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BLADE: Combining Vocabulary Pruning and Intermediate Pretraining for Scaleable Neural CLIR

Authors:

James Mayfield,

Douglas W. OardAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1219 - 1229

https://doi.org/10.1145/3539618.3591644

Published: 18 July 2023 Publication History

Abstract

Learning sparse representations using pretrained language models enhances the monolingual ranking effectiveness. Such representations are sparse vectors in the vocabulary of a language model projected from document terms. Extending such approaches to Cross-Language Information Retrieval (CLIR) using multilingual pretrained language models poses two challenges. First, the larger vocabularies of multilingual models affect both training and inference efficiency. Second, the representations of terms from different languages with similar meanings might not be sufficiently similar. To address these issues, we propose a learned sparse representation model, BLADE, combining vocabulary pruning with intermediate pre-training based on cross-language supervision. Our experiments reveal BLADE significantly reduces indexing time compared to its monolingual counterpart, SPLADE, on machine-translated documents, and it generates rankings with strengths complementary to those of other efficient CLIR methods.

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This talk presents BLADE, a novel Cross-Language Information Retrieval (CLIR) model that utilizes sparse representations with multilingual pretrained language models. We address two key challenges in building sparse neural CLIR models: handling larger vocabularies and ensuring similarity between terms across languages. BLADE combines vocabulary pruning and intermediate pre-training under cross-language supervision to tackle these issues, thereby balancing effectiveness and efficiency.

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References

[1]

Amine Abdaoui, Camille Pradel, and Grégoire Sigel. 2020. Load What You Need: Smaller Versions of Multilingual BERT. In SustaiNLP / EMNLP.

[2]

Ahmed Abdelali, Francisco Guzman, Hassan Sajjad, and Stephan Vogel. 2014. The AMARA Corpus: Building Parallel Language Resources for the Educational Domain. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14). European Language Resources Association (ELRA), Reykjavik, Iceland, 1856--1862. http://www.lrec-conf.org/proceedings/lrec2014/pdf/877_Paper.pdf

[3]

Mikel Artetxe, Gorka Labaka, and Eneko Agirre. 2020. Translation Artifacts in Cross-lingual Transfer Learning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Online, 7674--7684. https://doi.org/10.18653/v1/2020.emnlp-main.618

[4]

Yang Bai, Xiaoguang Li, Gang Wang, Chaoliang Zhang, Lifeng Shang, Jun Xu, Zhaowei Wang, Fangshan Wang, and Qun Liu. 2020. SparTerm: Learning term-based sparse representation for fast text retrieval. arXiv preprint arXiv:2010.00768 (2020).

[5]

Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang. 2016. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. https://doi.org/10.48550/ARXIV.1611.09268

[6]

Loïc Barrault, Ondvr ej Bojar, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Philipp Koehn, Shervin Malmasi, Christof Monz, Mathias Müller, Santanu Pal, Matt Post, and Marcos Zampieri. 2019. Findings of the 2019 Conference on Machine Translation (WMT19). In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1). Association for Computational Linguistics, Florence, Italy, 1--61. https://doi.org/10.18653/v1/W19-5301

[7]

Michael Bendersky and Oren Kurland. 2008. Utilizing passage-based language models for document retrieval. In European Conference on Information Retrieval. Springer, 162--174.

[8]

Luiz Henrique Bonifacio, Israel Campiotti, Vitor Jeronymo, Roberto Lotufo, and Rodrigo Nogueira. 2021. mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset. arXiv preprint arXiv:2108.13897 (2021).

[9]

Martin Braschler and Carol Peters. 2004. CLEF 2003 Methodology and Metrics. In Comparative Evaluation of Multilingual Information Access Systems. Springer Berlin Heidelberg, 7--20.

[10]

Eunseong Choi, Sunkyung Lee, Minijn Choi, Hyeseon Ko, Young-In Song, and Jongwuk Lee. 2022. SpaDE: Improving sparse representations using a dual document encoder for first-stage retrieval. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 272--282.

Digital Library

[11]

Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised Cross-lingual Representation Learning at Scale. arxiv: 1911.02116 [cs.CL]

[12]

Gordon V Cormack, Charles LA Clarke, and Stefan Buettcher. 2009. Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 758--759.

Digital Library

[13]

Javid Dadashkarimi, Azadeh Shakery, Heshaam Faili, and Hamed Zamani. 2017. An expectation-maximization algorithm for query translation based on pseudo-relevant documents. Information Processing & Management, Vol. 53, 2 (2017), 371--387.

