Challenge: Recent pre-trained language models such as BERT have led to noticeable improvements in semantic similarity detection.
Approach: They propose to explicitly inject linguistic information in the form of word embeddings into any layer of a pre-trained BERT.
Outcome: The proposed method improves on multiple semantic similarity datasets and shows that it is beneficial and currently missing from the original model.

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Challenge: Pre-trained contextual representations like BERT have been widely used for NLP tasks.
Approach: They propose to transform anisotropic sentence embedding distribution to smooth and isotropic Gaussian distribution by normalizing flows that are learned with an unsupervised objective.
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E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT (2020.findings-emnlp)

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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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Fusing Label Embedding into BERT: An Efficient Improvement for Text Classification (2021.findings-acl)

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Challenge: Existing methods to improve text classification performance of pre-trained models have been used to improve their performance.
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Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity (2020.coling-main)

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Challenge: Unsupervised pretraining models encode only distributional knowledge encoded in text corpora, incorporated through language modeling objectives.
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Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (2020.acl-main)

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Challenge: Recent pre-trained language models achieve state-of-the-art performance for downstream NLP tasks.
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Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge (2022.naacl-main)

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Challenge: Existing evidence suggests that pre-trained Transformers encode commonsense knowledge . however, the extent to which this knowledge is acquired is unclear .
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Dict-BERT: Enhancing Language Model Pre-training with Dictionary (2022.findings-acl)

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Challenge: Pre-trained language models (PLMs) capture word semantics in different contexts, hence the embeddings of rare words on the tail are poorly optimized.
Approach: They propose to leverage definitions of rare words in dictionaries to enhance language model pre-training by leveraging dictionary definitions.
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bert2BERT: Towards Reusable Pretrained Language Models (2022.acl-long)

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Challenge: Pre-training large language models can be expensive and wasteful.
Approach: They propose a method which can transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and a two-stage learning method to further accelerate the pre-training.
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Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees (2021.eacl-main)

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Challenge: Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information.
Approach: They propose a plug-and-play framework that incorporates syntax trees into pre-trained Transformers.
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BERT Rediscovers the Classical NLP Pipeline (P19-1)

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Challenge: Pre-trained text encoders have advanced the state of the art on many NLP tasks . Qualitative analysis reveals that the model can and often does adjust this pipeline dynamically .
Approach: They aim to quantify where linguistic information is captured within a network model . they aim to use pre-trained text encoders to displace static word embeddings .
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