| Challenge: | Existing approaches combine word embeddings with character-level features to model additional features such as subword structures and meaning ambiguity. |
| Approach: | They present FLAIR, an NLP framework that enables embeddings of word and document data . they propose a hierarchical learning architecture that concatenates output states of a character-level CNN or RNN with the output states from a task data. |
| Outcome: | The proposed framework hides embedding-specific engineering complexity and allows researchers to "mix and match" various embeddables with little effort. |
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Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
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Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
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| Challenge: | Existing resources for standardized, easily accessible IGT data limit their applicability to linguistic research. |
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DocSplit: Simple Contrastive Pretraining for Large Document Embeddings (2023.findings-emnlp)
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Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models (2021.emnlp-main)
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| Challenge: | Recent studies have shown that powerful pre-trained language models can be fooled by small perturbations or intentional attacks. |
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A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)
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Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu
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