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Thematische Bibliographien / Cross-Lingual knowledge transfer / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Cross-Lingual knowledge transfer“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 31. Juli 2025

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Wang, Yabing, Fan Wang, Jianfeng Dong, and Hao Luo. "CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5651–59. http://dx.doi.org/10.1609/aaai.v38i6.28376.

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Cross-lingual cross-modal retrieval has garnered increasing attention recently, which aims to achieve the alignment between vision and target language (V-T) without using any annotated V-T data pairs. Current methods employ machine translation (MT) to construct pseudo-parallel data pairs, which are then used to learn a multi-lingual and multi-modal embedding space that aligns visual and target-language representations. However, the large heterogeneous gap between vision and text, along with the noise present in target language translations, poses significant challenges in effectively aligning

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Chai, Linzheng, Jian Yang, Tao Sun, et al. "XCOT: Cross-lingual Instruction Tuning for Cross-lingual Chain-of-Thought Reasoning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 22 (2025): 23550–58. https://doi.org/10.1609/aaai.v39i22.34524.

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Chain-of-thought (CoT) has emerged as a powerful technique to elicit reasoning in large language models and improve a variety of downstream tasks. CoT mainly demonstrates excellent performance in English, but its usage in low-resource languages is constrained due to poor language generalization. To bridge the gap among different languages, we propose a cross-lingual instruction fine-tuning framework (xCoT) to transfer knowledge from high-resource languages to low-resource languages. Specifically, the multilingual instruction training data (xCoT-Instruct) is created to encourage the semantic al

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Abhishek Singhal, Happa Khan, Aditya Sharma. "Empowering Multilingual AI: Cross-Lingual Transfer Learning." Tuijin Jishu/Journal of Propulsion Technology 43, no. 4 (2023): 284–87. http://dx.doi.org/10.52783/tjjpt.v43.i4.2353.

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Multilingual Natural Language Processing (NLP) and Cross-Lingual Transfer Learning have emerged as pivotal fields in the realm of language technology. This abstract explores the essential concepts and methodologies behind these areas, shedding light on their significance in a world characterized by linguistic diversity. Multilingual NLP enables machines to process global collaboration. Cross-lingual transfer learning, on the other hand, leverages knowledge from one language to enhance NLP tasks in another, facilitating efficient resource utilization and improved model performance. The abstract

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Al-Duwais, Mashael, Hend Al-Khalifa, and Abdulmalik Al-Salman. "A Benchmark Evaluation of Multilingual Large Language Models for Arabic Cross-Lingual Named-Entity Recognition." Electronics 13, no. 17 (2024): 3574. http://dx.doi.org/10.3390/electronics13173574.

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Multilingual large language models (MLLMs) have demonstrated remarkable performance across a wide range of cross-lingual Natural Language Processing (NLP) tasks. The emergence of MLLMs made it possible to achieve knowledge transfer from high-resource to low-resource languages. Several MLLMs have been released for cross-lingual transfer tasks. However, no systematic evaluation comparing all models for Arabic cross-lingual Named-Entity Recognition (NER) is available. This paper presents a benchmark evaluation to empirically investigate the performance of the state-of-the-art multilingual large l

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Zhang, Mozhi, Yoshinari Fujinuma, and Jordan Boyd-Graber. "Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9547–54. http://dx.doi.org/10.1609/aaai.v34i05.6500.

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Text classification must sometimes be applied in a low-resource language with no labeled training data. However, training data may be available in a related language. We investigate whether character-level knowledge transfer from a related language helps text classification. We present a cross-lingual document classification framework (caco) that exploits cross-lingual subword similarity by jointly training a character-based embedder and a word-based classifier. The embedder derives vector representations for input words from their written forms, and the classifier makes predictions based on t

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Colhon, Mihaela. "Language engineering for syntactic knowledge transfer." Computer Science and Information Systems 9, no. 3 (2012): 1231–47. http://dx.doi.org/10.2298/csis120130032c.

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In this paper we present a method for an English-Romanian treebank construction, together with the obtained evaluation results. The treebank is built upon a parallel English-Romanian corpus word-aligned and annotated at the morphological and syntactic level. The syntactic trees of the Romanian texts are generated by considering the syntactic phrases of the English parallel texts automatically resulted from syntactic parsing. The method reuses and adjusts existing tools and algorithms for cross-lingual transfer of syntactic constituents and syntactic trees alignment.

