Papers by Joshua Tanner
MWE as WSD: Solving Multiword Expression Identification with Word Sense Disambiguation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent approaches to word sense disambiguation use encodings of the sense gloss and context information to improve performance. |
| Approach: | They propose a poly-encoder architecture which uses the sense gloss to improve WSD performance. |
| Outcome: | The proposed approach outperforms the state-of-the-art in word sense disambiguation by 1.9 F1 points and on the PARSEME 1.1 English dataset. |
Context-Informed Machine Translation of Manga using Multimodal Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Automated manga translation is a promising potential solution, but it is underdeveloped due to the need to incorporate visual elements into the translation process to resolve ambiguities. |
| Approach: | They propose a method that leverages the vision component of multimodal large language models to improve translation quality and evaluate the impact of translation unit size, context length, and propose 'token efficient' approach for manga translation. |
| Outcome: | The proposed method achieves state-of-the-art results for Japanese-English translation and sets a new standard for Japanese and Polish translation. |
CoAM: Corpus of All-Type Multiword Expressions (2025.acl-long)
Copied to clipboard
Yusuke Ide, Joshua Tanner, Adam Nohejl, Jacob Hoffman, Justin Vasselli, Hidetaka Kamigaito, Taro Watanabe
| Challenge: | Existing datasets for multiword expressions are inconsistently annotated, limited to a single type of MWE, or limited in size. |
| Approach: | They propose to use a new interface to generate MWE annotations for the first time in a dataset of MWE identification. |
| Outcome: | The proposed model outperforms existing models on the DiMSUM dataset. |