Multilingual (non-English) NLP — 7 things to know before getting started

A primer on differences between English and Non-English NLP

Ramsri Goutham


Multilingual Natural Language Processing (NLP) is a rapidly growing field, but it is different from English NLP in several ways. It requires a deep understanding of multiple languages and their unique characteristics.

Image from Pixabay

From the lack of space characters in some languages to the different cultural contexts, grammar and syntax, text direction, and complex structures, the challenges are multiple!

In this article, we will see 7 reasons why multilingual NLP is different from English NLP with relevant examples as necessary.

1. No spaces between words

Unlike English, languages like Chinese, Thai, and Japanese do not have spaces to separate words. This makes it more difficult for NLP algorithms to accurately segment text into individual words and phrases, which can impact the accuracy of NLP tasks.

For example, the sentence “今天天气很好” in Chinese translates to “Today the weather is good,” but there are no spaces between the words, making it difficult for an NLP system to split words without morpheme analysis.

English requires less processing for this reason as you can just split on spaces.

2. Transliterated text

People write transliterated text, which is when they write a different language but use English characters. This can cause confusion for NLP algorithms, as they may not be able to distinguish between the actual language being used and the characters being used to represent it.

For example, the text “kaam ho gaya” is “Work is done” in Hindi but if one uses English characters instead of “काम हो गया”, it becomes very hard to disambiguate the exact language.

3. Grammar and Syntax differences

The grammar and syntax of many languages are different from English. This can make it difficult for NLP algorithms to understand the structure of sentences in these languages.

For example, in Spanish, the verb must agree with the subject in terms of number and gender…