Modeling Word Formation in English–German Neural Machine Translation (2020.acl-main)
Copied to clipboard
| Challenge: | linguistically sound word segmentation approaches overcome word formation problems . word-level approaches to MT lack morphological generalization for large vocabulary . |
| Approach: | They propose a word segmentation approach that considers fusional morphology to model word formation . they apply a linguistically sound segmentation method to both the source and target sides . |
| Outcome: | The proposed approach overcomes the problems caused by fusional morphology . the best system variants employ source-side morphological analysis and model complex target-side words . |
Similar Papers
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (P18-1)
Copied to clipboard
| Challenge: | Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation. |
| Approach: | They propose a method to regularize subword segmentations probabilistically by sampling subwords . they also propose 'unigram' language model to be used for better subword sampling . |
| Outcome: | The proposed method improves on low resource and out-of-domain settings with multiple corpora. |
Compositional Representation of Morphologically-Rich Input for Neural Machine Translation (P18-2)
Copied to clipboard
| Challenge: | Neural machine translation models are typically trained with fixed-size input and output vocabularies, which creates a bottleneck on their accuracy and generalization capability. |
| Approach: | They propose to replace the source-language embedding layer of NMT with a bi-directional recurrent neural network that generates compositional representations of the input at any desired level of granularity. |
| Outcome: | The proposed approach outperforms existing methods in a low-resource setting with five languages . the proposed approach consistently outperformed existing methods with a single word representation . |
Improving Character-Based Decoding Using Target-Side Morphological Information for Neural Machine Translation (N18-1)
Copied to clipboard
| 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. |
On the Importance of Word Boundaries in Character-level Neural Machine Translation (D19-56)
Copied to clipboard
| Challenge: | Neural Machine Translation models typically use a fixed-size lexical vocabulary . subword segmentation methods rely on statistical heuristics that lack any linguistic notion . |
| Approach: | They propose a hierarchical decoding architecture for character-level NMT using subwords . they propose fewer parameters and a more efficient approach to perform translation at the level of words . |
| Outcome: | The proposed model can reach higher translation accuracy than the subword-level model with fewer parameters while maintaining longer-distance contextual and grammatical dependencies. |
One Size Does Not Fit All: Comparing NMT Representations of Different Granularities (N19-1)
Copied to clipboard
| Challenge: | Recent work has shown that contextualized word representations are a viable alternative to simple word prediction tasks. |
| Approach: | They propose to use subword units and characters to model morphology, syntax, and semantics instead of word embeddings. |
| Outcome: | The proposed representations are better for modeling syntax and more robust to noisy input. |
Low-resource neural machine translation with morphological modeling (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for character-based and sub-word tokenization are limited to the surface forms of the words. |
| Approach: | They propose a framework-solution for modeling complex morphology in low-resource settings using a transformer architecture and beam search-based decoder. |
| Outcome: | The proposed model improves translation performance on Kinyarwanda English translation using public-domain parallel text. |
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation (2021.acl-short)
Copied to clipboard
| Challenge: | Subword segmentation algorithms can produce sub-optimal segmentation when the target language is rich in morphological changes or there is not enough data for learning compact composition rules. |
| Approach: | They compare character-based and subword-based neural machine translation systems . they find character-driven models are better at handling morphological phenomena . |
| Outcome: | The character-based models are better at handling morphological phenomena, generating rare and unknown words, and more suitable for transferring to unseen domains. |
How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology? (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Data-driven subword segmentation is the default strategy for open-vocabulary machine translation but may not be sufficiently generic for learning non-concatenative morphology. |
| Approach: | They propose to test data-driven subword segmentation on non-concatenative morphological phenomena in a controlled, semi-synthetic setting. |
| Outcome: | The proposed model can translate non-concatenative morphological phenomena in a controlled, semi-synthetic setting. |
On the Word Alignment from Neural Machine Translation (P19-1)
Copied to clipboard
| Challenge: | Prior researches suggest that neural machine translation (NMT) captures word alignment through its attention mechanism, however, attention may fail to capture word alignment for some NMT models. |
| Approach: | They propose two methods to induce word alignment which are general and agnostic to specific NMT models. |
| Outcome: | The proposed methods induce much better word alignment than attention. |
Improving Neural Machine Translation by Incorporating Hierarchical Subword Features (C18-1)
Copied to clipboard
| 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. |
| Approach: | They propose a method that expresses a word by combining "subwords" they propose to incorporate hierarchical subword features into a single embedding layer . |
| Outcome: | The proposed method improves BLEU scores on the IWSLT evaluation datasets. |