Precise answers are extracted from a text for a given input question in a
question answering system. Marathi question answering system is created in
recent studies by using ontology, rule base and machine learning based
approaches. Recently transformer models and transfer learning approaches are
used to solve question answering challenges. In this paper we investigate
different transformer models for creating a reading comprehension-based Marathi
question answering system. We have experimented on different pretrained Marathi
language multilingual and monolingual models like Multilingual Representations
for Indian Languages (MuRIL), MahaBERT, Indic Bidirectional Encoder
Representations from Transformers (IndicBERT) and fine-tuned it on a Marathi
reading comprehension-based data set. We got the best accuracy in a MuRIL
multilingual model with an EM score of 0.64 and F1 score of 0.74 by fine tuning
the model on the Marathi dataset.

使用不同的 Transformer 模型，我们创建了一个基于阅读理解的马拉提语问答系统，并在马拉提语阅读理解数据集上通过对 MuRIL 多语言模型进行微调，获得了最佳准确性，EM 得分为 0.64，F1 得分为 0.74。

低资源印度语言马拉地问题回答的深度学习

Question answering using deep learning in low resource Indian language  Marathi

Social media often serves as a breeding ground for various hateful and
offensive content. Identifying such content on social media is crucial due to
its impact on the race, gender, or religion in an unprejudiced society.
However, while there is extensive research in hate speech detection in English,
there is a gap in hateful content detection in low-resource languages like
Bengali. Besides, a current trend on social media is the use of Romanized
Bengali for regular interactions. To overcome the existing research's
limitations, in this study, we develop an annotated dataset of 10K Bengali
posts consisting of 5K actual and 5K Romanized Bengali tweets. We implement
several baseline models for the classification of such hateful posts. We
further explore the interlingual transfer mechanism to boost classification
performance. Finally, we perform an in-depth error analysis by looking into the
misclassified posts by the models. While training actual and Romanized datasets
separately, we observe that XLM-Roberta performs the best. Further, we witness
that on joint training and few-shot training, MuRIL outperforms other models by
interpreting the semantic expressions better. We make our code and dataset
public for others.

本研究针对 Bengali 等低资源语言在社交媒体上的仇恨言论进行研究，开发了一个定量数据集并建立了基线模型和一些额外的方法以更好地进行分类。在分析误分类之后，我们确认 XLM-Roberta 在单独训练 actual 或 rominized 数据集时表现最好。而在联合训练和小样本训练中，MuRIL 通过更好地解析语义表达比其他模型表现更优秀。

孟加拉语仇恨言论和冒犯性语言检测

Hate Speech and Offensive Language Detection in Bengali

We present the AsNER, a named entity annotation dataset for low resource
Assamese language with a baseline Assamese NER model. The dataset contains
about 99k tokens comprised of text from the speech of the Prime Minister of
India and Assamese play. It also contains person names, location names and
addresses. The proposed NER dataset is likely to be a significant resource for
deep neural based Assamese language processing. We benchmark the dataset by
training NER models and evaluating using state-of-the-art architectures for
supervised named entity recognition (NER) such as Fasttext, BERT, XLM-R, FLAIR,
MuRIL etc. We implement several baseline approaches with state-of-the-art
sequence tagging Bi-LSTM-CRF architecture. The highest F1-score among all
baselines achieves an accuracy of 80.69% when using MuRIL as a word embedding
method. The annotated dataset and the top performing model are made publicly
available.

我们提出 AsNER，这是一个适用于资源匮乏的阿萨姆语的命名实体注释数据集，其中包括来自印度总理演讲和阿萨姆语剧本的约 99k 个单词，数据集中包含了人名，地名和地址信息，并基于 Fasttext, BERT, XLM-R, FLAIR, MuRIL 等算法进行评估。其中最高的 F1-score 在使用 MuRIL 作为词嵌入方法时达到了 80.69％的准确率。

AsNER—— 用于阿萨姆语命名实体识别的注释数据集和基准线

AsNER -- Annotated Dataset and Baseline for Assamese Named Entity recognition

India is a multilingual society with 1369 rationalized languages and dialects
being spoken across the country (INDIA, 2011). Of these, the 22 scheduled
languages have a staggering total of 1.17 billion speakers and 121 languages
have more than 10,000 speakers (INDIA, 2011). India also has the second largest
(and an ever growing) digital footprint (Statista, 2020). Despite this, today's
state-of-the-art multilingual systems perform suboptimally on Indian (IN)
languages. This can be explained by the fact that multilingual language models
(LMs) are often trained on 100+ languages together, leading to a small
representation of IN languages in their vocabulary and training data.
Multilingual LMs are substantially less effective in resource-lean scenarios
(Wu and Dredze, 2020; Lauscher et al., 2020), as limited data doesn't help
capture the various nuances of a language. One also commonly observes IN
language text transliterated to Latin or code-mixed with English, especially in
informal settings (for example, on social media platforms) (Rijhwani et al.,
2017). This phenomenon is not adequately handled by current state-of-the-art
multilingual LMs. To address the aforementioned gaps, we propose MuRIL, a
multilingual LM specifically built for IN languages. MuRIL is trained on
significantly large amounts of IN text corpora only. We explicitly augment
monolingual text corpora with both translated and transliterated document
pairs, that serve as supervised cross-lingual signals in training. MuRIL
significantly outperforms multilingual BERT (mBERT) on all tasks in the
challenging cross-lingual XTREME benchmark (Hu et al., 2020). We also present
results on transliterated (native to Latin script) test sets of the chosen
datasets and demonstrate the efficacy of MuRIL in handling transliterated data.

MuRIL 是一种专为印度语言建立的多语言语言模型， 使用翻译和音译文档对增加跨语言信号在训练期间，在多项任务中表现优异，尤其针对拉丁转写的数据具有很高的处理效能。