Papers with English Wiktionary
Injecting Wiktionary to improve token-level contextual representations using contrastive learning (2024.eacl-short)
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| Challenge: | lexical semantics tasks require contextual word embeddings that are not blind to context, despite the fact that vectors of the same meaning are too different. |
| Approach: | They propose to fine-tune pre-trained language models by using automatically self-augmented examples to target contextual word embeddings. |
| Outcome: | The proposed method achieves significant improvements on the original WiC test set and in two new tests. |
Wiktextract: Wiktionary as Machine-Readable Structured Data (2022.lrec-1)
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| Challenge: | Unlike previous Wiktionary extractions, the new extractor, Wiktextract, fully interprets and expands templates and Lua modules in Wiktionaries. |
| Approach: | They propose a machine-readable structured version of Wiktionary that interprets and expands templates and Lua modules. |
| Outcome: | The extracted data is multilingual and includes lemmas, inflected forms, translations, etymology, usage examples, pronunciations, and various morphological, syntactic, semantic, topical, and dialectal annotations. |
NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning (2026.acl-long)
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| Challenge: | Neologism-aware machine translation aims to translate source sentences containing neologismes into target languages. |
| Approach: | They propose an agentic framework for neologism-aware machine translation equipped with a Wiktionary-based search toolkit. |
| Outcome: | The proposed framework is based on a Wiktionary-based search toolkit and a dedicated dataset for neologism-aware machine translation. |
Wikinflection Corpus: A (Better) Multilingual, Morpheme-Annotated Inflectional Corpus (2020.lrec-1)
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| Challenge: | Inflectional corpora with annotated morpheme boundaries are scarce in the NLP community . a generated, multilingual inflectional lexicon with morphological features is not as good as UniMorph's . |
| Approach: | They evaluate a multilingual inflectional corpus with morpheme boundaries from the English Wiktionary and the UniMorph project's inflection corpus. |
| Outcome: | The generated Wikinflection corpus is not as good as UniMorph's, but extracts significant amount of words from the intersection of the two corpora. |