| Challenge: | Existing studies in cross-language information retrieval (CLIR) use general text representation models that are not optimized for the target task. |
| Approach: | They propose a novel text representation model based on adversarial learning which seeks a task-specific embedding space for CLIR. |
| Outcome: | The proposed model outperforms state-of-the-art continuous space models and is better than the strong machine translation baseline. |
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Cross-Lingual Learning-to-Rank with Shared Representations (N18-2)
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| Challenge: | Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user's query. |
| Approach: | They propose a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. |
| Outcome: | The proposed model can improve the results of Swahili-English CLIR in Japanese and Japanese. |
Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
| Approach: | They propose a cross-lingual transfer learning method that leverages annotated data from other languages to build NLP models for a target language. |
| Outcome: | The proposed model achieves significant performance gains over prior art over multiple text classification and sequence tagging tasks including a large-scale industry dataset. |
Bridging the Gap between Native Text and Translated Text through Adversarial Learning: A Case Study on Cross-Lingual Event Extraction (2023.findings-eacl)
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| Challenge: | Recent research in cross-lingual learning has found that combining large-scale pretrained multilingual language models with machine translation can yield good performance. |
| Approach: | They propose a model architecture that jointly encodes a source language input sentence with its translation to the target language during training and takes a target language sentence with it as input during evaluation. |
| Outcome: | The proposed model architecture can integrate machine translation to improve event extraction while adding machine-translated data yields unstable performance due to representational gap. |
Cross-Lingual Training of Neural Models for Document Ranking (2020.findings-emnlp)
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| Challenge: | a recent study shows that multi-lingual BERT models can be used for document ranking in non-English languages . a blog post by Google suggests that the company is exploring this approach to improve web search across a number of languages. |
| Approach: | They propose to leverage relevance judgments in English to train neural document ranking models for mono-lingual retrieval in multiple target languages. |
| Outcome: | The proposed approach improves search quality in non-English languages while requiring low resources. |
Data Augmentation with Adversarial Training for Cross-Lingual NLI (2021.acl-long)
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| Challenge: | Existing approaches to train cross-lingual models with labeled data are subpar, resulting in subpar results. |
| Approach: | They propose a data augmentation strategy that enriches data to reflect more diversity in a semantically faithful way and leverages adversarial training regimens to achieve greater robustness. |
| Outcome: | The proposed approach improves cross-lingual inference by leveraging the data to reflect more diversity in a semantically faithful way. |
Representation Alignment and Adversarial Networks for Cross-lingual Dependency Parsing (2024.findings-emnlp)
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| 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. |
Multi-Adversarial Learning for Cross-Lingual Word Embeddings (2021.naacl-main)
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| Challenge: | Generative adversarial networks (GANs) have succeeded in inducing cross-lingual word embeddings without supervision, but their performance for distant languages is still not satisfactory. |
| Approach: | They propose a multi-adversarial method that induces the seed cross-lingual dictionary through multiple mappings, each induced to fit the mapping for one subspace. |
| Outcome: | The proposed method improves performance on bilingual lexicon induction and cross-lingual document classification on unsupervised bilingual linguistic induction. |
Cross-Lingual NLU: Mitigating Language-Specific Impact in Embeddings Leveraging Adversarial Learning (2024.lrec-main)
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| Challenge: | Low-resource languages and computational expenses pose significant challenges in the domain of large language models. |
| Approach: | They propose a novel approach that uses adversarial techniques to mitigate the impact of language-specific information in contextual embeddings generated by large multilingual language models. |
| Outcome: | The proposed approach excels in zero-shot scenarios for Latin languages like Spanish, but fails to perform for languages distant from English, such as Thai and Persian. |
Examining Multilingual Embedding Models Cross-Lingually Through LLM-Generated Adversarial Examples (2025.findings-emnlp)
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| Challenge: | Cross-Lingual Semantic Discrimination (CLSD) is a lightweight evaluation task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. |
| Approach: | They propose a lightweight task that requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. |
| Outcome: | The proposed task requires only parallel sentences and a Large Language Model (LLM) to generate adversarial distractors. |
Deep Adversarial Learning for NLP (N19-5)
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| Challenge: | Adversarial learning is a game-theoretic learning paradigm that has achieved huge successes in the field of Computer Vision recently. |
| Approach: | This tutorial introduces the foundations of deep adversarial learning and some practical problems and solutions in NLP. |
| Outcome: | This tutorial introduces the foundations of deep adversarial learning and some practical problems and solutions in NLP. |