| Challenge: | Existing rules that model binary relations such as X treat Y are based on the distributional hypothesis of Harris (1954). |
| Approach: | They propose to implement the distributional hypothesis using contextualized embeddings provided by a transformer-network-based language model to measure the similarity between slots instead of lexical overlap. |
| Outcome: | The proposed approach outperforms the original DIRT algorithm in the question answering-based evaluation. |
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| Challenge: | Contextual embeddings have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity. |
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Can Transformers Reason in Fragments of Natural Language? (2022.emnlp-main)
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| Challenge: | Recent work on natural language inference has identified two strands of research . |
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How to Dissect a Muppet: The Structure of Transformer Embedding Spaces (2022.tacl-1)
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| Challenge: | Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm . a novel decomposition of Transformer output embeddables is demonstrated . |
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Quantifying Compositionality of Classic and State-of-the-Art Embeddings (2025.findings-emnlp)
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| Challenge: | Static word embeddings make strong claims about compositionality, but the SOTA generative models go too far in the other direction. |
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| Challenge: | Experimental results show that unified model outperforms other models that treat encoding and matching separately. |
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Too Much in Common: Shifting of Embeddings in Transformer Language Models and its Implications (2021.naacl-main)
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Do Transformer Modifications Transfer Across Implementations and Applications? (2021.emnlp-main)
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Sharan Narang, Hyung Won Chung, Yi Tay, Liam Fedus, Thibault Fevry, Michael Matena, Karishma Malkan, Noah Fiedel, Noam Shazeer, Zhenzhong Lan, Yanqi Zhou, Wei Li, Nan Ding, Jake Marcus, Adam Roberts, Colin Raffel
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Unravelling the Logic: Investigating the Generalisation of Transformers in Numerical Satisfiability Problems (2025.acl-long)
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| Challenge: | Transformer models exhibit minimal scale and noise invariance, along with limited vocabulary and number invariancy. |
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When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks (2022.emnlp-main)
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| Challenge: | Recent benchmarks like ReaSCAN use navigation tasks grounded in a grid world to assess whether neural models exhibit compositional behaviour. |
| Approach: | They propose a transformer-based model that outperforms specialized architectures on ReaSCAN and a modified version of gSCAN to test their performance. |
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