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Zhan, Qingran, Xiang Xie, Chenguang Hu, Juan Zuluaga-Gomez, Jing Wang, and Haobo Cheng. "Domain-Adversarial Based Model with Phonological Knowledge for Cross-Lingual Speech Recognition." Electronics 10, no. 24 (2021): 3172. http://dx.doi.org/10.3390/electronics10243172.

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Phonological-based features (articulatory features, AFs) describe the movements of the vocal organ which are shared across languages. This paper investigates a domain-adversarial neural network (DANN) to extract reliable AFs, and different multi-stream techniques are used for cross-lingual speech recognition. First, a novel universal phonological attributes definition is proposed for Mandarin, English, German and French. Then a DANN-based AFs detector is trained using source languages (English, German and French). When doing the cross-lingual speech recognition, the AFs detectors are used to t

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Xu, Zenan, Linjun Shou, Jian Pei, et al. "A Graph Fusion Approach for Cross-Lingual Machine Reading Comprehension." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13861–68. http://dx.doi.org/10.1609/aaai.v37i11.26623.

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Although great progress has been made for Machine Reading Comprehension (MRC) in English, scaling out to a large number of languages remains a huge challenge due to the lack of large amounts of annotated training data in non-English languages. To address this challenge, some recent efforts of cross-lingual MRC employ machine translation to transfer knowledge from English to other languages, through either explicit alignment or implicit attention. For effective knowledge transition, it is beneficial to leverage both semantic and syntactic information. However, the existing methods fail to expli

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Li, Xun, and Kun Zhang. "Contrastive Learning Pre-Training and Quantum Theory for Cross-Lingual Aspect-Based Sentiment Analysis." Entropy 27, no. 7 (2025): 713. https://doi.org/10.3390/e27070713.

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The cross-lingual aspect-based sentiment analysis (ABSA) task continues to pose a significant challenge, as it involves training a classifier on high-resource source languages and then applying it to classify texts in low-resource target languages, thereby bridging linguistic gaps while preserving accuracy. Most existing methods achieve exceptional performance by relying on multilingual pre-trained language models (mPLM) and translation systems to transfer knowledge across languages. However, little attention has been paid to factors beyond semantic similarity, which ultimately hinders classif

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Rijhwani, Shruti, Jiateng Xie, Graham Neubig, and Jaime Carbonell. "Zero-Shot Neural Transfer for Cross-Lingual Entity Linking." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6924–31. http://dx.doi.org/10.1609/aaai.v33i01.33016924.

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Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While previous work relies heavily on bilingual lexical resources to bridge the gap between the source and the target languages, these resources are scarce or unavailable for many low-resource languages. To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource language. Specifically, we propose pivot-basedentity

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Qi, Kunxun, and Jianfeng Du. "Translation-Based Matching Adversarial Network for Cross-Lingual Natural Language Inference." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8632–39. http://dx.doi.org/10.1609/aaai.v34i05.6387.

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Cross-lingual natural language inference is a fundamental task in cross-lingual natural language understanding, widely addressed by neural models recently. Existing neural model based methods either align sentence embeddings between source and target languages, heavily relying on annotated parallel corpora, or exploit pre-trained cross-lingual language models that are fine-tuned on a single language and hard to transfer knowledge to another language. To resolve these limitations in existing methods, this paper proposes an adversarial training framework to enhance both pre-trained models and cl

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Bari, M. Saiful, Shafiq Joty, and Prathyusha Jwalapuram. "Zero-Resource Cross-Lingual Named Entity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7415–23. http://dx.doi.org/10.1609/aaai.v34i05.6237.

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Recently, neural methods have achieved state-of-the-art (SOTA) results in Named Entity Recognition (NER) tasks for many languages without the need for manually crafted features. However, these models still require manually annotated training data, which is not available for many languages. In this paper, we propose an unsupervised cross-lingual NER model that can transfer NER knowledge from one language to another in a completely unsupervised way without relying on any bilingual dictionary or parallel data. Our model achieves this through word-level adversarial learning and augmented fine-tuni

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Zhang, Weizhao, and Hongwu Yang. "Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis." Applied Sciences 12, no. 23 (2022): 12185. http://dx.doi.org/10.3390/app122312185.

