Do Neural Language Models Inferentially Compose Concepts the Way Humans Can? (2024.lrec-main)
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| Challenge: | a new study shows that language models and humans may rely on different approaches to represent and compose lexical items across sentence structure. |
| Approach: | They propose to use a dataset to test the performance of neural language models and humans on inferentially driven conceptual compositions. |
| Outcome: | The proposed model elicits probability estimates for a noun in a minimally composed phrase . RoBERTa, BERT-large, and GPT-2 exhibited the closest resemblance to human responses . |
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| Challenge: | Compositionality is a hallmark of human language, but many phrases are non-compositional . a study by a team of researchers shows that LMs may not be able to distinguish between compositional and non-composable phrases. |
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The Paradox of the Compositionality of Natural Language: A Neural Machine Translation Case Study (2022.acl-long)
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| Challenge: | Obtaining human-like performance in NLP is often argued to require compositional generalisation. |
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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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| Challenge: | Compositionality is a core property of natural language, and it is regarded as a key goal for modern NLP systems. |
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| Challenge: | Existing computational metric to quantify extent of meaning composition is lacking . despite extensive neurolinguistic research, understanding how meaning is constructed is difficult . |
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Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language? (2020.acl-main)
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| Challenge: | Despite the success of language models using neural networks, it remains unclear to what extent neural models have the generalization ability to perform inferences. |
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| Challenge: | Despite the popularity of transformer-based models, little is known about how they represent compound words and whether they are compositional. |
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Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
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Do Neural Language Models Show Preferences for Syntactic Formalisms? (2020.acl-main)
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| Challenge: | Recent work on interpretability of deep neural language models concludes that many properties of natural language syntax are encoded in their representational spaces. |
| Approach: | They propose to examine whether syntactic structure adheres to a surface-syntactical or deep syntaktic style of analysis. |
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