Digital Library

[14]

Zhuyun Dai and Jamie Callan. 2019a. Context-aware sentence/passage term importance estimation for first stage retrieval. arXiv preprint arXiv:1910.10687 (2019).

[15]

Zhuyun Dai and Jamie Callan. 2019b. Deeper Text Understanding for IR with Contextual Neural Language Modeling. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR'19). Association for Computing Machinery, New York, NY, USA, 985--988.

Digital Library

[16]

Kareem Darwish and Douglas W Oard. 2003. Probabilistic structured query methods. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 338--344.

Digital Library

[17]

Tobias Domhan, Michael Denkowski, David Vilar, Xing Niu, Felix Hieber, and Kenneth Heafield. 2020. The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020. In Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track). Association for Machine Translation in the Americas, Virtual, 110--115.

[18]

Tobias Domhan, Eva Hasler, Ke Tran, Sony Trenous, Bill Byrne, and Felix Hieber. 2022. The devil is in the details: on the pitfalls of vocabulary selection in neural machine translation. arXiv preprint arXiv:2205.06618 (2022).

[19]

Andreas Eisele and Yu Chen. 2010. MultiUN: A Multilingual Corpus from United Nation Documents. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10). European Language Resources Association (ELRA), Valletta, Malta. http://www.lrec-conf.org/proceedings/lrec2010/pdf/686_Paper.pdf

[20]

Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant. 2021a. SPLADE v2: Sparse lexical and expansion model for information retrieval. arXiv preprint arXiv:2109.10086 (2021).

[21]

Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant. 2022. From distillation to hard negative sampling: Making sparse neural ir models more effective. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2353--2359.

Digital Library

[22]

Thibault Formal, Benjamin Piwowarski, and Stéphane Clinchant. 2021b. SPLADE: Sparse lexical and expansion model for first stage ranking. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2288--2292.

Digital Library

[23]

Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tie-Yan Liu. 2019. Representation Degeneration Problem in Training Natural Language Generation Models. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https://openreview.net/forum?id=SkEYojRqtm

[24]

Luyu Gao, Xueguang Ma, Jimmy J. Lin, and Jamie Callan. 2022. Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval. arXiv preprint arxiv:2203.05765 (2022).

[25]

Felix Hieber, Tobias Domhan, Michael Denkowski, and David Vilar. 2020. SOCKEYE 2: A Toolkit for Neural Machine Translation. In EAMT 2020. https://www.amazon.science/publications/sockeye-2-a-toolkit-for-neural-machine-translation

[26]

Sebastian Hofstätter, Sophia Althammer, Michael Schröder, Mete Sertkan, and Allan Hanbury. 2020. Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation. arxiv: 2010.02666 [cs.IR]

[27]

Zhiqi Huang, Puxuan Yu, and James Allan. 2022. Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation. In Proceedings of Web Search and Data Mining.

Digital Library

[28]

Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. 2015. On Using Very Large Target Vocabulary for Neural Machine Translation. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Beijing, China, 1--10. https://doi.org/10.3115/v1/P15--1001

[29]

David Kamholz, Jonathan Pool, and Susan Colowick. 2014. PanLex: Building a Resource for Panlingual Lexical Translation. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14). European Language Resources Association (ELRA), Reykjavik, Iceland, 3145--3150. http://www.lrec-conf.org/proceedings/lrec2014/pdf/1029_Paper.pdf

[30]

Vladimir Karpukhin, Barlas Oug uz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for open-domain question answering. arXiv preprint arXiv:2004.04906 (2020).

[31]

Omar Khattab and Matei Zaharia. 2020. ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 39--48.

Digital Library

[32]

Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

[33]

Philipp Koehn. 2005. Europarl: A Parallel Corpus for Statistical Machine Translation. Machine Translation Summit, 2005 (2005), 79--86.

[34]

Carlos Lassance and Stéphane Clinchant. 2022. An efficiency study for SPLADE models. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2220--2226.

Digital Library

[35]

Dawn Lawrie, Sean MacAvaney, James Mayfield, Paul McNamee, Douglas W. Oard, Luca Soldanini, and Eugene Yang. 2022. Overview of the TREC 2022 NeuCLIR Track. In The Thirty-first Text REtrieval Conference (TREC 2022) Proceedings.