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The paper proposes a meta-learning-based Mandarin-Tibetan cross-lingual text-to-speech (TTS) to realize both Mandarin and Tibetan speech synthesis under a unique framework. First, we build two kinds of Tacotron2-based Mandarin-Tibetan cross-lingual baseline TTS. One is a shared encoder Mandarin-Tibetan cross-lingual TTS, and another is a separate encoder Mandarin-Tibetan cross-lingual TTS. Both baseline TTS use the speaker classifier with a gradient reversal layer to disentangle speaker-specific information from the text encoder. At the same time, we design a prosody generator to extract proso

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Li, Zhuoran, Chunming Hu, Junfan Chen, Zhijun Chen, and Richong Zhang. "Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24530–38. https://doi.org/10.1609/aaai.v39i23.34632.

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Word order difference between source and target languages is a major obstacle to cross-lingual transfer, especially in the dependency parsing task. Current works are mostly based on order-agnostic models or word reordering to mitigate this problem. However, such methods either do not leverage grammatical information naturally contained in word order or are computationally expensive as the permutation space grows exponentially with the sentence length. Moreover, the reordered source sentence with an unnatural word order may be a form of noising that harms the model learning. To this end, we pro

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Kalluri, Kartheek. "ADAPTING LLMs FOR LOW RESOURCE LANGUAGES-TECHNIQUES AND ETHICAL CONSIDERATIONS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/isjem00140.

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Adaptive large language models (LLMs) to resource-scarce languages and also analyze the ethical considerations involved. Already incorporated the elements of mixed methods. It consists of a literature review, corpus collection, expert interviews, and shareholders meeting. Some adaptation techniques examined in this study are data augmentation, multilingual pre-training, change of architecture, and parameter-efficient fine-tuning. The quantitative analysis indicated model performance improvements for under-resourced languages, particularly through cross-lingual knowledge transfer and data augme

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Aswin Babu Rasipuram Sivaselvan. "Cross-Lingual Transfer Learning for Low-Resource Languages: Expanding NLP’s Global Reach." Indian Engineering Journal 3, no. 1 (2025): 28–39. https://doi.org/10.52783/iej.12.

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The fast evolution of move-lingual switch gaining knowledge of (XLT) has introduced great advancements in herbal language processing (NLP), in particular in addressing linguistic disparities for low-aid languages. Notwithstanding those developments, critical demanding situations persist, inclusive of confined annotated corpora, structural linguistic variety, and inherent biases in multilingual models. Present tactics regularly desire excessive-useful resource languages, leading to suboptimal overall performance in low-resource settings. To bridge this gap, we introduce a novel go-lingual switc

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Zhang, Jingyi, Bocheng Zhao, Wenxing Zhang, and Qiguang Miao. "Knowledge Translator: Cross-Lingual Course Video Text Style Transform via Imposed Sequential Attention Networks." Electronics 14, no. 6 (2025): 1213. https://doi.org/10.3390/electronics14061213.

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Massive Online Open Courses (MOOCs) have been growing rapidly in the past few years. Video content is an important carrier for cultural exchange and education popularization, and needs to be translated into multiple language versions to meet the needs of learners from different countries and regions. However, current MOOC video processing solutions rely excessively on manual operations, resulting in low efficiency and difficulty in meeting the urgent requirement for large-scale content translation. Key technical challenges include the accurate localization of embedded text in complex video fra

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Chen, Xilun, Yu Sun, Ben Athiwaratkun, Claire Cardie, and Kilian Weinberger. "Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification." Transactions of the Association for Computational Linguistics 6 (December 2018): 557–70. http://dx.doi.org/10.1162/tacl_a_00039.

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In recent years great success has been achieved in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most languages do not enjoy such an abundance of labeled data. To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN 1 ) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exist. ADAN has two discriminative branches: a sentiment class

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Fang, Yuwei, Shuohang Wang, Zhe Gan, Siqi Sun, and Jingjing Liu. "FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12776–84. http://dx.doi.org/10.1609/aaai.v35i14.17512.

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Large-scale cross-lingual language models (LM), such as mBERT, Unicoder and XLM, have achieved great success in cross-lingual representation learning. However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual alignment between different languages that proves essential for multilingual tasks. In this paper, we propose FILTER, an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. Specifically, FILTER first encodes text input in the source la

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Li, Xun, and Kun Zhang. "Heterogeneous Graph Neural Network with Multi-View Contrastive Learning for Cross-Lingual Text Classification." Applied Sciences 15, no. 7 (2025): 3454. https://doi.org/10.3390/app15073454.

