# Enriching Rare Word Representations in Neural Language Models by Embedding Matrix Augmentation

arXiv: https://arxiv.org/abs/1904.03799 · HTML: https://mlsanity.com/paper/1904.03799/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 7 of 7)

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- [No One Size Fits All: QueryBandits for Hallucination Mitigation](https://mlsanity.com/paper/2602.20332/) (2026)
- [SEA-SpeechBench: A Large-Scale Multitask Benchmark for Speech Understanding Across Southeast Asia](https://mlsanity.com/paper/2609.09672/) (2026)

## Abstract (arXiv)

The neural language models (NLM) achieve strong generalization capability by learning the dense representation of words and using them to estimate probability distribution function. However, learning the representation of rare words is a challenging problem causing the NLM to produce unreliable probability estimates. To address this problem, we propose a method to enrich representations of rare words in pre-trained NLM and consequently improve its probability estimation performance. The proposed method augments the word embedding matrices of pre-trained NLM while keeping other parameters unchanged. Specifically, our method updates the embedding vectors of rare words using embedding vectors of other semantically and syntactically similar words. To evaluate the proposed method, we enrich the rare street names in the pre-trained NLM and use it to rescore 100-best hypotheses output from the Singapore English speech recognition system. The enriched NLM reduces the word error rate by 6% relative and improves the recognition accuracy of the rare words by 16% absolute as compared to the baseline NLM.