Bangla (Bengali) is the fifth most spoken language globally and, yet, the
problem of automatic grammar correction in Bangla is still in its nascent
stage. This is mostly due to the need for a large corpus of grammatically
incorrect sentences, with their corresponding correct counterparts. The present
state-of-the-art techniques to curate a corpus for grammatically wrong
sentences involve random swapping, insertion and deletion of words.
However,these steps may not always generate grammatically wrong sentences in
Bangla. In this work, we propose a pragmatic approach to generate grammatically
wrong sentences in Bangla. We first categorize the different kinds of errors in
Bangla into 5 broad classes and 12 finer classes. We then use these to generate
grammatically wrong sentences systematically from a correct sentence. This
approach can generate a large number of wrong sentences and can, thus, mitigate
the challenge of lacking a large corpus for neural networks. We provide a
dataset, Vaiyakarana, consisting of 92,830 grammatically incorrect sentences as
well as 18,426 correct sentences. We also collected 619 human-generated
sentences from essays written by Bangla native speakers. This helped us to
understand errors that are more frequent. We evaluated our corpus against
neural models and LLMs and also benchmark it against human evaluators who are
native speakers of Bangla. Our analysis shows that native speakers are far more
accurate than state-of-the-art models to detect whether the sentence is
grammatically correct. Our methodology of generating erroneous sentences can be
applied for most other Indian languages as well.

为缺乏巨大的语料库提供了一个实用的方法来生成孟加拉语的语法错误句子，该方法分类了孟加拉语中的不同错误类型，并从正确的句子中系统地生成错误的句子，该方法提供了一个包含 92,830 个语法错误句子和 18,426 个正确句子的数据集，该数据集还通过收集 619 个孟加拉语母语者撰写的文章中的句子，帮助我们了解更常见的错误。通过与神经模型、LLM 和母语为孟加拉语的人类评估者进行了对比评估，结果表明母语为孟加拉语的人类评估者比先进的模型更准确地检测句子的语法正确性。这种生成错误句子的方法也可以应用于其他印度语言。

VAIYAKARANA：孟加拉语自动语法纠正的基准

VAIYAKARANA : A Benchmark for Automatic Grammar Correction in Bangla

This paper reports on progress towards building an online language learning
tool to provide learners with conversational experience by using dialog systems
as conversation practice partners. Our system can adapt to users' language
proficiency on the fly. We also provide automatic grammar error feedback to
help users learn from their mistakes. According to our first adopters, our
system is entertaining and useful. Furthermore, we will provide the learning
technology community a large-scale conversation dataset on language learning
and grammar correction. Our next step is to make our system more adaptive to
user profile information by using reinforcement learning algorithms.

本研究旨在构建一种在线语言学习工具，通过使用对话系统作为实践对话伙伴，为学习者提供对话体验。我们的系统可以根据用户的语言熟练程度进行自适应，并且提供自动语法错误反馈来帮助用户从他们的错误中学习。根据我们的初步用户反馈，我们的系统既实用又有趣。此外，我们还将为学习技术社区提供一个大规模的关于语言学习和语法更正的对话数据集。下一步，我们将使用强化学习算法来使我们的系统更加适应用户个人信息。

使用聊天机器人教授语言

Using Chatbots to Teach Languages

Many common sequential data sources, such as source code and natural
language, have a natural tree-structured representation. These trees can be
generated by fitting a sequence to a grammar, yielding a hierarchical ordering
of the tokens in the sequence. This structure encodes a high degree of
syntactic information, making it ideal for problems such as grammar correction.
However, little work has been done to develop neural networks that can operate
on and exploit tree-structured data. In this paper we present the
Tree-Transformer \textemdash{} a novel neural network architecture designed to
translate between arbitrary input and output trees. We applied this
architecture to correction tasks in both the source code and natural language
domains. On source code, our model achieved an improvement of $25\%$
$\text{F}0.5$ over the best sequential method. On natural language, we achieved
comparable results to the most complex state of the art systems, obtaining a
$10\%$ improvement in recall on the CoNLL 2014 benchmark and the highest to
date $\text{F}0.5$ score on the AESW benchmark of $50.43$.

本文提出 Tree-Transformer 神经网络架构，可用于树状结构数据的矫正任务，并在源代码和自然语言两个领域中分别取得了 25% 和 10% 的改进。