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The cross-lingual text classification task remains a long-standing challenge that aims to train a classifier on high-resource source languages and apply it to classify texts in low-resource target languages, bridging linguistic gaps while maintaining accuracy. Most existing methods achieve exceptional performance by relying on multilingual pretrained language models to transfer knowledge across languages. However, little attention has been paid to factors beyond semantic similarity, which leads to the degradation of classification performance in the target languages. This study proposes a nove

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Wang, Mengqiu, and Christopher D. Manning. "Cross-lingual Projected Expectation Regularization for Weakly Supervised Learning." Transactions of the Association for Computational Linguistics 2 (December 2014): 55–66. http://dx.doi.org/10.1162/tacl_a_00165.

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We consider a multilingual weakly supervised learning scenario where knowledge from annotated corpora in a resource-rich language is transferred via bitext to guide the learning in other languages. Past approaches project labels across bitext and use them as features or gold labels for training. We propose a new method that projects model expectations rather than labels, which facilities transfer of model uncertainty across language boundaries. We encode expectations as constraints and train a discriminative CRF model using Generalized Expectation Criteria (Mann and McCallum, 2010). Evaluated

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Zhang, Weitai, Lirong Dai, Junhua Liu, and Shijin Wang. "Improving Many-to-Many Neural Machine Translation via Selective and Aligned Online Data Augmentation." Applied Sciences 13, no. 6 (2023): 3946. http://dx.doi.org/10.3390/app13063946.

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Multilingual neural machine translation (MNMT) models are theoretically attractive for low- and zero-resource language pairs with the impact of cross-lingual knowledge transfer. Existing approaches mainly focus on English-centric directions and always underperform compared to their pivot-based counterparts for non-English directions. In this work, we aim to build a many-to-many MNMT system with an emphasis on the quality of non-English directions by exploring selective and aligned online data augmentation algorithms. Based on our findings showing that the augmented synthetic samples are not “t

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Ge, Ling, Chunming Hu, Guanghui Ma, Jihong Liu, and Hong Zhang. "Discrepancy and Uncertainty Aware Denoising Knowledge Distillation for Zero-Shot Cross-Lingual Named Entity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 18056–64. http://dx.doi.org/10.1609/aaai.v38i16.29762.

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The knowledge distillation-based approaches have recently yielded state-of-the-art (SOTA) results for cross-lingual NER tasks in zero-shot scenarios. These approaches typically employ a teacher network trained with the labelled source (rich-resource) language to infer pseudo-soft labels for the unlabelled target (zero-shot) language, and force a student network to approximate these pseudo labels to achieve knowledge transfer. However, previous works have rarely discussed the issue of pseudo-label noise caused by the source-target language gap, which can mislead the training of the student netw

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Wu, Tianxing, Chaoyu Gao, Lin Li, and Yuxiang Wang. "Leveraging Multi-Modal Information for Cross-Lingual Entity Matching across Knowledge Graphs." Applied Sciences 12, no. 19 (2022): 10107. http://dx.doi.org/10.3390/app121910107.

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In recent years, the scale of knowledge graphs and the number of entities have grown rapidly. Entity matching across different knowledge graphs has become an urgent problem to be solved for knowledge fusion. With the importance of entity matching being increasingly evident, the use of representation learning technologies to find matched entities has attracted extensive attention due to the computability of vector representations. However, existing studies on representation learning technologies cannot make full use of knowledge graph relevant multi-modal information. In this paper, we propose

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Alhanai, Tuka, Adam Kasumovic, Mohammad M. Ghassemi, Aven Zitzelberger, Jessica M. Lundin, and Guillaume Chabot-Couture. "Bridging the Gap: Enhancing LLM Performance for Low-Resource African Languages with New Benchmarks, Fine-Tuning, and Cultural Adjustments." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 27 (2025): 27802–12. https://doi.org/10.1609/aaai.v39i27.34996.

