| Challenge: | In language, a word can contribute a very different meaning depending on the context . lexical ambiguity involves both morphosyntactic and semantic aspects . |
| Approach: | They propose a method to probe hidden representations for lexical and contextual information about words. |
| Outcome: | The proposed method shows that both types of information are represented to a large extent, but there is room for improvement for contextual information. |
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| Challenge: | Few studies have systematically compared LMs’ contextualized word embeddings for languages beyond English. |
| Approach: | They evaluate Spanish ambiguous nouns in context in a suite of Spanish-language monolingual and multilingual BERT-based models. |
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Implicit Representations of Meaning in Neural Language Models (2021.acl-long)
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| Challenge: | Neural language models (NLMs) encode lexical relations and syntactic structure, but their effectiveness is still unclear. |
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Dissecting Contextual Word Embeddings: Architecture and Representation (D18-1)
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| Challenge: | Existing work on learning contextual representations has used LSTM-based biLMs, but there is no reason to believe this is effective. |
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Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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Deep Generative Model for Joint Alignment and Word Representation (N18-1)
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| Challenge: | EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. |
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Analysing Lexical Semantic Change with Contextualised Word Representations (2020.acl-main)
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| Challenge: | Existing studies on lexical semantic change have focused on detecting and characterising word meaning shifts using distributional semantic models. |
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Probing Pretrained Language Models for Lexical Semantics (2020.emnlp-main)
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| Challenge: | Existing studies have focused on morphosyntactic, semantic, and world knowledge, but it remains unclear to what extent LMs derive lexical type-level knowledge from words in context. |
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| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
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Speakers Fill Lexical Semantic Gaps with Context (2020.emnlp-main)
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| Challenge: | Lexical ambiguity is widespread in language, allowing for the reuse of economical word forms and thus making language more efficient. |
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We’re Afraid Language Models Aren’t Modeling Ambiguity (2023.emnlp-main)
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Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah Smith, Yejin Choi
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