Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data (2023.findings-acl)
Copied to clipboard
| Challenge: | Using zero-shot rankers, cross-lingual IR models are limited by their language coverage. |
| Approach: | They propose to train ranking models on artificially code-switched data instead of using a dictionary. |
| Outcome: | The proposed approach is robust towards the ratio of code-switched tokens and extends to unseen languages. |
Similar Papers
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)
Copied to clipboard
Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
| Challenge: | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages . |
| Approach: | They propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embedders without semantic loss. |
| Outcome: | Experimental results show that the proposed method outperforms existing methods on cross-lingual tasks and can achieve a better multilingual alignment. |
Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages (2024.acl-short)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown impressive zero-shot capabilities in various passage ranking tasks. |
| Approach: | They analyze and compare the effectiveness of monolingual reranking using query or document translations and evaluate the effectiveness when leveraging their own generated translations. |
| Outcome: | The proposed models perform better when using their own translations than when using query or document translations. |
Cross-Lingual Training of Neural Models for Document Ranking (2020.findings-emnlp)
Copied to clipboard
| 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. |
Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing work on cross-language entity linking grounds mentions written in multiple languages to a monolingual knowledge base is lacking. |
| Approach: | They propose a task that uses multilingual BERT representations of both the mention and context as input and explore zero-shot language transfer. |
| Outcome: | The proposed model performs well in both monolingual and multilingual settings. |
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval (2022.coling-1)
Copied to clipboard
| Challenge: | State-of-the-art neural rankers are notoriously data-hungry and rarely used in multilingual and cross-lingual retrieval settings. |
| Approach: | They propose to use Sparse Fine-Tuning Masks and Adapters to transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders. |
| Outcome: | The proposed methods outperform standard zero-shot transfer with full MMT fine-tuning while being more modular and reducing training times. |
Model-Based Ranking of Source Languages for Zero-Shot Cross-Lingual Transfer (2025.emnlp-main)
Copied to clipboard
| Challenge: | NN-Rank is an algorithm for ranking source languages for cross-lingual transfer . it leverages hidden representations from multilingual models and unlabeled target-language data . |
| Approach: | They propose an algorithm for ranking source languages for cross-lingual transfer which leverages hidden representations from multilingual models and unlabeled target-language data. |
| Outcome: | The proposed algorithm outperforms state-of-the-art models on in-domain data and shows that it can achieve 92.8% of the NDCG achieved using all available target data. |
Can Monolingual Pretrained Models Help Cross-Lingual Classification? (2020.aacl-main)
Copied to clipboard
| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
| Approach: | They propose to transfer the knowledge from monolingual pretrained models to multilingual ones to improve zero-shot cross-lingual classification by using machine translation systems. |
| Outcome: | The proposed methods outperform vanilla multilingual fine-tuning on two cross-lingual classification benchmarks. |
Exploring efficient zero-shot synthetic dataset generation for Information Retrieval (2024.findings-eacl)
Copied to clipboard
| Challenge: | Recent advances in large language models offer a new avenue of generating synthetic training data to train neural retrieval models for unlabelled data collections. |
| Approach: | They propose a method to generate high-quality synthetic datasets using a small language model and a filtering mechanism to ensure the quality of generated questions. |
| Outcome: | The proposed method outperforms unsupervised retrieval methods such as BM25 and pretrained monoT5. |
Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers (2026.findings-acl)
Copied to clipboard
Qingcheng Zeng, Yuheng Lu, Zeqi Zhou, Heli Qi, Puxuan Yu, Fuheng Zhao, Hitomi Yanaka, Weihao Xuan, Naoto Yokoya
| Challenge: | a new study examines the performance of code-switching IR in monolingual contexts . code-witching is a pervasive linguistic phenomenon in global communication . |
| Approach: | They propose a benchmark to evaluate code-switching IR in monolingual contexts . they propose CS-MTEB, which measures performance declines of up to 27% . |
| Outcome: | The proposed benchmark shows that code-switching performance is degraded by 27% . the proposed benchmark is based on a dataset of mixed-language queries . |
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training (2021.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained multilingual language encoders do not precisely align words and phrases across languages. |
| Approach: | They propose a learning strategy for training robust models by drawing connections between adversarial examples and failure cases of zero-shot cross-lingual transfer. |
| Outcome: | The proposed model can achieve good performance even if representations of different languages are not aligned well. |