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Large Language Models (LLMs) have shown remarkable performance across various tasks, yet significant disparities remain for non-English languages, and especially native African languages. This paper addresses these disparities by creating approximately 1 million human-translated words of new benchmark data in 8 low-resource African languages, covering a population of over 160 million speakers of: Amharic, Bambara, Igbo, Sepedi (Northern Sotho), Shona, Sesotho (Southern Sotho), Setswana, and Tsonga. Our benchmarks are translations of Winogrande and three sections of MMLU: college medicine, clin

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Cui, Ruixiang, Rahul Aralikatte, Heather Lent, and Daniel Hershcovich. "Compositional Generalization in Multilingual Semantic Parsing over Wikidata." Transactions of the Association for Computational Linguistics 10 (2022): 937–55. http://dx.doi.org/10.1162/tacl_a_00499.

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Abstract Semantic parsing (SP) allows humans to leverage vast knowledge resources through natural interaction. However, parsers are mostly designed for and evaluated on English resources, such as CFQ (Keysers et al., 2020), the current standard benchmark based on English data generated from grammar rules and oriented towards Freebase, an outdated knowledge base. We propose a method for creating a multilingual, parallel dataset of question-query pairs, grounded in Wikidata. We introduce such a dataset, which we call Multilingual Compositional Wikidata Questions (MCWQ), and use it to analyze the

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He, Keqing, Weiran Xu, and Yuanmeng Yan. "Multi-Level Cross-Lingual Transfer Learning With Language Shared and Specific Knowledge for Spoken Language Understanding." IEEE Access 8 (2020): 29407–16. http://dx.doi.org/10.1109/access.2020.2972925.

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Cheng, Shaohuan, Wenyu Chen, Yujia Tang, Mingsheng Fu, and Hong Qu. "Unified Training for Cross-Lingual Abstractive Summarization by Aligning Parallel Machine Translation Pairs." Mathematics 12, no. 13 (2024): 2107. http://dx.doi.org/10.3390/math12132107.

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Cross-lingual summarization (CLS) is essential for enhancing global communication by facilitating efficient information exchange across different languages. However, owing to the scarcity of CLS data, recent studies have employed multi-task frameworks to combine parallel monolingual summaries. These methods often use independent decoders or models with non-shared parameters because of the mismatch in output languages, which limits the transfer of knowledge between CLS and its parallel data. To address this issue, we propose a unified training method for CLS that combines parallel machine trans

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Eleanor, Hughes, Ward Nathaniel, Bennett Clara, Grant Oliver, and Turner Sophie. "Cross-Lingual Instruction Alignment in Large Language Models via Lightweight Prompt Distillation." International Journal of Advance in Applied Science Research 4, no. 4 (2025): 9–16. https://doi.org/10.5281/zenodo.15232962.

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<em>With the continued expansion of large language models in multilingual tasks, achieving efficient and robust instruction alignment has become a key technical challenge in the field of natural language processing. This study proposes a lightweight instruction fine-tuning framework that combines cross-lingual transfer learning with a hierarchical prompt distillation strategy. The framework first performs initial optimization on the model using high-quality English instruction data. Then, through a carefully designed hierarchical prompt structure, knowledge is distilled and transferred to mode

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Du, Yangkai, Tengfei Ma, Lingfei Wu, Xuhong Zhang, and Shouling Ji. "AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 17942–50. http://dx.doi.org/10.1609/aaai.v38i16.29749.

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Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention. Modern software often involves a diverse range of programming languages. However, current code clone detection methods are generally limited to only a few popular programming languages due to insufficient annotated data as well as their own model design constraints. To address these issues, we present AdaCCD, a novel cross-lingual adaptation method that can detect cloned codes in a new language without annotations in that language. AdaCCD leverages languag

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Ge, Ling, Chunming Hu, Guanghui Ma, Hong Zhang, and Jihong Liu. "ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12818–26. http://dx.doi.org/10.1609/aaai.v37i11.26507.

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For named entity recognition (NER) in zero-resource languages, utilizing knowledge distillation methods to transfer language-independent knowledge from the rich-resource source languages to zero-resource languages is an effective means. Typically, these approaches adopt a teacher-student architecture, where the teacher network is trained in the source language, and the student network seeks to learn knowledge from the teacher network and is expected to perform well in the target language. Despite the impressive performance achieved by these methods, we argue that they have two limitations. Fir

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Zhou, Shuyan, Shruti Rijhwani, John Wieting, Jaime Carbonell, and Graham Neubig. "Improving Candidate Generation for Low-resource Cross-lingual Entity Linking." Transactions of the Association for Computational Linguistics 8 (July 2020): 109–24. http://dx.doi.org/10.1162/tacl_a_00303.

