Mittens: an Extension of GloVe for Learning Domain-Specialized Representations (N18-2)
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| Challenge: | We show that the resulting representations can lead to faster learning and better results on a variety of tasks. |
| Approach: | They propose a simple extension of the GloVe representation learning model that starts with general-purpose representations and updates them based on specialized data sets. |
| Outcome: | The proposed model synthesizes general-purpose representations with specialized data while remaining faithful to the original space. |
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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 . |
| Approach: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and then move to other types of embeddable vectors . |
| Outcome: | This tutorial will provide a high-level synthesis of the main embedding techniques in NLP . it will start with word embedds and move to other types of embeddable representations . |
Domain Pre-training Impact on Representations (2025.findings-emnlp)
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| Challenge: | a small, specialized corpus can produce effective representations, but the quality of pre-training is not affected by the choice of corpus. |
| Approach: | They focus on the representation quality achieved through pre-training alone . |
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Academics Can Contribute to Domain-Specialized Language Models (2024.emnlp-main)
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Mark Dredze, Genta Winata, Prabhanjan Kambadur, Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, David Rosenberg, Sebastian Gehrmann
| Challenge: | Commercially available models dominate academic leaderboards, focusing on creating and adapting general-purpose models . however, general- purpose models often underperform in specialized domains, and domain-specific models yield superior results. |
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exBERT: Extending Pre-trained Models with Domain-specific Vocabulary Under Constrained Training Resources (2020.findings-emnlp)
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| Challenge: | Existing methods to train pre-trained models with limited corpus and computational resources are limited by the complexity of the training resources. |
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The Trade-offs of Domain Adaptation for Neural Language Models (2022.acl-long)
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| Challenge: | Neural Language Models (LMs) trained on large generic training sets have been shown to be effective at adapting to smaller, specific target domains for language modeling and other downstream tasks. |
| Approach: | They propose a framework for a Neural Language Models (LM) to be presented in a common framework. |
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A Primer in BERTology: What We Know About How BERT Works (2020.tacl-1)
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| Challenge: | a new study examines the current state of knowledge about the BERT model . the model is a stack of transformer encoder layers that are based on multiple self-attention ''heads'' |
| Approach: | They present a survey of over 150 studies of the popular Transformer-based model BERT . they discuss the current state of knowledge about how BERT works and how it is represented . |
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Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019) (D19-61)
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| Challenge: | EMNLP-IJCNLP 2019 Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing takes place in Hong Kong, China . |
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Leveraging Meta-Embeddings for Bilingual Lexicon Extraction from Specialized Comparable Corpora (C18-1)
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| Challenge: | Recent studies on bilingual lexicon extraction from specialized comparable corpora show differences in performance . lack of large specialized corporan to build efficient representations can be partially explained . |
| Approach: | They propose to use character-based embedding models to combine different embeddable models . they emphasize how character-driven embeddance models outperform other models on quality . |
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Self-Specialization: Uncovering Latent Expertise within Large Language Models (2024.findings-acl)
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Junmo Kang, Hongyin Luo, Yada Zhu, Jacob Hansen, James Glass, David Cox, Alan Ritter, Rogerio Feris, Leonid Karlinsky
| Challenge: | Recent studies have demonstrated the effectiveness of self-alignment in which a large language model is aligned to follow general instructions using instructional data generated from the model itself. |
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Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)
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| Challenge: | general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data. |
| Approach: | This tutorial aims to examine the capabilities of general-purpose large language models . authors discuss adaptation of LLMs to address conflicts, defense against attacks . |
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