[36]

Yulong Li, Martin Franz, Md Arafat Sultan, Bhavani Iyer, Young-Suk Lee, and Avirup Sil. 2022. Learning Cross-Lingual IR from an English Retriever. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Seattle, United States, 4428--4436. https://doi.org/10.18653/v1/2022.naacl-main.329

[37]

Percy Liang, Ben Taskar, and Dan Klein. 2006. Alignment by Agreement. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference. Association for Computational Linguistics, New York City, USA, 104--111. https://aclanthology.org/N06-1014

Digital Library

[38]

Jimmy Lin and Xueguang Ma. 2021. A few brief notes on DeepImpact, COIL, and a conceptual framework for information retrieval techniques. arXiv preprint arXiv:2106.14807 (2021).

[39]

Robert Litschko, Ivan Vulić, Simone Paolo Ponzetto, and Goran Glavavs. 2021. On Cross-Lingual Retrieval with Multilingual Text Encoders. arXiv preprint arXiv:2112.11031 (2021).

[40]

Joel Mackenzie, Antonio Mallia, Alistair Moffat, and Matthias Petri. 2022a. Accelerating Learned Sparse Indexes Via Term Impact Decomposition. In Findings of the Association for Computational Linguistics: EMNLP 2022. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 2830--2842. https://aclanthology.org/2022.findings-emnlp.205

[41]

Joel Mackenzie, Andrew Trotman, and Jimmy Lin. 2021. Wacky weights in learned sparse representations and the revenge of score-at-a-time query evaluation. arXiv preprint arXiv:2110.11540 (2021).

[42]

Joel Mackenzie, Andrew Trotman, and Jimmy Lin. 2022b. Efficient Document-at-a-Time and Score-at-a-Time Query Evaluation for Learned Sparse Representations. ACM Transactions on Information Systems (2022).

[43]

Antonio Mallia, Omar Khattab, Torsten Suel, and Nicola Tonellotto. 2021. Learning passage impacts for inverted indexes. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1723--1727.

Digital Library

[44]

Antonio Mallia, Joel Mackenzie, Torsten Suel, and Nicola Tonellotto. 2022. Faster learned sparse retrieval with guided traversal. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1901--1905.

Digital Library

[45]

Antonio Mallia, Michal Siedlaczek, Joel Mackenzie, and Torsten Suel. 2019b. PISA: Performant Indexes and Search for Academia. In Proceedings of the Open-Source IR Replicability Challenge co-located with 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, OSIRRC@SIGIR 2019, Paris, France, July 25, 2019. 50--56. http://ceur-ws.org/Vol-2409/docker08.pdf

[46]

Antonio Mallia, Michał Siedlaczek, and Torsten Suel. 2019a. An experimental study of index compression and DAAT query processing methods. In Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14-18, 2019, Proceedings, Part I 41. Springer, 353--368.

Digital Library

[47]

Suraj Nair, Petra Galuscakova, and Douglas W Oard. 2020. Combining contextualized and non-contextualized query translations to improve CLIR. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1581--1584.

Digital Library

[48]

Suraj Nair, Eugene Yang, Dawn Lawrie, Kevin Duh, Paul McNamee, Kenton Murray, James Mayfield, and Douglas W Oard. 2022a. Transfer learning approaches for building cross-language dense retrieval models. In European Conference on Information Retrieval. Springer, 382--396.

Digital Library

[49]

Suraj Nair, Eugene Yang, Dawn Lawrie, James Mayfield, and Douglas W Oard. 2022b. Learning a Sparse Representation Model for Neural CLIR. In Proceedings of the Third International Conference on Design of Experimental Search & Information REtrieval Systems, San Jose, CA.

[50]

Rodrigo Nogueira and Jimmy Lin. 2019. From doc2query to docTTTTTquery. Technical Report. University of Waterloo. https://cs.uwaterloo.ca/ jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf

[51]

Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019. Document expansion by query prediction. arXiv preprint arXiv:1904.08375 (2019).

[52]

Franz Josef Och and Hermann Ney. 2003. A systematic comparison of various statistical alignment models. Computational Linguistics (2003).

[53]

Robert Östling and Jörg Tiedemann. 2016. Efficient word alignment with markov chain monte carlo. The Prague Bulletin of Mathematical Linguistics (2016).

[54]

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics. 311--318.

