| Challenge: | a new method for bilingual terminology extraction is proposed for a source language and a target language. |
| Approach: | They propose to use a bilingual terminology extraction approach for a source language and a target language to extract the terminology for sri lanka. |
| Outcome: | The proposed method extracts terminology for a source language and a target language from it. |
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Towards a unified framework for bilingual terminology extraction of single-word and multi-word terms (C18-1)
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| Challenge: | Existing methods for extracting bilingual terminology from comparable corpora are limited to a set of syntactic patterns. |
| Approach: | They propose a framework for aligning bilingual terms independently of term lengths . they introduce some enhancements to the context-based and neural network based approaches . |
| Outcome: | The proposed framework improves the performance of the context-based and neural network based approaches and can be adapted in specialized domains. |
A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents (L18-1)
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| Challenge: | Terms are notoriously difficult to identify, both automatically and manually. |
| Approach: | They propose a method to annotate terms manually from a comparable corpus . they show that the gold standard provides a tool for evaluation and a rich source of information . |
| Outcome: | The proposed method provides a tool for evaluation and rich source of information about terms. |
Multi-lingual Entity Discovery and Linking (P18-5)
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| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
| Approach: | This tutorial will review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
| Outcome: | The aim of this tutorial is to review the framework of cross-lingual EL and motivate it as a broad paradigm for the Information Extraction task. |
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data (2022.lrec-1)
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| Challenge: | Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and can be used for further NLP applications. |
| Approach: | They propose to fine-tune pre-trained BERT models for automatic Term Extraction (ATE) using cross-lingual and cross-domain transfer learning to extract single and multi-word terms. |
| Outcome: | The proposed models can capture cross-domain and cross-lingual terminologically-marked contexts shared by terms, opening a new design-pattern for ATE. |
Crossing Domains without Labels: Distant Supervision for Term Extraction (2025.emnlp-industry)
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| Challenge: | Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. |
| Approach: | They propose a benchmark spanning seven diverse domains to evaluate ATE performance . they propose psuedo-labels and post-hoc heuristics to ensure generalizability . |
| Outcome: | The proposed model outperforms supervised cross-domain encoder models and few-shot learning baselines on the document- and corpus-levels and its GPT-4o teacher on the benchmark. |
Multilingualization of Medical Terminology: Semantic and Structural Embedding Approaches (2020.lrec-1)
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| Challenge: | Existing methods for multilingual terminology curation are limited as they do not fit the term within existing terminology. |
| Approach: | They propose a method to encode the structural property of a term by aligning embeddings using graph convolutional networks trained from separate languages. |
| Outcome: | The proposed method can encode the structural property of a term by aligning embeddings using graph convolutional networks trained from separate languages. |
Transforming Term Extraction: Transformer-Based Approaches to Multilingual Term Extraction Across Domains (2021.findings-acl)
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| Challenge: | Automated Term Extraction (ATE) is a challenging task, with few exceptions. |
| Approach: | They propose to use a transformer-based term extraction model to extract terms from sentences . they also propose to employ a language model for token classification and a sequence model to reduce sentences to terms . |
| Outcome: | The proposed models outperform baselines on the ATE challenge TermEval 2020 dataset in English, French, and Dutch. |
Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora (C18-1)
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| Challenge: | Recent studies on bilingual lexicon extraction from specialized comparable corpora show differences in performance . lack of large specialized corporan to build efficient representations can be partially explained . |
| Approach: | They propose to use character-based embedding models to combine different embeddable models . they emphasize how character-driven embeddance models outperform other models on quality . |
| Outcome: | The proposed model outperforms other models on quality of extracted bilingual lexicons . comparable corpora are an interesting and practical alternative to parallel corporation . |
word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs (2020.lrec-1)
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| Challenge: | Our dataset provides top-k word translations in 3,564 (directed) language pairs across 62 languages in OpenSubtitles2018. |
| Approach: | They propose a dataset and an open-source Python package for cross-lingual word translations extracted from sentence-level parallel corpora. |
| Outcome: | The proposed bilingual lexicons have high coverage and achieve competitive translation quality for several language pairs. |
MINERS: Multilingual Language Models as Semantic Retrievers (2024.findings-emnlp)
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| Challenge: | Existing benchmarks have evaluated language models to evaluate their performance across a range of embedding tasks. |
| Approach: | They propose a benchmark to evaluate the robustness of multilingual language models in semantic retrieval tasks including bitext mining and classification via retrieval-augmented contexts. |
| Outcome: | The proposed framework evaluates the robustness of multilingual LMs in retrieval tasks across over 200 languages, including extremely low-resource languages in challenging cross-lingual and code-switching settings. |