How Context Affects Language Models' Factual Predictions
| Autor: | Petroni, Fabio, Lewis, Patrick, Piktus, Aleksandra, Rocktäschel, Tim, Wu, Yuxiang, Miller, Alexander H., Riedel, Sebastian |
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| Rok vydání: | 2020 |
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| Druh dokumentu: | Working Paper |
| Popis: | When pre-trained on large unsupervised textual corpora, language models are able to store and retrieve factual knowledge to some extent, making it possible to use them directly for zero-shot cloze-style question answering. However, storing factual knowledge in a fixed number of weights of a language model clearly has limitations. Previous approaches have successfully provided access to information outside the model weights using supervised architectures that combine an information retrieval system with a machine reading component. In this paper, we go a step further and integrate information from a retrieval system with a pre-trained language model in a purely unsupervised way. We report that augmenting pre-trained language models in this way dramatically improves performance and that the resulting system, despite being unsupervised, is competitive with a supervised machine reading baseline. Furthermore, processing query and context with different segment tokens allows BERT to utilize its Next Sentence Prediction pre-trained classifier to determine whether the context is relevant or not, substantially improving BERT's zero-shot cloze-style question-answering performance and making its predictions robust to noisy contexts. Comment: accepted at AKBC 2020 |
| Databáze: | arXiv |
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