Papers with fine-grained representations
Understand before Answer: Improve Temporal Reading Comprehension via Precise Question Understanding (2022.naacl-main)
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| Challenge: | Temporal reading comprehension (TRC) is a natural way to study temporal relations since natural language questions are flexible to capture divergent temporal relationships. |
| Approach: | They propose a reading comprehension approach that uses precise question understanding . they embed a temporal ordering question into two vectors and evaluate the temporal relation based on that . |
| Outcome: | The proposed approach outperforms strong baselines and achieves state-of-the-art performance on the TORQUE dataset. |
Morphology-Aware Multi-Granularity Representation Learning for Agglutinative Languages (2026.acl-srw)
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| Challenge: | Existing methods for learning low-resource agglutinative languages are limited to word and phrase levels. |
| Approach: | They propose a morphology-aware gated multi-granularity pre-training framework for agglutinative languages . framework leverages morphological knowledge and integrates a word-level encoder to capture contextual semantics . |
| Outcome: | The proposed framework improves on Mongolian and Turkish agglutinative languages . it leverages morphological knowledge and integrates tagging and segmentation to build fine-grained representations . |
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations (2022.coling-1)
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Borun Chen, Hongyin Tang, Jiahao Bu, Kai Zhang, Jingang Wang, Qifan Wang, Hai-Tao Zheng, Wei Wu, Liqian Yu
| Challenge: | Pre-trained language models (PLMs) have achieved remarkable performance gains across numerous downstream tasks in natural language understanding. |
| Approach: | They propose a Chinese pre-trained language model that implicitly encodes words into characters . they propose 'contrastive learning over word' and 'character' representations to improve learning . |
| Outcome: | The proposed model can encode words into fine-grained representations without modification of production pipelines. |
Divide and Conquer Radiology Report Generation via Observation Level Fine-grained Pretraining and Prompt Tuning (2024.emnlp-main)
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| Challenge: | Recent advances in image captioning and vision-language pretraining have made it difficult for radiologists to generate coherent and accurate reports. |
| Approach: | They propose a model which breaks down full-text radiology reports into concise observation descriptions and encodes observation predictions into a decoding stage. |
| Outcome: | The proposed model achieves significant improvements across all metrics, underscoring its capability to generate semantically coherent and clinically accurate radiology reports. |
Fine-grained Medical Vision-Language Representation Learning for Radiology Report Generation (2023.emnlp-main)
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| Challenge: | Existing methods to learn medical vision-language representations by contrasting images with entire reports are not effective. |
| Approach: | They propose a phenotype-driven medical vision-language representation learning framework to bridge the gap between visual and textual modalities for improved text-oriented generation. |
| Outcome: | The proposed framework bridges the gap between visual and textual modalities for improved radiology report generation. |