Most of the common applications of Named Entity Recognition (NER) is on
English and other highly available languages. In this work, we present our
findings on Named Entity Recognition for 5 Nigerian Languages (Nigerian
English, Nigerian Pidgin English, Igbo, Yoruba and Hausa). These languages are
considered low-resourced, and very little openly available Natural Language
Processing work has been done in most of them. In this work, individual NER
models were trained and metrics recorded for each of the languages. We also
worked on a combined model that can handle Named Entity Recognition (NER) for
any of the five languages. The combined model works well for Named Entity
Recognition(NER) on each of the languages and with better performance compared
to individual NER models trained specifically on annotated data for the
specific language. The aim of this work is to share our learning on how
information extraction using Named Entity Recognition can be optimized for the
listed Nigerian Languages for inclusion, ease of deployment in production and
reusability of models. Models developed during this project are available on
GitHub this https URL and an interactive web app
this https URL

该研究研究了 5 种尼日利亚语言（尼日利亚英语，尼日利亚皮钦英语，伊博语，约鲁巴语和豪萨语）的命名实体识别，在建立个体模型和组合模型的基础上，提高了信息提取的效率，提高了模型的再利用性和生产部署的便利性。

NaijaNER: 面向尼日利亚 5 种语言的全面命名实体识别

NaijaNER : Comprehensive Named Entity Recognition for 5 Nigerian  Languages

Multilingual machine translation, which translates multiple languages with a
single model, has attracted much attention due to its efficiency of offline
training and online serving. However, traditional multilingual translation
usually yields inferior accuracy compared with the counterpart using individual
models for each language pair, due to language diversity and model capacity
limitations. In this paper, we propose a distillation-based approach to boost
the accuracy of multilingual machine translation. Specifically, individual
models are first trained and regarded as teachers, and then the multilingual
model is trained to fit the training data and match the outputs of individual
models simultaneously through knowledge distillation. Experiments on IWSLT, WMT
and Ted talk translation datasets demonstrate the effectiveness of our method.
Particularly, we show that one model is enough to handle multiple languages (up
to 44 languages in our experiment), with comparable or even better accuracy
than individual models.

本文提出一种基于蒸馏的方法来提高多语言机器翻译的准确性，并在数据集上进行了实验，结果表明该方法可以通过训练单独的模型（即老师）来训练多语言模型，这使得一个模型即可具备处理多达 44 种不同语言的能力（且其准确性与单独模型相当甚至更优）。