When performing Polarity Detection for different words in a sentence, we need
to look at the words around to understand the sentiment. Massively pretrained
language models like BERT can encode not only just the words in a document but
also the context around the words along with them. This begs the questions,
"Does a pretrain language model also automatically encode sentiment information
about each word?" and "Can it be used to infer polarity towards different
aspects?". In this work we try to answer this question by showing that training
a comparison of a contextual embedding from BERT and a generic word embedding
can be used to infer sentiment. We also show that if we finetune a subset of
weights the model built on comparison of BERT and generic word embedding, it
can get state of the art results for Polarity Detection in Aspect Based
Sentiment Classification datasets.

本文旨在回答是否预训练的语言模型（如 BERT）是否可以自动编码每个单词的情感信息以及是否可以用于推断不同方面的极性，通过展示比较 BERT 和通用单词嵌入的上下文嵌入的训练，我们可以利用其推断情感，并且我们表明，如果我们微调用于 BERT 和通用单词嵌入比较构建的模型的子集权重，可以在基于方面的情感分类数据集中获得最新功能的极性检测。

BERT 是否理解情感？利用上下文和非上下文嵌入之间的比较来改善基于方面的情感模型

Does BERT Understand Sentiment? Leveraging Comparisons Between  Contextual and Non-Contextual Embeddings to Improve Aspect-Based Sentiment  Models

We present a transfer learning system to perform a mixed Spanish-English
sentiment classification task. Our proposal uses the state-of-the-art language
model BERT and embed it within a ULMFiT transfer learning pipeline. This
combination allows us to predict the polarity detection of code-mixed
(English-Spanish) tweets. Thus, among 29 submitted systems, our approach
(referred to as dplominop) is ranked 4th on the Sentimix Spanglish test set of
SemEval 2020 Task 9. In fact, our system yields the weighted-F1 score value of
0.755 which can be easily reproduced -- the source code and implementation
details are made available.

本文介绍了一种混合英语 - 西班牙语情感分类任务的迁移学习系统，使用 BERT 语言模型，在 ULMFiT 迁移学习管道内进行嵌入，允许我们预测 code-mixed (英语 - 西班牙语) 推文的情感极性检测。

基于 Transformer 的鲁棒代码混合情感分类系统：SemEval-2020 任务 9 中的 Palomino-Ochoa

Palomino-Ochoa at SemEval-2020 Task 9: Robust System based on  Transformer for Code-Mixed Sentiment Classification

Sentiments expressed in user-generated short text and sentences are nuanced
by subtleties at lexical, syntactic, semantic and pragmatic levels. To address
this, we propose to augment traditional features used for sentiment analysis
and sarcasm detection, with cognitive features derived from the eye-movement
patterns of readers. Statistical classification using our enhanced feature set
improves the performance (F-score) of polarity detection by a maximum of 3.7%
and 9.3% on two datasets, over the systems that use only traditional features.
We perform feature significance analysis, and experiment on a held-out dataset,
showing that cognitive features indeed empower sentiment analyzers to handle
complex constructs.

本研究提出一种使用读者眼动模式获取认知特征的方法，以增强情感分析和讽刺检测的性能，结果表明：使用这种增强型特征集合可以将极性检测的 F 分数最高提升 3.7％和 9.3％。