Papers with word-based models
Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation (N18-1)
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| Challenge: | Morphologically complex words (MCWs) are multi-layer structures consisting of different subunits, each of which carries semantic information and has a specific syntactic role. |
| Approach: | They propose an extension to the state-of-the-art model which works at the character level and boosts the decoder with target-side morphological information. |
| Outcome: | The proposed model improves on the state-of-the-art model and can be extended to include morphologically complex words (MCWs) in three languages. |
Using Morphological Knowledge in Open-Vocabulary Neural Language Models (N18-1)
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| Challenge: | Existing models that generate words from a fixed vocabulary are linguistically nave . authors present an open-vocabulary language model that incorporates morphological knowledge into a neural framework . |
| Approach: | They propose a model that incorporates morphological knowledge into a neural model by generating words as a sequence of characters, generating full word forms and combining them with a hand-written morphology analyzer. |
| Outcome: | The proposed model outperforms character-based models on Finnish, Turkish, and Russian on three languages. |
Tailoring Neural Architectures for Translating from Morphologically Rich Languages (C18-1)
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| Challenge: | A morphologically complex word is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures. |
| Approach: | They propose a neural architecture which is designed to deal with morphological complexities on the source side and redesign the decoder accordingly to benefit from such information. |
| Outcome: | The proposed model outperforms existing subword- and character-based architectures and showed significant improvements on translating from German, Russian, and Turkish into English. |
Is Word Segmentation Necessary for Deep Learning of Chinese Representations? (P19-1)
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| Challenge: | Using word-based models, we compare word-oriented models with char-based ones . word-driven models are more vulnerable to data sparsity and the presence of out-of-vocabulary words . |
| Approach: | They benchmark word-based models with char-based model which does not involve word segmentation in four NLP benchmark tasks. |
| Outcome: | The proposed model outperforms char-based models in four NLP benchmark tasks. |
Extended Parallel Corpus for Amharic-English Machine Translation (2022.lrec-1)
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| Challenge: | Existing approaches to automate the complex task of translation are tedious and expensive. |
| Approach: | They describe acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. |
| Outcome: | The proposed corpus outperforms statistical machine translation models by six to seven BLEU points . the results show that the subword models outperformed word-based models by three to four BLUE points compared with the word-base models . |