Papers by Carlos Aspillaga
Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks (2020.lrec-1)
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| Challenge: | Existing models are weak and take advantage of failures and errors in datasets to improve performance. |
| Approach: | They evaluate three Transformer-based models in Natural Language Inference and Question Answering tasks to see if they are more robust or have the same flaws as their predecessors. |
| Outcome: | The proposed models outperform recurrent neural network models to stress tests on both NLI and QA tasks. |
Inspecting the concept knowledge graph encoded by modern language models (2021.findings-acl)
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| Challenge: | Pre-trained language models are used to solve tasks such as summarization and information retrieval. |
| Approach: | They propose to use word embeddings, text generators, context encoders to extract underlying knowledge graphs of nine influential language models. |
| Outcome: | The proposed model is able to encode word embeddings, text generators, and context encoders, but suffers from several inaccuracies. |