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Keyword: XLNet ×Clear all

4 claims from 2 papers

1. Computer Science › Topic Modeling

   XLNet: Generalized Autoregressive Pretraining for Language Understanding

   Yang, Zihang, Yang, Carbonell, Salakhutdinov and Le · arXiv (Cornell University) · 2019

   The paper proposes XLNet, a pretraining method combining autoregressive modelling with bidirectional context, and reports that it outperforms BERT on 20 language tasks.

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   1. UncheckedUnder comparable experiment settings, the XLNet language model scored higher than BERT on 20 tasks, often by a large margin, per the paper.“Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking.”

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. 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=…”
   2. 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.”
   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.”