ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems (N19-1)
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| Challenge: | Existing methods to regularize neural machine translation are limited in low-resource settings. |
| Approach: | They propose a method that uses regressing word embeddings to regularize neural machine translation. |
| Outcome: | The proposed system improves on a strong baseline and a state-of-the-art system. |
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Unsupervised Joint Training of Bilingual Word Embeddings (P19-1)
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| Challenge: | Existing methods for unsupervised bilingual word embeddings are limited by the dissimilarity between the word embedded spaces. |
| Approach: | They propose a method that trains unsupervised bilingual word embeddings jointly on parallel data generated through unsupervised machine translation. |
| Outcome: | The proposed method outperforms unsupervised mapped bilingual word embeddings in cross-lingual NLP tasks. |
When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation? (N18-2)
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| Challenge: | Pre-trained word embeddings have proven to be invaluable for improving performance in natural language analysis tasks where large-scale parallel corpora cannot be obtained. |
| Approach: | They perform five sets of experiments to analyze when pre-trained word embeddings can be useful in NMT tasks. |
| Outcome: | The embeddings provide gains of up to 20 BLEU points in the most favorable setting. |
Dynamic Meta-Embeddings for Improved Sentence Representations (D18-1)
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| Challenge: | A sprawling literature has emerged about what word embeddings are most useful for which tasks . word embed-ding is a technique that can be used to learn word-level meaning representations for a variety of tasks. |
| Approach: | They propose a method for supervised learning of embedding ensembles that leads to state-of-the-art performance on a variety of tasks. |
| Outcome: | The proposed method leads to state-of-the-art performance on a variety of tasks. |
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates (P18-1)
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| 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. |
Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
Neural Machine Translation with Reordering Embeddings (P19-1)
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| Challenge: | Existing work exploits the reordering information in neural machine translation . experimental results show that the proposed methods can significantly improve the performance of the transformer translation system. |
| Approach: | They propose a reordering mechanism to learn the re ordering embedding of a word based on contextual information and stack them together with self-attention networks to learn sentence representation for machine translation. |
| Outcome: | The proposed method improves translation performance on English-to-German, NIST Chinese-to English, and WAT Japanese-toEnglish translation tasks. |
Are Girls Neko or Shōjo? Cross-Lingual Alignment of Non-Isomorphic Embeddings with Iterative Normalization (P19-1)
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| Challenge: | Cross-lingual word embeddings (CLWE) are used to perform multilingual natural language processing tasks. |
| Approach: | They propose a method that transforms monolingual embeddings to make orthogonal alignment easier by simultaneously enforcing that (1) individual word vectors are unit length, and (2) each language’s average vector is zero. |
| Outcome: | The proposed method improves translation accuracy of three CLWE methods, with the largest improvement observed on English-Japanese (2% to 44% test accuracy). |
BERTSeg: BERT Based Unsupervised Subword Segmentation for Neural Machine Translation (2022.aacl-short)
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| Challenge: | Existing subword segmenters are frequency-based without semantics information or neural-based but trained on parallel corpora. |
| Approach: | They propose an unsupervised neural subword segmenter for neural machine translation that utilizes contextualized semantic embeddings of words from characterBERT and maximizes the generation probability of subword segments. |
| Outcome: | The proposed method improves translation performance on ALT, IWSLT15 Vi->En, WMT16 Ro->En and WMT15 Fi->En datasets. |
REZE: Representation Regularization for Domain-adaptive Text Embedding Pre-finetuning (2026.acl-long)
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| Challenge: | Recent text embedding models often introduce task-induced bias alongside domain knowledge, leading to performance degradation. |
| Approach: | They propose a representation regularization framework that explicitly controls representation shift during embedding pre-finetuning. |
| Outcome: | The proposed framework outperforms standard pre-finetuning and isotropy-oriented post-hoc regularization in most settings. |
The Unreasonable Effectiveness of Random Target Embeddings for Continuous-Output Neural Machine Translation (2024.naacl-short)
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| Challenge: | Continuous-output neural machine translation models are trained to predict the continuous representation based on distances between vectors. |
| Approach: | They propose a continuous-output neural machine translation (CoNMT) approach that uses random output embeddings to outperform laboriously pre-trained models. |
| Outcome: | The proposed strategy outperforms pre-trained embeddings on large datasets and is strongest for rare words due to the geometry of their embedders. |