Recently, I worked on adding Russian support to my sentence rewriter. During development, I encountered several technical challenges, ranging from NLP model adaptation to text handling and front-end multilingual rendering. In this post, I’ll share practical solutions, code snippets, and lessons learned for anyone interested in small-language NLP projects.
Why Russian?
Most sentence rewriter focus on English, but Russian has unique grammatical structures and usage patterns. Supporting it isn’t just a matter of translating the UI; it requires adapting models and front-end components to handle language-specific characteristics.
Handling Multilingual Text
Russian uses Cyrillic characters, so ensuring full UTF-8 support is critical. On the backend, this can be done with Node.js + Express:
app.use(express.json({ limit: '2mb' }));
app.use((req, res, next) => {
res.setHeader('Content-Type', 'application/json; charset=utf-8');
next();
});
Additionally, tokenization and punctuation handling must be adapted to Russian. For example, using the Python razdel library:
from razdel import tokenize
text = "Привет! Как дела?"
tokens = [t.text for t in tokenize(text)]
print(tokens)
# ['Привет', '!', 'Как', 'дела', '?']
razdel efficiently handles Russian word forms and punctuation.
Adapting NLP Models
Most pre-trained NLP models are English-centric. For Russian, I took several steps:
- Integrated a Russian tokenizer and lemmatizer – to correctly handle Russian word forms and morphology.
- Fine-tuned models – so that generated sentences respect Russian grammar, word order, and syntax.
- Used automated tests – to verify that the rewritten sentences are grammatically correct and readable.
Example using Hugging Face transformers:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ru-en")
text = "Сегодня хороший день."
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
For rewriting rather than translation, I either fine-tune on Russian corpora or combine a GPT-style API with post-processing for grammar correction.
Front-End Multilingual Handling
On the front end, I used i18next for language management and ensured that the UI adapts to longer Russian sentences:
import { useTranslation } from 'react-i18next';
function Rewriter() {
const { t } = useTranslation();
return (
<div>
<textarea placeholder={t('enter_text')} />
<button>{t('rewrite')}</button>
</div>
);
}
CSS adjustments for long sentences:
textarea {
width: 100%;
min-height: 120px;
word-break: break-word;
}
I also ensured that Cyrillic letters render consistently across browsers and screen sizes.
Performance Considerations
Russian sentences are often longer than English ones. To keep response times low:
- Batch-process requests asynchronously.
- Cache repeated requests using Redis.
- Minify JSON responses:
const compressed = JSON.stringify(response).replace(/\s+/g, '');
res.send(compressed);
Lessons Learned
- UTF-8 support is essential – encoding issues can silently break tokenization or front-end rendering.
- Tokenization and lemmatization are critical for natural rewriting.
- Flexible UI layouts are important for accommodating long sentences.
- Automated tests for grammar, readability, and encoding save time and reduce errors.
Additional Observations
- Multilingual NLP pipelines can accelerate development for other small languages.
- Planning front-end localization early prevents costly redesigns later.
- Performance optimizations should consider language-specific characteristics, like sentence length and morphological complexity.
You can explore the Russian implementation through sentence rewriter or Синонимайзер to see how these changes work in practice.
Top comments (1)
I love your pictuer