| Challenge: | Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. |
| Approach: | They propose a method that aligns two words in two languages and iteratively refines the alignment. |
| Outcome: | The proposed method achieves better performance than state-of-the-art deep adversarial approaches on word translation of European and Non-European languages. |
Similar Papers
Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training (N19-1)
Copied to clipboard
| Challenge: | Recent work has shown superior performance for non-adversarial methods in more challenging language pairs. |
| Approach: | They propose to use adversarial autoencoder to map monolingual embeddings to a shared space and to put the target encoders as an adversary against the corresponding discriminator. |
| Outcome: | The proposed method is more robust and achieves better performance than previously proposed adversarial and non-adversarial methods. |
A Comparative Analysis of Unsupervised Language Adaptation Methods (D19-61)
Copied to clipboard
| Challenge: | Recent proposed approaches to perform unsupervised language adaptation lack annotated resources in less-resourced languages. |
| Approach: | They propose to use Adversarial Training, Sentence Encoder Alignment and Shared-Private Architecture to perform unsupervised language adaptation without using aligned sentences. |
| Outcome: | The proposed approaches are more suitable when the source and target language datasets contain other variations in content besides the language shift. |
Learning Unsupervised Word Translations Without Adversaries (D18-1)
Copied to clipboard
| Challenge: | Current methods for word translation are based on adversarial models and suffer from instability and hyper-parameter sensitivity. |
| Approach: | They propose a statistical dependency-based approach to bilingual dictionary induction that is unsupervised and introduces no adversary. |
| Outcome: | The proposed method outperforms adversarial alternatives and is much easier to train. |
SimAlign: High Quality Word Alignments Without Parallel Training Data Using Static and Contextualized Embeddings (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Word alignments are useful for statistical and neural machine translation (NMT) and cross-lingual annotation projection. |
| Approach: | They propose to leverage multilingual word embeddings for word alignment. |
| Outcome: | The proposed methods perform better for four languages and comparable for two languages than traditional statistical aligners even with abundant parallel data. |
Why is unsupervised alignment of English embeddings from different algorithms so hard? (D18-1)
Copied to clipboard
| Challenge: | a new paper challenges word embedding algorithms to align independent English word embeds with 100% precision . authors show that when two different embeddables are used, they fail to do so . |
| Approach: | They propose to use unsupervised bilingual dictionary induction to study English-English alignments. |
| Outcome: | The proposed approach is more of a challenge than a technical contribution . it shows that the results challenge unsupervised bilingual dictionary induction algorithms . |
Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models (P19-1)
Copied to clipboard
| Challenge: | Existing methods that map word embeddings into a common space without any parallel data or pre-training have been proposed that are limited in resources and perform poorly under resource-poor conditions. |
| Approach: | They propose a model that maps monolingual word embeddings into a common space without any parallel data and generates multilingual embeddables without any pre-training. |
| Outcome: | The proposed model outperforms existing methods on word alignment tasks on low-resource conditions and with limited resources. |
A Multilingual View of Unsupervised Machine Translation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Empirically, we show that our approach results in higher BLEU scores over state-of-the-art unsupervised models on the WMT’14 English-French, WMT'16 English-German, and WMT‘16 English–Romanian datasets in most directions. |
| Approach: | They propose a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. |
| Outcome: | The proposed framework achieves higher BLEU scores than state-of-the-art unsupervised models on the WMT’14 English-French, WMT'16 English-German, and WMT‘16 English–Romanian datasets in most directions. |
Assessing Non-autoregressive Alignment in Neural Machine Translation via Word Reordering (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing non-autoregressive neural machine translation models that implicitly model dependencies are sub-optimal in handling word order errors. |
| Approach: | They propose to learn a non-autoregressive language model that can be combined with Viterbi decoding to achieve better reordering performance. |
| Outcome: | The proposed model outperforms state-of-the-art reordering mechanisms under different word permutation settings with a 2-27 BLEU improvement, suggesting high potential for word alignment in NAT. |
Representation Alignment and Adversarial Networks for Cross-lingual Dependency Parsing (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models have improved dependency parsing accuracy in resource-rich languages . however, the accuracy drops sharply when the model is transferred to low-resource language . |
| Approach: | They propose a representation alignment and adversarial model to filter out useful knowledge from rich-resource language and ignore useless ones. |
| Outcome: | The proposed model outperforms baseline models on the benchmark datasets by 1.37 LAS and 1.34 UAS. |
Phrase-Based & Neural Unsupervised Machine Translation (D18-1)
Copied to clipboard
| Challenge: | Recent advances in machine translation have reported near human-level performance on several languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences. |
| Approach: | They propose two models that leverage a careful initialization of the parameters and denoising effect of language models. |
| Outcome: | The proposed models outperform the current methods on English-French and German-English benchmarks while being simpler and having fewer hyper-parameters. |