| Challenge: | a new paper focuses on temporal grounding of time expressions to specific hours in the day . we propose language-agnostic methods for mapping time expression to specific times . cultural differences can cause variation in interpretation of time-specific expressions . |
| Approach: | They propose to use language-agnostic methods to map time expressions to specific hours . they use a time expression that is interpreted by different people . |
| Outcome: | The proposed method achieves promising results on gold standard annotations for 27 languages. |
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| Challenge: | XLM-R is a multilingual language model for temporal relation classification between events in four languages. |
| Approach: | They propose to use a multilingual language model for temporal relation classification between events in four languages to obtain contextualized embeddings. |
| Outcome: | The proposed model outperforms state-of-the-art models in obtaining competitive results against state- of-the art systems, but lacks suitable encoded information to address this task. |
Multilingual Normalization of Temporal Expressions with Masked Language Models (2023.eacl-main)
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| Challenge: | Existing methods for normalizing temporal expressions are rule-based, which severely limits the applicability in multilingual settings. |
| Approach: | They propose a neural method for normalizing temporal expressions based on masked language modeling and a slot-based prediction scheme for context-independent representations. |
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Creative and Context-Aware Translation of East Asian Idioms with GPT-4 (2024.findings-emnlp)
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| Challenge: | figurative language is a challenge for human translators, who often choose a context-aware translation . a set of commonly used idioms condenses its figurativ meaning into a few characters . |
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Measuring and Modeling Language Change (N19-5)
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| Challenge: | This tutorial will help researchers answer questions fundamental to the social sciences and humanities . |
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Mining Cross-Cultural Differences and Similarities in Social Media (P18-1)
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| Challenge: | a new paper examines the problem of computing cross-cultural differences and similarities in natural language understanding . cross-culture differences are important for cross-lingual research, especially in social media . |
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The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)
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| Challenge: | Until recently, language descriptions were available in paper form only, with indexes as the only search aid. |
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
| Approach: | They propose a taxonomy of methods to improve cross-lingual alignment . they argue that an effective trade-off between language-neutral and language-specific information is key . |
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Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction (2021.naacl-main)
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| Challenge: | Scholarly work in this area uses toy worlds and synthetic linguistic data, but grounded language learning offers several practical and scientific advantages. |
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When Time Makes Sense: A Historically-Aware Approach to Targeted Sense Disambiguation (2021.findings-acl)
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Kaspar Beelen, Federico Nanni, Mariona Coll Ardanuy, Kasra Hosseini, Giorgia Tolfo, Barbara McGillivray
| Challenge: | a new paper examines whether making NLP models sensitive to time improves their performance . timesensitive Sense Disambiguation is a variation on Word Sense disambiguation . authors present a task to determine whether a token in a text is related to a specific sense . |
| Approach: | They propose a task to determine whether a token in a text is related to a specific sense of a lemma. |
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DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages (2021.emnlp-main)
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| Challenge: | Existing methods for graded contextual word meaning annotation have not been implemented yet. |
| Approach: | They propose a multi-round incremental annotation process and a clustering algorithm to group usages into senses to create a large-scale dataset. |
| Outcome: | The proposed method is the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments. |