Code-switching (CSW) is a common phenomenon among multilingual speakers where
multiple languages are used in a single discourse or utterance. Mixed language
utterances may still contain grammatical errors however, yet most existing
Grammar Error Correction (GEC) systems have been trained on monolingual data
and not developed with CSW in mind. In this work, we conduct the first
exploration into the use of GEC systems on CSW text. Through this exploration,
we propose a novel method of generating synthetic CSW GEC datasets by
translating different spans of text within existing GEC corpora. We then
investigate different methods of selecting these spans based on CSW ratio,
switch-point factor and linguistic constraints, and identify how they affect
the performance of GEC systems on CSW text. Our best model achieves an average
increase of 1.57 $F_{0.5}$ across 3 CSW test sets (English-Chinese,
English-Korean and English-Japanese) without affecting the model's performance
on a monolingual dataset. We furthermore discovered that models trained on one
CSW language generalise relatively well to other typologically similar CSW
languages.

通过生成合成的 CSW GEC 数据集并选择 CSW 比例、转换点因素和语言约束进行研究，我们的研究探索了将 GEC 系统应用于 CSW 文本的方法，最佳模型在多个 CSW 测试集上取得了平均增长 1.57 的 F0.5，同时对单语数据集的性能没有影响，并发现在一个 CSW 语言上训练的模型相对于其他类型相似的 CSW 语言具有较好的泛化能力。

英语学习者的混合语句文法错误修正

Grammatical Error Correction for Code-Switched Sentences by Learners of  English

Human emotion understanding is pivotal in making conversational technology
mainstream. We view speech emotion understanding as a perception task which is
a more realistic setting. With varying contexts (languages, demographics, etc.)
different share of people perceive the same speech segment as a non-unanimous
emotion. As part of the ACM Multimedia 2023 Computational Paralinguistics
ChallengE (ComParE) in the EMotion Share track, we leverage their rich dataset
of multilingual speakers and multi-label regression target of 'emotion share'
or perception of that emotion. We demonstrate that the training scheme of
different foundation models dictates their effectiveness for tasks beyond
speech recognition, especially for non-semantic speech tasks like emotion
understanding. This is a very complex task due to multilingual speakers,
variability in the target labels, and inherent imbalance in the regression
dataset. Our results show that HuBERT-Large with a self-attention-based
light-weight sequence model provides 4.6% improvement over the reported
baseline.

人类情绪理解对于推动对话技术走向主流至关重要。我们视语音情绪理解为一项感知任务，这是一个更加现实的设置。通过在 ACM Multimedia 2023 计算跨语音信息挑战（ComParE）中利用其丰富的多语种演讲者数据集和 ' 情感共享 ' 多标签回归目标，我们展示了不同基础模型的训练方案对于超越语音识别等非语义语音任务（例如情绪理解）的效果产生重要影响，尤其是针对多语种演讲者、目标标签的可变性以及回归数据集中的固有不平衡性，我们的结果表明，基于自注意力的轻量级序列模型 HuBERT-Large 相较于报告的基准线有 4.6% 的改进。

注意力和自监督语音嵌入对非语义语音任务的影响

Effect of Attention and Self-Supervised Speech Embeddings on  Non-Semantic Speech Tasks

Code-switching is a phenomenon of mixing grammatical structures of two or
more languages under varied social constraints. The code-switching data differ
so radically from the benchmark corpora used in NLP community that the
application of standard technologies to these data degrades their performance
sharply. Unlike standard corpora, these data often need to go through
additional processes such as language identification, normalization and/or
back-transliteration for their efficient processing. In this paper, we
investigate these indispensable processes and other problems associated with
syntactic parsing of code-switching data and propose methods to mitigate their
effects. In particular, we study dependency parsing of code-switching data of
Hindi and English multilingual speakers from Twitter. We present a treebank of
Hindi-English code-switching tweets under Universal Dependencies scheme and
propose a neural stacking model for parsing that efficiently leverages
part-of-speech tag and syntactic tree annotations in the code-switching
treebank and the preexisting Hindi and English treebanks. We also present
normalization and back-transliteration models with a decoding process tailored
for code-switching data. Results show that our neural stacking parser is 1.5%
LAS points better than the augmented parsing model and our decoding process
improves results by 3.8% LAS points over the first-best normalization and/or
back-transliteration.

本文研究代码切换数据的句法分析，提出了规范化和反回译模型的解码过程，以及利用词性标注和句法树注释的神经堆叠模型。结果显示，我们的神经叠加分析器比增强分析模型优越 1.5％LAS 点，并且我们的解码过程比第一个最佳规范化和 / 或反回译提高了 3.8％LAS 点。