Papers with FA
Rethinking Full Finetuning from Pretraining Checkpoints in Active Learning for African Languages (2025.acl-srw)
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| Challenge: | Existing approaches to improve model performance are finetuning on all acquired data after each round, which is computationally expensive in multilingual and low-resource settings. |
| Approach: | They evaluate continual finetuning (CF) against full finetuned (FA) across 28 African languages using MasakhaNEWS and SIB-200. |
| Outcome: | The proposed approach outperforms full finetuning (FA) in 28 African languages, achieving up to 35% reductions in GPU memory, FLOPs, and training time. |
Comparing Explanation Faithfulness between Multilingual and Monolingual Fine-tuned Language Models (2024.naacl-long)
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| Challenge: | Previous studies have investigated how different factors affect faithfulness of model explanations . |
| Approach: | They find that the larger the multilingual model, the less faithful FAs are compared to its counterpart monolingual models. |
| Outcome: | The results show that the larger the multilingual model, the less faithful the FAs are compared to its counterpart monolingual models. |
Incorporating Attribution Importance for Improving Faithfulness Metrics (2023.acl-long)
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| Challenge: | Feature attribution methods (FAs) are popular for providing insights into the model reasoning process of making predictions. |
| Approach: | They propose a simple yet effective criterion that randomly masks tokens proportionately to their FA importance. |
| Outcome: | The proposed method is more faithful than hard sufficiency and comprehensiveness metrics. |
Semi-supervised Domain Adaptation for Dependency Parsing via Improved Contextualized Word Representations (2020.coling-main)
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| Challenge: | Recent advances in deep neural network models have improved parsing performance on in-domain texts . however, the problem is to improve performance on out-of-domain text data when there is only a small-scale out-domain labeled data. |
| Approach: | They propose to use adversarial learning and fine-tuning BERT to improve contextualized word representations on out-of-domain texts. |
| Outcome: | The proposed models achieve consistent improvement and fine-tune BERT processes boost parsing accuracy by a large margin. |
Tucker Decomposition with Frequency Attention for Temporal Knowledge Graph Completion (2023.findings-acl)
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| Challenge: | Existing models for temporal knowledge graph completion only consider the combination of one relation with one timestamp, ignoring the global nature of the embedding. |
| Approach: | They propose a temporal knowledge Graph Completion model that captures global temporal dependencies between one relation and the entire timestamp. |
| Outcome: | The proposed model outperforms the state-of-the-art models on three standard TKGC datasets on several metrics. |
Towards Comprehensive Description Generation from Factual Attribute-value Tables (P19-1)
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| Challenge: | Existing models for comprehensive descriptions for factual attribute-value tables might suffer from missing key attributes and groundless information problems. |
| Approach: | They propose a force attention method to encourage the generator to pay more attention to uncovered attributes to avoid potential key attributes missing. |
| Outcome: | The proposed model outperforms the state-of-the-art baselines on automatic and human evaluation. |
LLM-ForcedAligner: A Non-Autoregressive and Accurate LLM-Based Forced Aligner for Multilingual and Long-Form Speech (2026.acl-long)
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| Challenge: | Existing methods for forcing alignment are language-specific and prone to temporal shifts. |
| Approach: | They propose a slot-filling paradigm that uses time indices to predict slot positions. |
| Outcome: | The proposed method reduces accumulated temporal shifts by 69% compared with prior methods. |