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Keyword: BERT ×Clear all

8 claims from 4 papers

1. Computer Science › Topic Modeling

   Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

   池谷, Tinn, Cheng et al. · ACM Transactions on Computing for Healthcare · 2021

   The paper compiles a biomedical NLP benchmark and reports that language models pretrained from scratch on biomedical text reach new state-of-the-art results across a wide range of tasks.

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   1. UncheckedFor fields with plenty of unlabelled text, like biomedicine, training language models from scratch gave substantial gains over adapting general-domain models.“In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.”
   2. UncheckedWith BERT models, some common practices, such as complex tagging schemes for named entity recognition, are found to be unnecessary.“Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition.”

2. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

   Elnaggar, Heinzinger, Dallago et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2021

   The authors trained six language models on huge protein sequence sets and showed their embeddings, used alone, could predict protein structure and location, with the best beating methods that need sequence alignments.

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   1. UncheckedSimplifying protein language model embeddings from unlabelled sequences showed they captured some biophysical features of proteins.“Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences.”
   2. UncheckedProtein language model embeddings alone, used as input, predicted secondary structure, cell location and membrane status with the stated accuracies.“We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=…”
   3. UncheckedThe best ProtTrans embeddings (ProtT5) predicted protein secondary structure better than the prior best method without alignments or evolutionary information.“For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches.”

3. Computer Science › Topic Modeling

   Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

   池谷, Tinn, Cheng et al. · ACM Transactions on Computing for Healthcare · 2021

   The paper argues that biomedical language models pretrained from scratch on domain text outperform continually pretrained general models, and releases a benchmark (BLURB), models and a leaderboard.

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   1. UncheckedFor fields with plenty of unlabelled text, such as biomedicine, training a language model from scratch gives substantial gains over adapting a general-domain model.“In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.”

4. Computer Science › Topic Modeling

   On the Role of Bidirectionality in Language Model Pre-Training

   Artetxe, Du, Goyal, Zettlemoyer and Stoyanov · arXiv (Cornell University) · 2022

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   1. Unchecked“We find that the optimal configuration is largely application-dependent (e.g., bidirectional attention is beneficial for fine-tuning and infilling, but harmful for next token prediction and zero-shot priming).”
   2. Unchecked“We train models with up to 6.7B parameters, and find differences to remain consistent at scale.”