Digital Library

[55]

Biswajit Paria, Chih-Kuan Yeh, Ian E.H. Yen, Ning Xu, Pradeep Ravikumar, and Barnabás Póczos. 2020. Minimizing FLOPs to Learn Efficient Sparse Representations. In International Conference on Learning Representations. https://openreview.net/forum?id=SygpC6Ntvr

[56]

Ruihong Qiu, Zi Huang, Hongzhi Yin, and Zijian Wang. 2022. Contrastive learning for representation degeneration problem in sequential recommendation. In Proceedings of the fifteenth ACM international conference on web search and data mining. 813--823.

Digital Library

[57]

Nils Reimers and Iryna Gurevych. 2020. Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. https://arxiv.org/abs/2004.09813

[58]

Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995. Okapi at TREC-3. NIST Special Publication Sp, Vol. 109 (1995), 109.

[59]

Keshav Santhanam, Omar Khattab, Christopher Potts, and Matei Zaharia. 2022. PLAID: an efficient engine for late interaction retrieval. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1747--1756.

Digital Library

[60]

Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. 2021. Colbertv2: Effective and efficient retrieval via lightweight late interaction. arXiv preprint arXiv:2112.01488 (2021).

[61]

Xing Shi and Kevin Knight. 2017. Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Vancouver, Canada, 574--579. https://doi.org/10.18653/v1/P17-2091

[62]

Jianlin Su, Jiarun Cao, Weijie Liu, and Yangyiwen Ou. 2021. Whitening sentence representations for better semantics and faster retrieval. arXiv preprint arXiv:2103.15316 (2021).

[63]

Shuo Sun and Kevin Duh. 2020. CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 4160--4170.

[64]

Jörg Tiedemann. 2012. Parallel data, tools and interfaces in OPUS. In Lrec, Vol. 2012. Citeseer, 2214--2218.

[65]

Howard Turtle and James Flood. 1995. Query evaluation: strategies and optimizations. Information Processing & Management, Vol. 31, 6 (1995), 831--850.

Digital Library

[66]

Vered Volansky, Noam Ordan, and Shuly Wintner. 2015. On the features of translationese. Digital Scholarship in the Humanities, Vol. 30, 1 (2015), 98--118.

[67]

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Online, 38--45. https://www.aclweb.org/anthology/2020.emnlp-demos.6

[68]

Jinxi Xu and Ralph Weischedel. 2000. Cross-lingual information retrieval using Hidden Markov models. In 2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora. 95--103.

Digital Library

[69]

Eugene Yang, Suraj Nair, Ramraj Chandradevan, Rebecca Iglesias-Flores, and Douglas W. Oard. 2022. C3: Continued Pretraining with Contrastive Weak Supervision for Cross Language Ad-Hoc Retrieval. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR '22). Association for Computing Machinery, New York, NY, USA, 2507--2512. https://doi.org/10.1145/3477495.3531886

Digital Library

[70]

Jheng-Hong Yang, Xueguang Ma, and Jimmy Lin. 2021. Sparsifying Sparse Representations for Passage Retrieval by Top-k Masking. arXiv preprint arXiv:2112.09628 (2021).

[71]

Peilin Yang, Hui Fang, and Jimmy Lin. 2017. Anserini: Enabling the Use of Lucene for Information Retrieval Research. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (Shinjuku, Tokyo, Japan) (SIGIR '17). Association for Computing Machinery, New York, NY, USA, 1253--1256.

Digital Library

[72]

Hamed Zamani, Mostafa Dehghani, W Bruce Croft, Erik Learned-Miller, and Jaap Kamps. 2018. From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 497--506.

Digital Library

[73]

Shengyao Zhuang and Guido Zuccon. 2021a. Fast passage re-ranking with contextualized exact term matching and efficient passage expansion. arXiv preprint arXiv:2108.08513 (2021).

[74]

Shengyao Zhuang and Guido Zuccon. 2021b. TILDE: Term independent likelihood moDEl for passage re-ranking. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1483--1492.

Digital Library

[75]

Michał Ziemski, Marcin Junczys-Dowmunt, and Bruno Pouliquen. 2016. The united nations parallel corpus v1. 0. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16). 3530--3534.

Cited By

Yang ELawrie DMayfield JOard DMiller S(2024)Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and DistillationAdvances in Information Retrieval10.1007/978-3-031-56060-6_4(50-65)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_4

Index Terms

BLADE: Combining Vocabulary Pruning and Intermediate Pretraining for Scaleable Neural CLIR
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

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Published: 18 July 2023

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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Cited By

Yang ELawrie DMayfield JOard DMiller S(2024)Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and DistillationAdvances in Information Retrieval10.1007/978-3-031-56060-6_4(50-65)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_4

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