We highlight the challenges faced by non-native speakers when using AI
writing assistants to paraphrase text. Through an interview study with 15
non-native English speakers (NNESs) with varying levels of English proficiency,
we observe that they face difficulties in assessing paraphrased texts generated
by AI writing assistants, largely due to the lack of explanations accompanying
the suggested paraphrases. Furthermore, we examine their strategies to assess
AI-generated texts in the absence of such explanations. Drawing on the needs of
NNESs identified in our interview, we propose four potential user interfaces to
enhance the writing experience of NNESs using AI writing assistants. The
proposed designs focus on incorporating explanations to better support NNESs in
understanding and evaluating the AI-generated paraphrasing suggestions.

本研究通过对 15 个英语水平各异的非母语英语人士的访谈研究，发现他们在使用 AI 写作助手进行改写时往往无法正确评估其生成的文本，主要由于缺乏解释。为此，我们提出了四种用户界面设计，旨在通过提供解释来支持非母语英语人士更好地理解和评估 AI 生成的改写建议。

针对非母语英语使用者的可解释人工智能写作助手

Towards Explainable AI Writing Assistants for Non-native English  Speakers

Non-native speakers show difficulties with spoken word processing. Many
studies attribute these difficulties to imprecise phonological encoding of
words in the lexical memory. We test an alternative hypothesis: that some of
these difficulties can arise from the non-native speakers' phonetic perception.
We train a computational model of phonetic learning, which has no access to
phonology, on either one or two languages. We first show that the model
exhibits predictable behaviors on phone-level and word-level discrimination
tasks. We then test the model on a spoken word processing task, showing that
phonology may not be necessary to explain some of the word processing effects
observed in non-native speakers. We run an additional analysis of the model's
lexical representation space, showing that the two training languages are not
fully separated in that space, similarly to the languages of a bilingual human
speaker.

研究发现，非母语人士在处理口语时存在困难，导致其在词汇记忆的语音编码上不够准确，本研究使用计算模型模拟语音学习，发现有些非母语人士的口音知觉可能是导致这种困难的原因之一。

非母语口语词语处理的音韵模型

A phonetic model of non-native spoken word processing

We present a computational analysis of cognate effects on the spontaneous
linguistic productions of advanced non-native speakers. Introducing a large
corpus of highly competent non-native English speakers, and using a set of
carefully selected lexical items, we show that the lexical choices of
non-natives are affected by cognates in their native language. This effect is
so powerful that we are able to reconstruct the phylogenetic language tree of
the Indo-European language family solely from the frequencies of specific
lexical items in the English of authors with various native languages. We
quantitatively analyze non-native lexical choice, highlighting cognate
facilitation as one of the important phenomena shaping the language of
non-native speakers.

通过大量非母语英语使用者的语料库，研究发现词汇选择受到个人母语的影响，特别是语序和词汇语境，甚至从单个例子出发可以重构印欧语系的语言家族树。

母语同源词对第二语言词汇选择的影响

Native Language Cognate Effects on Second Language Lexical Choice

This paper describes our experiments with automatically identifying native
accents from speech samples of non-native English speakers using low level
audio features, and n-gram features from manual transcriptions. Using a
publicly available non-native speech corpus and simple audio feature
representations that do not perform word/phoneme recognition, we show that it
is possible to achieve close to 90% classification accuracy for this task.
While character n-grams perform similar to speech features, we show that speech
features are not affected by prompt variation, whereas ngrams are. Since the
approach followed can be easily adapted to any language provided we have enough
training data, we believe these results will provide useful insights for the
development of accent recognition systems and for the study of accents in the
context of language learning.

本文通过简单音频和 n-gram 特征，达到了近 90% 的准确识别率，无论问题变化多少，音频特征都不受影响，经验证明这种方法可适用于任何语言的口音识别系统的开发和应用。