Being able to parse code-switched (CS) utterances, such as Spanish+English or
Hindi+English, is essential to democratize task-oriented semantic parsing
systems for certain locales. In this work, we focus on Spanglish
(Spanish+English) and release a dataset, CSTOP, containing 5800 CS utterances
alongside their semantic parses. We examine the CS generalizability of various
Cross-lingual (XL) models and exhibit the advantage of pre-trained XL language
models when data for only one language is present. As such, we focus on
improving the pre-trained models for the case when only English corpus
alongside either zero or a few CS training instances are available. We propose
two data augmentation methods for the zero-shot and the few-shot settings:
fine-tune using translate-and-align and augment using a generation model
followed by match-and-filter. Combining the few-shot setting with the above
improvements decreases the initial 30-point accuracy gap between the zero-shot
and the full-data settings by two thirds.

本研究的重点在于 Spanglish，提出了两种数据增强方法解决 CS 样本不足问题，结合少样本的情况，使零样本和全数据的准确度差距缩小了三分之二。

El Volumen Louder Por Favor: 任务导向的语义解析中的语码转换

El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic  Parsing

In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment
Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our
Hinglish (Hindi-English) and Spanglish (Spanish-English) corpora annotated with
word-level language identification and sentence-level sentiment labels. These
corpora are comprised of 20K and 19K examples, respectively. The sentiment
labels are - Positive, Negative, and Neutral. SentiMix attracted 89 submissions
in total including 61 teams that participated in the Hinglish contest and 28
submitted systems to the Spanglish competition. The best performance achieved
was 75.0% F1 score for Hinglish and 80.6% F1 for Spanglish. We observe that
BERT-like models and ensemble methods are the most common and successful
approaches among the participants.

本文介绍了 SemEval-2020 任务 9 关于混合代码推文情感分析（SentiMix 2020）的结果，释放和描述了标记有单词级别语言识别和句子级别情感标签的 Hinglish（印地语 - 英语）和 Spanglish（西班牙语 - 英语）语料库。最好的表现是在 Hinglish 和 Spanglish 分别获得了 75.0％ F1 得分和 80.6％ F1 得分。观察到在比赛者中 BERT-like 模型和集合方法是最常见和成功的方法。

SemEval-2020 任务 9：代码混合推文的情感分析概述

SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets

The growing popularity and applications of sentiment analysis of social media
posts has naturally led to sentiment analysis of posts written in multiple
languages, a practice known as code-switching. While recent research into
code-switched posts has focused on the use of multilingual word embeddings,
these embeddings were not trained on code-switched data. In this work, we
present word-embeddings trained on code-switched tweets, specifically those
that make use of Spanish and English, known as Spanglish. We explore the
embedding space to discover how they capture the meanings of words in both
languages. We test the effectiveness of these embeddings by participating in
SemEval 2020 Task 9: ~\emph{Sentiment Analysis on Code-Mixed Social Media
Text}. We utilised them to train a sentiment classifier that achieves an F-1
score of 0.722. This is higher than the baseline for the competition of 0.656,
with our team (codalab username \emph{francesita}) ranking 14 out of 29
participating teams, beating the baseline.

本文介绍了一种基于 Spanglish 语料库训练的词向量模型，用于混合代码的文本的情感分析，并在 SemEval 2020 任务上取得了较好的效果。