WEKO3
アイテム
Conditional checkpoint selection strategy based on sentence structures for text to triple translation using BiLSTM encoder–decoder model
http://hdl.handle.net/10228/0002001336
http://hdl.handle.net/10228/0002001336969bf1cd-df0b-4884-8a6f-0b4786097434
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
C2S2_based_on_sentence_structure_for_T2T_using_BiLSTM_with_changes_highlighted.pdf (3 MB)
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| Item type | 共通アイテムタイプ(1) | |||||||||||||
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| 公開日 | 2025-02-17 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Conditional checkpoint selection strategy based on sentence structures for text to triple translation using BiLSTM encoder–decoder model | |||||||||||||
| 言語 | en | |||||||||||||
| 著者 |
Shrivastava, Manu
× Shrivastava, Manu
× Shibata, Kosei
× 我妻, 広明
WEKO
30799
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| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024 | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報 | This is a post-peer-review, pre-copyedit version of an article published in International Journal of Data Science and Analytics. The final authenticated version is available online at: https://doi.org/10.1007/s41060-024-00672-0. | |||||||||||||
| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Understanding natural languages is one of the primary goals of artificial intelligence. Natural languages contain multiple clauses and long-term dependencies making them difficult for machines to understand, also sentences can have different types of dependency structures such as simple, compound, or complex which makes interpretation further difficult. One alternative way is to represent language using predicate logic which is easier for machines to understand. The task of manually converting language to predicate logic or ontologies can be cumbersome, but it can be automated using machine translation. For these ontologies to be effective the quality of translation should be good. In this research, we focus on analyzing the effect of sentence structure on machine translation quality, i.e., how the model performance is affected if the dataset is structurally skewed meaning it has sentences of similar structure more as compared to other structures. We further investigate the model learning behavior by performing statistical analysis on features learned by these models and understand the effects of sentence structure on these learned features. The statistical analysis helps us understand the distribution followed by the features and based on the insights gained we proposed a conditional checkpoint selection strategy centered on sentence structure along with utilizing Modified J-Divergence as a loss function for optimizing model performance for different sentence structures thus achieving better translation quality. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : International Journal of Data Science and Analytics 発行日 2024-10-30 |
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| 出版社 | ||||||||||||||
| 出版者 | Springer | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | AM | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||
| DOI | ||||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1007/s41060-024-00672-0 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | PISSN | |||||||||||||
| 収録物識別子 | 2364-415X | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2364-4168 | |||||||||||||
| 査読の有無 | ||||||||||||||
| 値 | yes | |||||||||||||
| 研究者情報 | ||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/358_ja.html | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 13019 | |||||||||||||
