| Challenge: | Existing language modeling methods rely on large-scale text data to learn the sequential patterns of words. |
| Approach: | They propose to use sememes to represent the implicit semantics behind words for language modeling . they propose to employ sememe-driven language models to fine-grained semem-level semantics . |
| Outcome: | Experiments on language modeling and the downstream application of headline generation show the effectiveness of SDLM. |
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| Challenge: | Generating speech through a pipeline that operates at the text level typically loses nuances, intonations, and non-verbal vocalizations. |
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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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PolyLM: Learning about Polysemy through Language Modeling (2021.eacl-main)
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Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work (2022.aacl-tutorials)
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| Challenge: | Existing language models are trained to optimize unsupervised objectives on text . despite their centrality, current models do not represent natural language semantics well . |
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
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Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling (2022.naacl-main)
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| Challenge: | Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way. |
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