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Cross-lingual entity linking (XEL) is the task of finding referents in a target-language knowledge base (KB) for mentions extracted from source-language texts. The first step of (X)EL is candidate generation, which retrieves a list of plausible candidate entities from the target-language KB for each mention. Approaches based on resources from Wikipedia have proven successful in the realm of relatively high-resource languages, but these do not extend well to low-resource languages with few, if any, Wikipedia pages. Recently, transfer learning methods have been shown to reduce the demand for res

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Ge, Ling, Chunming Hu, Guanghui Ma, Jihong Liu, and Hong Zhang. "DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (2024): 18047–55. http://dx.doi.org/10.1609/aaai.v38i16.29761.

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Multi-Source cross-lingual transfer learning deals with the transfer of task knowledge from multiple labelled source languages to an unlabeled target language under the language shift. Existing methods typically focus on weighting the predictions produced by language-specific classifiers of different sources that follow a shared encoder. However, all source languages share the same encoder, which is updated by all these languages. The extracted representations inevitably contain different source languages' information, which may disturb the learning of the language-specific classifiers. Additi

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Monnar, Ayme Arango, Jorge Perez Rojas, and Barbara Polete Labra. "Cross-lingual hate speech detection using domain-specific word embeddings." PLOS ONE 19, no. 7 (2024): e0306521. http://dx.doi.org/10.1371/journal.pone.0306521.

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THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. Hate speech detection in online social networks is a multidimensional problem, dependent on language and cultural factors. Most supervised learning resources for this task, such as labeled datasets and Natural Language Processing (NLP) tools, have been specifically tailored for English. However, a large portion of web users around the world speak different languages, creating an important need for efficient multilingual hate speech detection approaches. In particular, such approaches should be

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Chen, Muzi. "Analysis on Transfer Learning Models and Applications in Natural Language Processing." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 446–52. http://dx.doi.org/10.54097/hset.v16i.2609.

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Assumptions have been established that many machine learning algorithms expect the training data and the testing data to share the same feature space or distribution. Thus, transfer learning (TL) rises due to the tolerance of the different feature spaces and the distribution of data. It is an optimization to improve performance from task to task. This paper includes the basic knowledge of transfer learning and summarizes some relevant experimental results of popular applications using transfer learning in the natural language processing (NLP) field. The mathematical definition of TL is briefly

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Wu, Qianhui, Zijia Lin, Guoxin Wang, et al. "Enhanced Meta-Learning for Cross-Lingual Named Entity Recognition with Minimal Resources." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 9274–81. http://dx.doi.org/10.1609/aaai.v34i05.6466.

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For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing methods directly transfer from source-learned model to a target language, in this paper, we propose to fine-tune the learned model with a few similar examples given a test case, which could benefit the prediction by leveraging the structural and semantic information conveyed in such similar examples. To this end, we present a meta-learning algorithm to find a good model parameter initialization that could fast adapt to th

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Pasini, Tommaso, Alessandro Raganato, and Roberto Navigli. "XL-WSD: An Extra-Large and Cross-Lingual Evaluation Framework for Word Sense Disambiguation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 15 (2021): 13648–56. http://dx.doi.org/10.1609/aaai.v35i15.17609.

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Transformer-based architectures brought a breeze of change to Word Sense Disambiguation (WSD), improving models' performances by a large margin. The fast development of new approaches has been further encouraged by a well-framed evaluation suite for English, which has allowed their performances to be kept track of and compared fairly. However, other languages have remained largely unexplored, as testing data are available for a few languages only and the evaluation setting is rather matted. In this paper, we untangle this situation by proposing XL-WSD, a cross-lingual evaluation benchmark for

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Pakray, Partha, Alexander Gelbukh, and Sivaji Bandyopadhyay. "Natural language processing applications for low-resource languages." Natural Language Processing 31, no. 2 (2025): 183–97. https://doi.org/10.1017/nlp.2024.33.

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AbstractNatural language processing (NLP) has significantly advanced our ability to model and interact with human language through technology. However, these advancements have disproportionately benefited high-resource languages with abundant data for training complex models. Low-resource languages, often spoken by smaller or marginalized communities, need help realizing the full potential of NLP applications. The primary challenges in developing NLP applications for low-resource languages stem from the need for large, well-annotated datasets, standardized tools, and linguistic resources. This

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Ebrahimi, Mohammadreza, Yidong Chai, Sagar Samtani, and Hsinchun Chen. "Cross-Lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning." MIS Quarterly 46, no. 2 (2022): 1209–26. http://dx.doi.org/10.25300/misq/2022/16618.

