Everyone wants to write beautiful and correct text, yet the lack of language
skills, experience, or hasty typing can result in errors. By employing the
recent advances in transformer architectures, we construct a grammatical error
correction model for Lithuanian, the language rich in archaic features. We
compare subword and byte-level approaches and share our best trained model,
achieving F$_{0.5}$=0.92, and accompanying code, in an online open-source
repository.

本研究使用 transformer 架构构建一个适用于具有古老特征的立陶宛语的语法错误纠正模型，并比较了子词和字节级方法，公开了 F$_{0.5}$=0.92 的最佳模型及其代码。

走向立陶宛语法错误修正

Towards Lithuanian grammatical error correction

Representation learning is the foundation of machine reading comprehension.
In state-of-the-art models, deep learning methods broadly use word and
character level representations. However, character is not naturally the
minimal linguistic unit. In addition, with a simple concatenation of character
and word embedding, previous models actually give suboptimal solution. In this
paper, we propose to use subword rather than character for word embedding
enhancement. We also empirically explore different augmentation strategies on
subword-augmented embedding to enhance the cloze-style reading comprehension
model reader. In detail, we present a reader that uses subword-level
representation to augment word embedding with a short list to handle rare words
effectively. A thorough examination is conducted to evaluate the comprehensive
performance and generalization ability of the proposed reader. Experimental
results show that the proposed approach helps the reader significantly
outperform the state-of-the-art baselines on various public datasets.

本文提出了一种使用子词而非字符的词嵌入增强方法，以增强阅读理解模型。 在不同的扩充策略下，该方法在公共数据集上显着优于现有的基线模型。

基于子词增强嵌入的填空阅读理解

Subword-augmented Embedding for Cloze Reading Comprehension

We describe a new challenge aimed at discovering subword and word units from
raw speech. This challenge is the followup to the Zero Resource Speech
Challenge 2015. It aims at constructing systems that generalize across
languages and adapt to new speakers. The design features and evaluation metrics
of the challenge are presented and the results of seventeen models are
discussed.

本文介绍了一项新挑战，旨在从原始语音中发现子词和单词单位。该挑战旨在构建跨语言的系统，并适应新的讲话者，介绍了挑战的设计特点和评价指标，并讨论了十七个模型的结果。