Untitled

Abstract

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

An Analysis of Encoder Representations in Transformer-Based Machine Translation

“…Models based on the Transformer architecture (Vaswani et al, 2017) have led to tremendous performance increases in a wide range of downstream tasks (Devlin et al, 2019). Despite these successes, the impact of the suggested parametrization choices, in particular the self-attention mechanism with its large number of attention heads distributed over several layers, has been the subject of many studies following two main lines of research.…”

Recognizing sentences concerning future research from the full text of JASIST

“…In this paper, a deep learning model ‐ BERT ‐ was used. With BERT, pre‐training is already performed on linguistic representation before the training begins (Devlin, Chang, Lee, & Toutanova, ). Our experiments were conducted based on the BERT pre‐training model (multi‐language version).…”

Deep Contextualized Self-training for Low Resource Dependency Parsing

“…The most common pre-training task is language modeling or a closely related variant (McCann et al, 2017;Peters et al, 2018;Devlin et al, 2019;Ziser and Reichart, 2018). The outputs of the pretrained DNN are often referred to as contextualized word embeddings, as these DNNs typically generate a vector embedding for each input word, which takes its context into account.…”

“…We present a novel self-training method, suitable for neural dependency parsing. Our algorithm ( § 4) follows recent work that has demonstrated the power of pre-training for improving DNN models in NLP (Peters et al, 2018;Devlin et al, 2019) and particularly for domain adaptation (Ziser and Reichart, 2018). However, while in previous work a representation model, also known as a contextualized embedding model, is trained on a language modeling related task, our algorithm utilizes a representation model that is trained on sequence prediction tasks derived from the parser's output.…”