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International dark web platforms operating within multiple geopolitical regions and languages host a myriad of hacker assets such as malware, hacking tools, hacking tutorials, and malicious source code. Cybersecurity analytics organizations employ machine learning models trained on human-labeled data to automatically detect these assets and bolster their situational awareness. However, the lack of human-labeled training data is prohibitive when analyzing foreign-language dark web content. In this research note, we adopt the computational design science paradigm to develop a novel IT artifact f

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Chen, Nuo, Linjun Shou, Ming Gong, and Jian Pei. "From Good to Best: Two-Stage Training for Cross-Lingual Machine Reading Comprehension." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 10501–8. http://dx.doi.org/10.1609/aaai.v36i10.21293.

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Cross-lingual Machine Reading Comprehension (xMRC) is a challenging task due to the lack of training data in low-resource languages. Recent approaches use training data only in a resource-rich language (such as English) to fine-tune large-scale cross-lingual pre-trained language models, which transfer knowledge from resource-rich languages (source) to low-resource languages (target). Due to the big difference between languages, the model fine-tuned only by the source language may not perform well for target languages. In our study, we make an interesting observation that while the top 1 result

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Jiang, Aiqi, and Arkaitz Zubiaga. "SexWEs: Domain-Aware Word Embeddings via Cross-Lingual Semantic Specialisation for Chinese Sexism Detection in Social Media." Proceedings of the International AAAI Conference on Web and Social Media 17 (June 2, 2023): 447–58. http://dx.doi.org/10.1609/icwsm.v17i1.22159.

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The goal of sexism detection is to mitigate negative online content targeting certain gender groups of people. However, the limited availability of labeled sexism-related datasets makes it problematic to identify online sexism for low-resource languages. In this paper, we address the task of automatic sexism detection in social media for one low-resource language -- Chinese. Rather than collecting new sexism data or building cross-lingual transfer learning models, we develop a cross-lingual domain-aware semantic specialisation system in order to make the most of existing data. Semantic special

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Pajoohesh, Parto. "A Probe into Lexical Depth: What is the Direction of Transfer for L1 Literacy and L2 Development?" Heritage Language Journal 5, no. 1 (2007): 117–46. http://dx.doi.org/10.46538/hlj.5.1.6.

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This paper examines the intersection between heritage language (HL) learning and the development of English and Farsi deep lexical knowledge. The study compares two groups of Farsi-English bilingual children with different HL educational backgrounds and a group of English-only children by testing their paradigmatic-syntagmatic knowledge of words. A statistical analysis of the children's deep lexical knowledge was conducted in light of their HL literacy experience, second language (English) schooling, and length of residence. The findings revealed that longer length of residence and L2 schoolin

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S, Tarun. "Bridging Languages through Images: A Multilingual Text-to-Image Synthesis Approach." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33773.

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This research investigates the challenges posed by the predominant focus on English language text-to-image generation (TTI) because of the lack of annotated image caption data in other languages. The resulting inequitable access to TTI technology in non-English-speaking regions motivates the research of multilingual TTI (mTTI) and the potential of neural machine translation (NMT) to facilitate its development. The study presents two main contributions. Firstly, a systematic empirical study employing a multilingual multi-modal encoder evaluates standard cross-lingual NLP methods applied to mTTI

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Yadav, Siddharth, and Tanmoy Chakraborty. "Zera-Shot Sentiment Analysis for Code-Mixed Data." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15941–42. http://dx.doi.org/10.1609/aaai.v35i18.17967.

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Code-mixing is the practice of alternating between two or more languages. A major part of sentiment analysis research has been monolingual and they perform poorly on the code-mixed text. We introduce methods that use multilingual and cross-lingual embeddings to transfer knowledge from monolingual text to code-mixed text for code-mixed sentiment analysis. Our methods handle code-mixed text through zero-shot learning and beat state-of-the-art English-Spanish code-mixed sentiment analysis by an absolute 3% F1-score. We are able to achieve 0.58 F1-score (without a parallel corpus) and 0.62 F1-scor

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Xu, Yaoli, Jinjun Zhong, Suzhi Zhang, et al. "A Domain-Oriented Entity Alignment Approach Based on Filtering Multi-Type Graph Neural Networks." Applied Sciences 13, no. 16 (2023): 9237. http://dx.doi.org/10.3390/app13169237.

