Challenge: a new interface for word translation is proposed to explore the semantic configurations of words in multiple languages at once.
Approach: They propose a web interface for word translation that points to the semantic configurations of many words in multiple languages at once.
Outcome: The proposed interface is available as a web application on seven language pairs . it points to the semantic configurations of many words in multiple languages at once .

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Lexical Resource Mapping via Translations (2022.lrec-1)

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Challenge: a lexical resource associates words with concepts in multiple languages, which makes it difficult to combine information from multiple resources.
Approach: They propose a translation-based approach to mapping lexical resources . they use word-concept pairs to align WordNet/BabelNet to CLICS and OmegaWiki .
Outcome: The proposed method achieves state-of-the-art accuracy without other sources of knowledge . it can be framed as word sense disambiguation, and it can improve on existing methods .
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.
Some Issues with Building a Multilingual Wordnet (2020.lrec-1)

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Challenge: Notable extensions include: confidence, corpus frequency, orthographic variants, lexicalized and non-lexicalised synsets and lemmas, new parts of speech, and more.
Approach: They propose to integrate a new open multilingual wordnet format that tests the extensions introduced by the new format and integrates a set of tools to ensure the integrity of the Collaborative Interlingual Index.
Outcome: The proposed format integrates a set of tools that test the extensions while ensuring the integrity of the Collaborative Interlingual Index (CILI).
Frame Semantics across Languages: Towards a Multilingual FrameNet (C18-3)

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Challenge: This workshop will present current research on aligning Frame Semantic resources across languages . resources based on FrameNet have been created for roughly a dozen languages based upon Fillmore's Frame Sementics .
Approach: This workshop will present current research on aligning Frame Semantic resources across languages . resources based on FrameNet have been created for roughly a dozen languages based upon Fillmore's Frame Sementics .
Outcome: This workshop will present current research on aligning Frame Semantic resources across languages and automatic frame semantic parsing in English and other languages.
KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus (L18-1)

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Challenge: Cross-lingual word embeddings are representations of words across languages in a shared continuous vector space.
Approach: They propose a multilingual word embedding corpus which is acquired by neural machine translation and is based on monolingual data.
Outcome: The proposed method is competitive with existing methods but on the cross-lingual document classification task, it obtains the best figures.
A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing (2022.aacl-tutorials)

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Challenge: a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics .
Approach: This tutorial reviews recent efforts to shed light on meaning in NLP . it will focus on three key open problems in lexical and sentence-level semantics .
Outcome: This tutorial reviews recent efforts to shed light on meaning in NLP . it focuses on three key open problems in lexical and sentence-level semantics .
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.
MultiLexBATS: Multilingual Dataset of Lexical Semantic Relations (2024.lrec-main)

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Challenge: Prior work has focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs) with some exceptions.
Approach: They propose to use a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian as an experiment on cross-lingual transfer of relational knowledge.
Outcome: The proposed dataset is adapted from a BATS-based dataset in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian.
Transfer of Frames from English FrameNet to Construct Chinese FrameNet: A Bilingual Corpus-Based Approach (L18-1)

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Challenge: Current publicly available Chinese FrameNet has a relatively low coverage of frames and lexical units compared with other languages.
Approach: They propose an automatic way to construct Chinese FrameNet using a sentence-aligned English-Chinese bilingual corpus.
Outcome: The proposed resource can provide frame recommendations acceptable by annotators.
Adapting Entities across Languages and Cultures (2021.findings-emnlp)

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Challenge: a structured knowledge base adapts named entities using their shared properties.
Approach: They propose automatic methods to adapt named entities using shared properties . they compare them to human adaptations using a new dataset of human adaptation data .
Outcome: The proposed methods compare to human adaptations using a new dataset.

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