BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

本文是对BERT本文的翻译和名词透析

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova (Google AI Language)

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 pre-train deep bidirectional representations from the 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 task-specific 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 points absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 points absolute improvement).

名词透析

Transformer: 一种语言表示模型
empirically: by means of observation or experience rather than theory or pure logic.

Introduction

ChangeLog

2022/1/10 20:32 未完待续……