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. |
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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. |
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| Challenge: | Using multilingual and multi-way neural machine translation approaches is a major advantage . training NMT systems for individual language pairs takes significantly more time than training of SMT systems . |
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| Challenge: | Existing methods for character-based and sub-word tokenization are limited to the surface forms of the words. |
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| Challenge: | Neural sequence-to-sequence models have proven effective for machine translation, but at the expense of interpretability. |
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| Challenge: | Using subwords, we find that the appropriate subword units for the three layers differ depending on the model . incorporating hierarchical subword features improves BLEU scores on the IWSLT evaluation datasets. |
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A Truly Joint Neural Architecture for Segmentation and Parsing (2024.eacl-long)
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| Challenge: | Contemporary multilingual dependency parsers can parse a diverse set of languages, but performance is lower for Morphologically Rich Languages. |
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| Challenge: | a decade has passed since the establishment of SPMRL to address the peculiar challenges of Statistical Parsing for Morphologically-rich languages (MRLs). |
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| Challenge: | Neural Machine Translation models typically use a fixed-size lexical vocabulary . subword segmentation methods rely on statistical heuristics that lack any linguistic notion . |
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Evaluating Pre-training Objectives for Low-Resource Translation into Morphologically Rich Languages (2022.lrec-1)
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| Challenge: | a lack of parallel data is a major limitation for Neural Machine Translation systems, especially for morphologically rich languages. |
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