing tasks including machine translation, question-answering tasks and semantic role labeling.
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NERetrieve: Dataset for Next Generation Named Entity Recognition and Retrieval (2023.findings-emnlp)

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Challenge: Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them .
Approach: They propose three variants of the NER task, together with a dataset to support them . they propose a move towards more fine-grained entities and zero-shot recognition .
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Best Practices for Distilling Large Language Models into BERT for Web Search Ranking (2025.coling-industry)

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Challenge: Recent studies have highlighted the potential of Large Language Models (LLMs) as zero-shot relevance rankers.
Approach: They propose to use a ranking loss to transfer ranking knowledge from LLMs to smaller models like BERT.
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Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition (2022.coling-1)

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Challenge: Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories.
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Large-scale Exploration of Neural Relation Classification Architectures (D18-1)

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Challenge: Existing studies on relation classification have been limited to a very narrow range of datasets, making comparisons between approaches difficult.
Approach: They propose a multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features.
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Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs? (2025.findings-emnlp)

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Challenge: Rapid adoption of LLMs has overshadowed the potential advantages of traditional BERT-like models in text classification.
Approach: They compare BERT-like models fine-tuning, LLM internal state utilization, and LLM zero-shot inference across six datasets.
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Named Entity Recognition for Entity Linking: What Works and What’s Next (2021.findings-emnlp)

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Challenge: Entity Linking (EL) systems have achieved impressive results on standard benchmarks thanks to the contextualized representations provided by recent pretrained language models.
Approach: They propose to exploit Named Entity Recognition (NER) to narrow the gap between EL systems trained on high and low amounts of labeled data.
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