Papers by Yasumasa Onoe
Biomedical Interpretable Entity Representations (2021.findings-acl)
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| Challenge: | Existing work on general interpretable representation learning does not transfer to biomedicine . pre-trained models induce dense entity representations but are not immediately interpretable. |
| Approach: | They propose a method that exploits BIER's final sparse and intermediate dense representations to facilitate model and entity type debugging. |
| Outcome: | The proposed model performs well on biomedical tasks including disambiguation and label classification. |
Interpretable Entity Representations through Large-Scale Typing (2020.findings-emnlp)
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| Challenge: | In traditional methods for natural language processing, entities are embedded in dense vector spaces with pre-trained models. |
| Approach: | They propose an approach to creating entity representations that are human readable and achieve high performance on entity-related tasks out of the box. |
| Outcome: | The proposed representations are vectors whose values correspond to posterior probabilities over fine-grained entity types, indicating the confidence of a typing model’s decision that the entity belongs to the corresponding type. |
Learning to Denoise Distantly-Labeled Data for Entity Typing (N19-1)
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| Challenge: | Distantly-labeled data can be used to scale up statistical models, but it is noisy . specialized probabilistic models can be employed to scale the training of models, however, they require sophisticated probabilistic inference for the training. |
| Approach: | They propose a method for denoising and denoising noisy data with supervised training. |
| Outcome: | The proposed method outperforms models trained on clean and denoised data on an ultra-fine entity typing task. |
Modeling Fine-Grained Entity Types with Box Embeddings (2021.acl-long)
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| Challenge: | Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling complex interdependencies. |
| Approach: | They propose to use box embeddings to embed types into a high-dimensional hyperrectangle space and then use it to hypothesize a type representation for the mention. |
| Outcome: | The proposed model captures latent type hierarchies better than a vector-based model on several entity typing benchmarks. |
ImageInWords: Unlocking Hyper-Detailed Image Descriptions (2024.emnlp-main)
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Roopal Garg, Andrea Burns, Burcu Karagol Ayan, Yonatan Bitton, Ceslee Montgomery, Yasumasa Onoe, Andrew Bunner, Ranjay Krishna, Jason Baldridge, Radu Soricut
| Challenge: | generating accurate hyper-detailed image descriptions is challenging for vision-language models trained on web-scraped image-text. |
| Approach: | They propose a data-centric framework for generating hyper-detailed image descriptions using web-scraped image-text. |
| Outcome: | The proposed framework improves on human evaluations on the data, even with only 9k samples. |
Entity Cloze By Date: What LMs Know About Unseen Entities (2022.findings-naacl)
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| Challenge: | Existing literature provides benchmarks to measure LMs' knowledge about entities . |
| Approach: | They propose a framework to analyze what language models can infer about new entities that did not exist when they were pretrained. |
| Outcome: | The proposed framework shows that models more informed about the entities achieve lower perplexity on this benchmark. |
Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge (2023.acl-long)
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| Challenge: | Existing methods for updating knowledge show little propagation of injected knowledge. |
| Approach: | They propose to inject individual facts into LMs and evaluate whether they can propagate injected facts while not changing predictions on other contexts. |
| Outcome: | The proposed model can make inferences based on injected facts and propagate them . existing methods show little propagation of injected knowledge . |
Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models (2023.acl-srw)
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| Challenge: | Existing datasets and analyses focusing on English do not address the need for resources in other languages. |
| Approach: | They propose a Japanese NLI benchmark focused on temporal inference . they use a set of temporal patterns to generate diverse examples . |
| Outcome: | The proposed model can perform fine-grained analysis in Japanese and English. |