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Journal articles on the topic 'Bilingual lexicon induction'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Bai, Xuefeng, Hailong Cao, Kehai Chen, and Tiejun Zhao. "A Bilingual Adversarial Autoencoder for Unsupervised Bilingual Lexicon Induction." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 10 (2019): 1639–48. http://dx.doi.org/10.1109/taslp.2019.2925973.

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2

Irvine, Ann, and Chris Callison-Burch. "A Comprehensive Analysis of Bilingual Lexicon Induction." Computational Linguistics 43, no. 2 (2017): 273–310. http://dx.doi.org/10.1162/coli_a_00284.

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Bilingual lexicon induction is the task of inducing word translations from monolingual corpora in two languages. In this article we present the most comprehensive analysis of bilingual lexicon induction to date. We present experiments on a wide range of languages and data sizes. We examine translation into English from 25 foreign languages: Albanian, Azeri, Bengali, Bosnian, Bulgarian, Cebuano, Gujarati, Hindi, Hungarian, Indonesian, Latvian, Nepali, Romanian, Serbian, Slovak, Somali, Spanish, Swedish, Tamil, Telugu, Turkish, Ukrainian, Uzbek, Vietnamese, and Welsh. We analyze the behavior of

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3

Kim, Jae-Hoon, Hyeong-Won Seo, and Hong-Seok Kwon. "Bilingual lexicon induction through a pivot language." Journal of the Korean Society of Marine Engineering 37, no. 3 (2013): 300–306. http://dx.doi.org/10.5916/jkosme.2013.37.3.300.

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4

ZHANG, Meng, Yang LIU, and Maosong SUN. "Bilingual lexicon induction from non-parallel corpora." SCIENTIA SINICA Informationis 48, no. 5 (2018): 564–73. http://dx.doi.org/10.1360/n112017-00256.

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5

IRVINE, ANN, and CHRIS CALLISON-BURCH. "End-to-end statistical machine translation with zero or small parallel texts." Natural Language Engineering 22, no. 4 (2016): 517–48. http://dx.doi.org/10.1017/s1351324916000127.

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AbstractWe use bilingual lexicon induction techniques, which learn translations from monolingual texts in two languages, to build an end-to-end statistical machine translation (SMT) system without the use of any bilingual sentence-aligned parallel corpora. We present detailed analysis of the accuracy of bilingual lexicon induction, and show how a discriminative model can be used to combine various signals of translation equivalence (like contextual similarity, temporal similarity, orthographic similarity and topic similarity). Our discriminative model produces higher accuracy translations than

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6

Nasution, Arbi Haza, Yohei Murakami, and Toru Ishida. "Plan Optimization to Bilingual Dictionary Induction for Low-resource Language Families." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 2 (2021): 1–28. http://dx.doi.org/10.1145/3448215.

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Creating bilingual dictionary is the first crucial step in enriching low-resource languages. Especially for the closely related ones, it has been shown that the constraint-based approach is useful for inducing bilingual lexicons from two bilingual dictionaries via the pivot language. However, if there are no available machine-readable dictionaries as input, we need to consider manual creation by bilingual native speakers. To reach a goal of comprehensively create multiple bilingual dictionaries, even if we already have several existing machine-readable bilingual dictionaries, it is still diffi

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7

Chen, Shizhe, Qin Jin, and Alexander Hauptmann. "Unsupervised Bilingual Lexicon Induction from Mono-Lingual Multimodal Data." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8207–14. http://dx.doi.org/10.1609/aaai.v33i01.33018207.

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Bilingual lexicon induction, translating words from the source language to the target language, is a long-standing natural language processing task. Recent endeavors prove that it is promising to employ images as pivot to learn the lexicon induction without reliance on parallel corpora. However, these vision-based approaches simply associate words with entire images, which are constrained to translate concrete words and require object-centered images. We humans can understand words better when they are within a sentence with context. Therefore, in this paper, we propose to utilize images and t

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8

Sholikah, Rizka, Yasuhiko Morimoto, Agus Arifin, Chastine Fatichah, and Ayu Purwarianti. "Exploiting Comparable Corpora to Enhance Bilingual Lexicon Induction from Monolingual Corpora." International Journal of Intelligent Engineering and Systems 13, no. 5 (2020): 379–92. http://dx.doi.org/10.22266/ijies2020.1031.34.

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9

Song, Yuting, Biligsaikhan Batjargal, and Akira Maeda. "Learning Japanese-English Bilingual Word Embeddings by Using Language Specificity." International Journal of Asian Language Processing 30, no. 03 (2020): 2050014. http://dx.doi.org/10.1142/s2717554520500149.

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Cross-lingual word embeddings have been gaining attention because they can capture the semantic meaning of words across languages, which can be applied to cross-lingual tasks. Most methods learn a single mapping (e.g., a linear mapping) to transform a word embedding space from one language to another. To improve bilingual word embeddings, we propose an advanced method that adds a language-specific mapping. We focus on learning Japanese-English bilingual word embedding mapping by considering the specificity of the Japanese language. We evaluated our method by comparing it with single mapping-ba

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10

Jawanpuria, Pratik, Arjun Balgovind, Anoop Kunchukuttan, and Bamdev Mishra. "Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach." Transactions of the Association for Computational Linguistics 7 (November 2019): 107–20. http://dx.doi.org/10.1162/tacl_a_00257.

