Can the Transformer Learn Nested Recursion with Symbol Masking? (2021.findings-acl)
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| Challenge: | Existing studies on self-attention models show they can generalise to context-free languages . |
| Approach: | They use encoder-only models to train to generalise nested symbols . they find that the predictions made correspond to a simple parenthesis counting strategy . |
| Outcome: | The proposed model can generalise to nested structures at higher nesting depth and with a push-down automaton. |
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Do Transformers Parse while Predicting the Masked Word? (2023.emnlp-main)
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| Challenge: | Existing studies show that pre-trained language models encode linguistic structures like parse trees while being trained unsupervised. |
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Syntax-Based Attention Masking for Neural Machine Translation (2021.naacl-srw)
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Can Transformers Process Recursive Nested Constructions, Like Humans? (2022.coling-1)
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| Challenge: | Existing studies on LSTMs have not revealed their ability to model syntactic properties. |
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Can Transformers Learn n-gram Language Models? (2024.emnlp-main)
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| Challenge: | Existing work has tested transformers' ability to represent formal languages, but language models are not classifiers of strings but rather distributions over them. |
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| Challenge: | Recent studies show that self-attention patterns in trained models contain a majority of non-linguistic regularities. |
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