Papers with BERT-
AdaBERT-CTC: Leveraging BERT-CTC for Text-Only Domain Adaptation in ASR (2023.emnlp-industry)
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| Challenge: | End-to-end (E2E) automatic speech recognition models struggle to recognize out-of-domain words such as proper nouns and domain-specific terms. |
| Approach: | They propose a domain adaptation technique that relies solely on textual data to adapt to out-of-domain words. |
| Outcome: | The proposed method outperforms the base model by up to 14% relative word error rate improvement on several out-of-domain, publicly available datasets. |
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders (2023.findings-emnlp)
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| Challenge: | Existing methods to rank sentences using pre-trained embeddings create a gap due to different optimization objectives. |
| Approach: | They propose a pre-trained embedding process that optimizes informative sentences . they use sentence-word bipartite graphs to model intra-sentential distinctive features . |
| Outcome: | The proposed model outperforms heavy BERT- or RoBERTa-based sentence ranking methods by providing summary-worthy representations. |
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition (2021.acl-long)
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| Challenge: | Existing NER models are supervised by a large number of training sequences, each pre-annotated with token-level labels. |
| Approach: | They propose a conditional hidden Markov model which can effectively infer true labels from multi-source noisy labels in an unsupervised way. |
| Outcome: | The proposed model outperforms state-of-the-art weakly supervised NER models on four benchmarks from various domains. |
Confidence-Aware Ranker Ensembles for Robust In-Context Knowledge Editing (2026.findings-acl)
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| Challenge: | Large language models excel at factual recall, but can propagate stale or incorrect knowledge. |
| Approach: | They propose a feature-weighted ensemble for in-context knowledge editing that calibrates three heterogeneous rankers and extracts simple confidence features from each ranker. |
| Outcome: | The proposed method achieves 88.33% Edit-Success Rate over the best single retriever . it improves edit accuracy without touching model weights and approaches oracle upper bound (91%). |