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Owing to the heterogeneity and incomplete information present in various domain knowledge graphs, the alignment of distinct source entities that represent an identical real-world entity becomes imperative. Existing methods focus on cross-lingual knowledge graph alignment, and assume that the entities of knowledge graphs in the same language are unique. However, due to the ambiguity of language, heterogeneous knowledge graphs in the same language are often duplicated, and relationship triples are far less than those of cross-lingual knowledge graphs. Moreover, existing methods rarely exclude no

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Mahajan, Vaibhav Fanindra. "The Evolution of AI Support: How RAG is Transforming Customer Experience." European Journal of Computer Science and Information Technology 13, no. 14 (2025): 115–26. https://doi.org/10.37745/ejcsit.2013/vol13n14115126.

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This article examines how Retrieval-Augmented Generation (RAG) is transforming customer support operations by addressing the fundamental limitations of traditional AI chatbots. While conventional chatbots rely on either rule-based systems or limited machine learning models with static knowledge bases, RAG represents a paradigm shift by dynamically retrieving information from enterprise knowledge sources before generating responses. This hybrid approach combines the strengths of retrieval-based and generation-based methods to deliver more accurate, contextually appropriate, and up-to-date suppo

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BOCHAROVA, Maiia, and Eugene MALAKHOV. "GENERAL-PURPOSE TEXT EMBEDDINGS LEARNING FOR UKRAINIAN LANGUAGE." Advanced Information Technology, no. 1 (3) (2024): 6–12. https://doi.org/10.17721/ait.2024.1.01.

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B a c k g r o u n d . Learning high-quality text embeddings typically requires large corpuses of labeled data, which can be challenging to obtain for many languages and domains. This study proposes a novel adaptation of cross-lingual knowledge transfer that employs a cosine similarity-based loss calculation to enhance the alignment of learned representations. M e t h o d s . The impact of teacher model selection on the quality of learned text representations is investigated. Specifically, the correlation between cosine similarity scores among vectors of randomly selected sentences and the tran

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Wu, Ji, Madeleine Orr, Kurumi Aizawa, and Yuhei Inoue. "Language Relativity in Legacy Literature: A Systematic Review in Multiple Languages." Sustainability 13, no. 20 (2021): 11333. http://dx.doi.org/10.3390/su132011333.

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Since the Olympic Agenda 2020, legacy has been widely used as a justification for hosting the Olympic Games, through which sustainable development can be achieved for both events and host cities. To date, no universal definition of legacy has been established, which presents challenges for legacy-related international knowledge transfer among host cities. To address this gap, a multilingual systematic review of the literature regarding the concept of legacy was conducted in French, Japanese, Chinese, and English. Using English literature as a baseline, points of convergence and divergence amon

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Guseynova, Innara. "Digital Transformation and Its Consequences for Heritage Languages." Nizhny Novgorod Linguistics University Bulletin, Special issue (December 31, 2020): 44–58. http://dx.doi.org/10.47388/2072-3490/lunn2020-si-44-58.

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The article attempts to conduct a primary analysis of the consequences of digital transformation for heritage languages which make up the cultural and historical legacy of individual ethnic communities. In a multilingual society, such a study requires an integrated approach, which takes into account the sociolinguistic parameters of various target audiences, communication channels aimed to disseminate and transfer information, discourse analyses of lin-guistic means, as well as extralinguistic factors impacting the development of different environments. It is equally important to study the spe

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Shwartz, Mila, Mark Leikin, and David L. Share. "Bi-literate bilingualism versus mono-literate bilingualism." Written Language and Literacy 8, no. 2 (2005): 103–30. http://dx.doi.org/10.1075/wll.8.2.08shw.

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The present study compared the early Hebrew (L2) literacy development of three groups; two groups of bilinguals — bi-literate and mono-literate Russian-Hebrew speakers, and a third group of monolingual Hebrew-speakers. We predicted that bi-literacy rather than bilingualism is the key variable as regards L2 literacy learning. In a longitudinal design, a variety of linguistic, meta-linguistic and cognitive tasks were administered at the commencement of first grade, with Hebrew reading and spelling assessed at the end of the year. Results demonstrated that bi-literate bilinguals were far in advan

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