Papers with word translation
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision (D18-1)
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| Challenge: | Existing methods for learning word and entity representations in monolingual settings are limited. |
| Approach: | They propose a method for joint representation learning of cross-lingual words and entities that captures mutually complementary knowledge and enables cross-linguistic inferences. |
| Outcome: | The proposed method captures mutually complementary knowledge and enables cross-lingual inferences among knowledge bases and texts. |
Characterizing Departures from Linearity in Word Translation (P18-2)
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| Challenge: | a class of methods has emerged to learn linear maps between word embedding spaces of different languages. |
| Approach: | They propose to approximate word embedding spaces using linear maps . they show that the underlying maps are non-linear but vary by a proportion of distance . |
| Outcome: | The proposed methods can be used to test non-linear methods and drive the design of more accurate maps for word translation. |
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision (N19-1)
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| Challenge: | Unsupervised BWE methods are evaluated on word translation or word similarity tasks. |
| Approach: | They propose a method that learns sentiment-specific word representations for two languages in a common space without cross-lingual supervision. |
| Outcome: | The proposed method outperforms previous unsupervised BWE methods and even supervised Bwe methods on three language pairs for cross-lingual sentiment analysis. |
Learning Unsupervised Word Translations Without Adversaries (D18-1)
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| 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. |
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion (D18-1)
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| Challenge: | Existing approaches to learn orthogonal matrix aligning bilingual lexicons are suboptimal . resulting models suffer from "hubness problem" because word vectors tend to be nearest neighbors of abnormally high number of other words. |
| Approach: | They propose a unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion. |
| Outcome: | The proposed approach outperforms the state-of-the-art on word translation on standard benchmarks. |
Media of Langue: Exploring Word Translation Network (2025.findings-naacl)
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| Challenge: | a new interface for word translation is proposed to explore the semantic configurations of words in multiple languages at once. |
| Approach: | They propose a web interface for word translation that points to the semantic configurations of many words in multiple languages at once. |
| Outcome: | The proposed interface is available as a web application on seven language pairs . it points to the semantic configurations of many words in multiple languages at once . |
Examining Gender Bias in Languages with Grammatical Gender (D19-1)
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| Challenge: | Existing studies on gender bias in word embeddings focus on English . however, these studies cannot be extended to languages with morphological agreement on gender . |
| Approach: | They propose new metrics to evaluate gender bias in word embeddings of English and Spanish . they extend existing approaches to mitigate gender bias while preserving original embeddables . |
| Outcome: | The proposed methods reduce gender bias while preserving the original embeddings. |
Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration Approach (2024.emnlp-main)
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| Challenge: | Incorrect translation of rare words can severely degrade the accuracy of ST models . |
| Approach: | They propose a retrieval-and-demonstration approach to enhance rare word translation accuracy in ST models by incorporating retrieved examples into ST models. |
| Outcome: | The proposed approach outperforms other modalities and exhibits higher robustness to unseen speakers. |
LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons (2024.findings-emnlp)
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| Challenge: | Existing word-to-word translations from labeled task data in low-resource languages have limited lexical overlap with task data. |
| Approach: | They propose a method that generates low-resource-language classification task data at scale using bilingual lexicons. |
| Outcome: | The proposed method improves on 17 low-resource languages with bilingual lexicons compared with existing models on sentiment analysis and topic classification tasks. |
Enabling Unsupervised Neural Machine Translation with Word-level Visual Representations (2023.findings-emnlp)
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| Challenge: | Unsupervised neural machine translation methods have been observed to make particular errors in comparison to supervised machine translation, such as confusing nouns that pertain to the same semantic category. |
| Approach: | They propose a method that incorporates images at the word level to augment lexical mappings. |
| Outcome: | Experiments on a multi-lingual dataset show that the proposed method generates more accurate translations with only monolingual data. |