From Characters to Words: Hierarchical Pre-trained Language Model for Open-vocabulary Language Understanding (2023.acl-long)
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| Challenge: | Current models for natural language understanding require a preprocessing step to convert raw text into discrete tokens. |
| Approach: | They propose a hierarchical open-vocabulary language model that adopts a shallow Transformer architecture to learn word representations from their characters and a deep inter-word Transformer module that contextualizes each word representation by attending to the entire word sequence. |
| Outcome: | The proposed model outperforms baselines on various downstream tasks and is robust to textual corruption and domain shift. |
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| Challenge: | aims to benchmark recent progress in language understanding models that output contextualised representations at the character level. |
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Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel
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| Challenge: | Neural Machine Translation models typically use a fixed-size lexical vocabulary . subword segmentation methods rely on statistical heuristics that lack any linguistic notion . |
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| Challenge: | Existing approaches to train character-level models require very deep architectures that are difficult and slow to train. |
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| Challenge: | Pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations . but these methods split a word into subword units and make it incomplete and fragile . |
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| Challenge: | Recent work shows that deeper character-based neural machine translation models outperform subword-based models. |
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| Challenge: | Recent successful models for document-level understanding have used hierarchical encoding and CRFs to capture dependencies between subsequent labels. |
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