Papers with CLIR
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage (P19-3)
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Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, Scott Miller
| Challenge: | a new cross-lingual information retrieval system for low-resource languages is available in less-frequently-taught languages . a multilingual system can search for relevant information in a haystack of documents in swahili or Somali . human-driven approaches to this problem are complicated in 'low-resourced' languages aaron sagar: "the key role played by humans in triaging results is complicated" |
| Approach: | They propose an end-to-end cross-lingual information retrieval system for low-resource languages . the system enables English speakers to search foreign language repositories using English queries . it summarizes the retrieved documents in English with respect to a particular information need . |
| Outcome: | The proposed system achieves top performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. |
CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task (2020.acl-demos)
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| Challenge: | evaluating machine translation (MT) with cross-lingual information retrieval is relatively time-consuming and subjective. |
| Approach: | They propose a toolkit that evaluates machine translation with a proxy task of cross-lingual information retrieval. |
| Outcome: | The proposed toolkit is based on the "metrics shared task" of WMT2019. |
Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval (D19-61)
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| Challenge: | Low resource languages often lack relevance annotations for cross-lingual information retrieval . when available, the training data has limited coverage for possible queries . |
| Approach: | They propose a weakly supervised neural model for Cross-lingual information retrieval from low-resource languages using weak supervision instead of relevance annotations. |
| Outcome: | The proposed model achieves 19 MAP points improvement compared to CNNs and 12 points improvement from machine translation-based CLIR models. |
Zero-Shot Cross-Lingual Reranking with Large Language Models for Low-Resource Languages (2024.acl-short)
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| 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 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. |
MuSeCLIR: A Multiple Senses and Cross-lingual Information Retrieval Dataset (2022.coling-1)
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| Challenge: | Existing datasets for cross-lingual information retrieval (CLIR) are dominated by searches for named entity mentions, which does not provide a good measure for disambiguation performance. |
| Approach: | They propose a dataset to evaluate CLIR systems' disambiguation ability based on polysemous common nouns with multiple possible translations. |
| Outcome: | The proposed dataset shows that it has a higher requirement on the ability of CLIR systems to disambiguate query terms. |
Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data (2023.findings-acl)
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| 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. |
Learning Neural Representation for CLIR with Adversarial Framework (D18-1)
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| 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. |
Segmenting Subtitles for Correcting ASR Segmentation Errors (2021.eacl-main)
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David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
| Challenge: | Typical ASR systems segment input audio into utterances using purely acoustic information, which may not resemble sentence-like units expected by conventional machine translation systems for spoken language translation (SLT). |
| Approach: | They propose a model for correcting ASR acoustic segmentation using subtitles as a proxy dataset for creating synthetic aural utterances by modeling common error modes. |
| Outcome: | The proposed model improves performance on MT and audio-document cross-language information retrieval (CLIR) it uses subtitles as a proxy dataset to correct ASR acoustic segmentation . |
Learning Cross-Lingual IR from an English Retriever (2022.naacl-main)
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| Challenge: | DR.DECR is a cross-lingual information retrieval system trained using multi-stage knowledge distillation (KD) DRDECR demonstrates superior accuracy over direct fine-tuning with labeled CLIR data. |
| Approach: | They propose a cross-lingual information retrieval system with multi-stage knowledge distillation . they teach powerful multilingual representations and CLIR by optimizing two corresponding KD objectives . |
| Outcome: | The proposed system is the best single-model retriever on the XOR-TyDi benchmark . it is based on a multi-stage knowledge distillation process that can be expensive . |
CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval (2020.emnlp-main)
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| Challenge: | Cross-Lingual Information Retrieval (CLIR) is a retrieval task in which search queries and candidate documents are written in different languages. |
| Approach: | They present a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia. |
| Outcome: | The proposed datasets are the largest and most comprehensive CLIR dataset to date. |
The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval (D19-1)
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| Challenge: | Existing studies do not investigate the effectiveness of MT metrics in predicting performance of downstream IR models. |
| Approach: | They examine the relationship between MT performance and IR quality in a CLIR-based system . they find that the choice of IR collection can significantly affect MT tuning decisions . |
| Outcome: | The proposed model can predict CLIR performance better from MT quality, the authors show . the proposed model is based on a BLEU-based model with a bag of words constraint . |
Domain Transfer based Data Augmentation for Neural Query Translation (2020.coling-main)
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| Challenge: | Query translation (QT) is a critical factor in successful cross-lingual information retrieval (CLIR). |
| Approach: | They propose to extend query translation (QT) with a domain transfer procedure to revise synthetic candidates to search-aware examples. |
| Outcome: | The proposed method outperforms baselines and domain transfer methods on translation quality and retrieval accuracy. |
Evaluating Large Language Models for Cross-Lingual Retrieval (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have been evaluated as second-stage reranking models for monolingual IR, but a systematic comparison is lacking for cross-lingual reranked IR. |
| Approach: | They propose to use machine translation to evaluate rerankers in cross-lingual IR . they find that LLMs perform better than LLM-based reranked models . |
| Outcome: | The proposed model improves cross-lingual IR but relies on machine translation for the first stage. |
A Multi-Task Architecture on Relevance-based Neural Query Translation (P19-1)
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| Challenge: | Existing models for cross-lingual information retrieval are not aware of the vocabulary distribution of the retrieval corpus. |
| Approach: | They propose a multi-task learning approach to train a Neural Machine Translation model with a Relevance-based Auxiliary Task (RAT) for search query translation. |
| Outcome: | The proposed model achieves 16% improvement over a strong baseline on Italian-English query-document dataset. |
Cross-Dialect Information Retrieval: Information Access in Low-Resource and High-Variance Languages (2025.coling-main)
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| Challenge: | lexical gaps between dialects in cross-lingual information retrieval (CLIR) are caused by orthographic variations and different regional expressions. |
| Approach: | They propose a dataset that consists of seven German dialects extracted from Wikipedia. |
| Outcome: | The proposed dataset consists of seven German dialects extracted from Wikipedia. |