{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-bilingual-lexicon-induction-with-1","title":"Improving Bilingual Lexicon Induction with Cross-Encoder Reranking","arxiv_id":"2210.16953","date":"2022-10-30","proceeding":null,"authors":["Yaoyiran Li","Fangyu Liu","Ivan Vulić","Anna Korhonen"],"abstract":"Bilingual lexicon induction (BLI) with limited bilingual supervision is a crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI methods rely on the induction of cross-lingual word embeddings (CLWEs) to capture cross-lingual word similarities; such CLWEs are obtained 1) via traditional static models (e.g., VecMap), or 2) by extracting type-level CLWEs from multilingual pretrained language models (mPLMs), or 3) through combining the former two options. In this work, we propose a novel semi-supervised post-hoc reranking method termed BLICEr (BLI with Cross-Encoder Reranking), applicable to any precalculated CLWE space, which improves their BLI capability. The key idea is to 'extract' cross-lingual lexical knowledge from mPLMs, and then combine it with the original CLWEs. This crucial step is done via 1) creating a word similarity dataset, comprising positive word pairs (i.e., true translations) and hard negative pairs induced from the original CLWE space, and then 2) fine-tuning an mPLM (e.g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores. At inference, we 3) combine the similarity score from the original CLWE space with the score from the BLI-tuned cross-encoder. BLICEr establishes new state-of-the-art results on two standard BLI benchmarks spanning a wide spectrum of diverse languages: it substantially outperforms a series of strong baselines across the board. We also validate the robustness of BLICEr with different CLWEs.","url_abs":"https://arxiv.org/abs/2210.16953v2","url_pdf":"https://arxiv.org/pdf/2210.16953v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"improving-bilingual-lexicon-induction-with-1","repo_url":"https://github.com/cambridgeltl/BLICEr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"bilingual-lexicon-induction","task_name":"Bilingual Lexicon Induction"},{"task_slug":null,"task_name":"Cross Encoder Reranking"},{"task_slug":"cross-lingual-word-embeddings","task_name":"Cross-Lingual Word Embeddings"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"multilingual-nlp","task_name":"Multilingual NLP"},{"task_slug":"multilingual-word-embeddings","task_name":"Multilingual Word Embeddings"},{"task_slug":"pretrained-multilingual-language-models","task_name":"Pretrained Multilingual Language Models"},{"task_slug":"reranking","task_name":"Reranking"},{"task_slug":"word-alignment","task_name":"Word Alignment"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":"word-similarity","task_name":"Word Similarity"},{"task_slug":"word-translation","task_name":"Word Translation"},{"task_slug":"xlm-r","task_name":"XLM-R"}],"methods":[{"method_slug":"cross-encoder-reranking","method_name":"Cross-encoder Reranking"},{"method_slug":"xlm-r","method_name":"XLM-R"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2210.16953","atlas_url":"https://app.syntology.ai/?focus=2210.16953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16953"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cambridgeltl/BLICEr","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":4},"by_repo_kind":{"official":{"samples":4,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"27cf45816a6bce78","entry":"getknn_csls_test","repo":"cambridgeltl/BLICEr","repo_kind":"official","path":"evaluate_ce.py","file_url":"https://github.com/cambridgeltl/BLICEr/blob/HEAD/evaluate_ce.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"27cf45816a6bce78"}},{"code_sha256_prefix":"6957715d8fa5b3ce","entry":"getknn_test","repo":"cambridgeltl/BLICEr","repo_kind":"official","path":"evaluate_ce.py","file_url":"https://github.com/cambridgeltl/BLICEr/blob/HEAD/evaluate_ce.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6957715d8fa5b3ce"}},{"code_sha256_prefix":"add2302ce4389e86","entry":"lexicon_dict2list","repo":"cambridgeltl/BLICEr","repo_kind":"official","path":"evaluate_ce.py","file_url":"https://github.com/cambridgeltl/BLICEr/blob/HEAD/evaluate_ce.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"add2302ce4389e86"}},{"code_sha256_prefix":"f1572a727a130821","entry":"load_embs","repo":"cambridgeltl/BLICEr","repo_kind":"official","path":"utils.py","file_url":"https://github.com/cambridgeltl/BLICEr/blob/HEAD/utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f1572a727a130821"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}