Imputing Out-of-Vocabulary Embeddings with LOVE Makes L anguage M odels Robust with Little Cost (2022)

Abstract: State-of-the-art NLP systems represent inputs with word embeddings, but these are brittle when faced with Out-of-Vocabulary (OOV) words. To address this issue, we follow the principle of mimick-like models to generate vectors for unseen words, by learning the behavior of pre-trained embeddings using only the surface form of words. We present a simple contrastive learning framework, LOVE, which extends the word representation of an existing pre-trained language model (such as BERT) and makes it robust to OOV with few additional parameters. Extensive evaluations demonstrate that our lightweight model achieves similar or even better performances than prior competitors, both on original datasets and on corrupted variants. Moreover, it can be used in a plug-and-play fashion with FastText and BERT, where it significantly improves their robustness.

## introduction word embeddings represent words as vectors @xcite . they have been instrumental in neural network approaches that brought impressive performance gains to many natural language processing (nlp) tasks. these approaches use a fixed-size vocabulary. thus they can deal only with words that have been seen during training. while this works well on many benchmark datasets, real-word corpora are typically much noisier and contain out-of-vocabulary (oov) words, i.e., rare words, domainspecific words, slang words, and words with typos, which have not been seen during training. model performance deteriorates a lot with unseen words, and minor character perturbations can flip the prediction of a model @xcite @xcite . simple experiments (figure 1 ) show that the addition of typos to datasets degrades the performance for text classification models considerably. to alleviate this problem, pioneering work pretrained word embeddings with morphological features (sub-word tokens) on large-scale datasets @xcite @xcite . one of the most prominent works in this direction is fasttext @xcite , which incorporates character n-grams into the skip-gram model. with fasttext, vectors of unseen words can be imputed by summing up the n-gram vectors. however, these subword-level models come with great costs: the requirements of pre-training from scratch and high memory footprint. hence, several simpler approaches have been developed, e.g., mim-ick @xcite , bos @xcite and @xcite . these use only the surface form of words to generate vectors for unseen words, through learning from pre-trained embeddings. although mimick-like models can efficiently reduce the parameters of pre-trained representations and alleviate the oov problem, two main challenges remain. first, the models remain bound in the trade-off between complexity and performance: the original mimick is lightweight but does not produce high-quality word vectors consistently. bos and kvq-fh obtain better word representations but need more parameters. second, these models cannot be used with existing pre-trained language models such as bert. it is these models, however, to which we owe so much progress in the domain @xcite @xcite . to date, these high-performant models are still fragile when dealing with rare words (schick and schütze, 2020), misspellings @xcite and domainspecific words @xcite . we address these two challenges head-on: we design a new contrastive learning framework to learn the behavior of pre-trained embeddings, dubbed love, learning out-of-vocabulary embeddings. our model builds upon a memory-saving mixed input of character and subwords instead of n-gram characters. it encodes this input by a lightweight positional attention module. during training, love uses novel types of data augmentation and hard negative generation. the model is then able to produce high-quality word representations that are robust to character perturbations, while consuming only a fraction of the cost of existing models. for instance, love with 6.5m parameters can obtain similar representations as the original fasttext model with more than 900m parameters. what is more, our model can be used in a plug-and-play fashion to robustify existing language models. we find that using love to produce vectors for unseen words improves the performance of fasttext and bert by around 1.4-6.8 percentage points on noisy text -without hampering their original capabilities (as shown in figure 2 ). in the following, section 2 discusses related work, section 3 introduces preliminaries, section 4 presents our approach, section 5 shows our experiments, and section 6 concludes. the appendix contains additional experiments and analyses. our code and data is available at https: //github.com/tigerchen52/love 2 related work ## conclusion we have presented a lightweight contrastivelearning framework, love, to learn word representations that are robust even in the face of out-ofvocabulary words. through a series of empirical studies, we have shown that our model (with only 6.5m parameters) can achieve similar or even better word embeddings on both intrinsic and extrinsic evaluations compared to other mimick-like models. moreover, our model can be added to models with static embeddings (such as fasttext) or dynamical embeddings (such as bert) in a plug-and-play fashion, and bring significant improvements there. for future work, we aim to extend our model to languages other than english. aleksandra piktus, necati bora edizel, piotr bojanowski, édouard grave, rui ferreira, and fabrizio silvestri. 2019. misspelling oblivious word embeddings. in proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 3226-3234. yuval pinter, robert guthrie, and jacob eisenstein. 2017. mimicking word embeddings using subword rnns. in proceedings of the 2017 conference on empirical methods in natural language processing, pages 102-112. danish pruthi, bhuwan dhingra, and zachary c lipton. 2019. combating adversarial misspellings with robust word recognition. in proceedings of the 57th annual meeting of the association for computational linguistics, pages 5582-5591. erik tjong kim sang and fien de meulder. 2003. introduction to the conll-2003 shared task: languageindependent named entity recognition. in proceedings of the seventh conference on natural language learning at hlt-naacl 2003, pages 142-147. shota sasaki, jun suzuki, and kentaro inui. 2019. subword-based compact reconstruction of word embeddings. in proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers), pages 3498-3508. timo schick and hinrich schütze. 2019a. attentive mimicking: better word embeddings by attending to informative contexts. arxiv preprint arxiv:1904.01617. timo schick and hinrich schütze. 2019b. learning semantic representations for novel words: leveraging both form and context. in proceedings of the aaai conference on artificial intelligence, volume 33, pages 6965-6973. timo schick and hinrich schütze. 2020. rare words: a major problem for contextualized embeddings and how to fix it by attentive mimicking. in proceedings of the aaai conference on artificial intelligence, volume 34, pages 8766-8774. m i s s p e l [sub] mis spell [sub] [sep] [cls] e 1 e 2 e 3 e 4 e 5 e 6 e 7 e 8 e 9 e 11 e 10 e 12 e 0 input embedding l e 7 figure a1: an illustration of our mixed input for the word misspell.