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We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation into (a) language-specific rotations on the original embeddings to align them in a common, latent space, and (b) a language-independent similarity metric in this common space to better model the similarity between the embeddings. Overall, we pose the bilingual mapping problem as a classification problem on smooth Riemannian manifolds. Empirically, our approach outperforms previous approaches on the bil

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11

Marie, Benjamin, and Atsushi Fujita. "Phrase Table Induction Using In-Domain Monolingual Data for Domain Adaptation in Statistical Machine Translation." Transactions of the Association for Computational Linguistics 5 (December 2017): 487–500. http://dx.doi.org/10.1162/tacl_a_00075.

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We present a new framework to induce an in-domain phrase table from in-domain monolingual data that can be used to adapt a general-domain statistical machine translation system to the targeted domain. Our method first compiles sets of phrases in source and target languages separately and generates candidate phrase pairs by taking the Cartesian product of the two phrase sets. It then computes inexpensive features for each candidate phrase pair and filters them using a supervised classifier in order to induce an in-domain phrase table. We experimented on the language pair English–French, both tr

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12

Church, Kenneth Ward. "Benchmarks and goals." Natural Language Engineering 26, no. 5 (2020): 579–92. http://dx.doi.org/10.1017/s1351324920000418.

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AbstractBenchmarks can be a useful step toward the goals of the field (when the benchmark is on the critical path), as demonstrated by the GLUE benchmark, and deep nets such as BERT and ERNIE. The case for other benchmarks such as MUSE and WN18RR is less well established. Hopefully, these benchmarks are on a critical path toward progress on bilingual lexicon induction (BLI) and knowledge graph completion (KGC). Many KGC algorithms have been proposed such as Trans[DEHRM], but it remains to be seen how this work improves WordNet coverage. Given how much work is based on these benchmarks, the lit

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13

PUREZA, RITA, ANA PAULA SOARES, and MONTSERRAT COMESAÑA. "Cognate status, syllable position and word length on bilingual Tip-Of-the-Tongue states induction and resolution." Bilingualism: Language and Cognition 19, no. 3 (2015): 533–49. http://dx.doi.org/10.1017/s1366728915000206.

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This study explores the role of cognate status, syllable position, and word length in Tip-of-the-Tongue (TOT) states induction and resolution for European Portuguese (EP; L1) – English (L2) bilinguals (and EP monolinguals as control). TOTs were induced using a picture naming task in L1 and L2 followed by a lexical decision task. Here, the first or the last syllable of the target word (or none for control) was embedded in pseudowords (syllabic pseudohomophones) in order to test its effect in TOT resolution. Bilinguals presented more TOTs in L2 than in L1, especially for noncognate words. Longer

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14

Heyman, Geert, Ivan Vulić, and Marie-Francine Moens. "A deep learning approach to bilingual lexicon induction in the biomedical domain." BMC Bioinformatics 19, no. 1 (2018). http://dx.doi.org/10.1186/s12859-018-2245-8.

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15

Batsuren, Khuyagbaatar, Gábor Bella, and Fausto Giunchiglia. "A large and evolving cognate database." Language Resources and Evaluation, May 30, 2021. http://dx.doi.org/10.1007/s10579-021-09544-6.

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AbstractWe present CogNet, a large-scale, automatically-built database of sense-tagged cognates—words of common origin and meaning across languages. CogNet is continuously evolving: its current version contains over 8 million cognate pairs over 338 languages and 35 writing systems, with new releases already in preparation. The paper presents the algorithm and input resources used for its computation, an evaluation of the result, as well as a quantitative analysis of cognate data leading to novel insights on language diversity. Furthermore, as an example on the use of large-scale cross-lingual

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16

Chakravarthi, Bharathi Raja, Priya Rani, Mihael Arcan, and John P. McCrae. "A Survey of Orthographic Information in Machine Translation." SN Computer Science 2, no. 4 (2021). http://dx.doi.org/10.1007/s42979-021-00723-4.

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AbstractMachine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the linguistic difference and variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable but orthographic information can also be used to improve

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17

Quick, Antje Endesfelder, and Stefan Hartmann. "The Building Blocks of Child Bilingual Code-Mixing: A Cross-Corpus Traceback Approach." Frontiers in Psychology 12 (July 27, 2021). http://dx.doi.org/10.3389/fpsyg.2021.682838.

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This paper offers an inductive, exploratory study on the role of input and individual differences in the early code-mixing of bilingual children. Drawing on data from two German-English bilingual children, aged 2–4, we use the traceback method to check whether their code-mixed utterances can be accounted for with the help of constructional patterns that can be found in their monolingual data and/or in their caregivers' input. In addition, we apply the traceback method to check whether the patterns used by one child can also be found in the input of the other child. Results show that patterns f

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