Subword Attention and Post-Processing for Rare and Unknown Contextualized Embeddings (2024)

Abstract: Word representations are an important aspect of Natural Language Processing (NLP). Representations are trained using large corpora, either as independent static embeddings or as part of a deep contextualized model. While word embeddings are useful, they struggle on rare and unknown words. As such, a large body of work has been done on estimating rare and unknown words. However, most of the methods focus on static embeddings, with few models focused on contextualized representations. In this work, we propose SPRUCE, a rare/unknown embedding architecture that focuses on contextualized representations. This architecture uses subword attention and embedding post-processing combined with the contextualized model to produce high quality embeddings. We then demonstrate these techniques lead to improved performance in most intrinsic and downstream tasks.

## introduction word representations are an important aspect of nlp. while initially, word embeddings were trained separately and inserted into task specific architectures ("static" embeddings), modern approaches use deep architectures to generate contextualized representations @xcite @xcite . a weakness of static representations is that they only exist for a trained vocabulary; there are no representations for unknown words. while deep contextualized models can theoretically produce a new representation, @xcite demonstrated that these representations for unknown/rare words are of poor quality, implying that rare/unknown words are still a challenge for contextualized embeddings. in response, there have been attempts to create new representations for these words. while there has been a large body of work on static embeddings, less has been focused on contextualized embeddings, especially approaches that incorporate recent innovations enhancing static rare/unknown estimation. motivated by this, we propose a new architecture for rare/unknown estimation of contextualized embeddings. this model incorporates subword attention and embedding post-processing for higher quality estimates for contextualized models. we call this approach subword attention and postprocessing for rare and unknown contextualized embeddings (spruce). we demonstrate that this model has superior results in most evaluation scenarios. ## related work rare/unknown word representations have been well studied in static word embeddings. early approaches used context sentences to estimate new word embeddings @xcite @xcite @xcite , while other approaches use the rare words' morphemes/subwords to estimate the embedding @xcite @xcite . the most effective approaches combine context sentences and subwords @xcite @xcite . the combined model subatt @xcite , for instance, uses transformer self attention @xcite on context like other models, but also uses transformer self attention on the subword representations, leading to strong results. rare/unknown words have also been studied on contextualized embeddings, with the goal of constructing new representations for use in the initial embedding layer of the contextualized deep model. while lessstudied than static embeddings, there have been attempts to effectively estimate rare/unknown contextualized embeddings. the current state-of-theart approach on contextualized models is bertram @xcite ; bertram constructs the context representations using the bert architecture. it then combines these representations us-ing the attention mechanism from attentive mimicking @xcite . it uses learned subwords to estimate the rare/unknown embedding, and then inputs this estimate into the bert model for each context sentence. bertram has been shown to output strong rare/unknown embeddings for use in a bert architecture. however, contextualized rare/unknown words are understudied, and models don't incorporate recent innovations found in static embedding equivalents. in response to this, we propose spruce, a model that incorporates the strengths of previous static models like subatt and contextualized models like bertram to create a new architecture that is stateof-the-art in most rare/unknown evaluation tasks. ## model we now present spruce 1 . we focus on estimating rare and unknown embeddings with the bert @xcite ) model, although this can be adapted to any deep model. we combine aspects of the previous state-of-the-art model bertram @xcite with attention on the subword input, similar to the one proposed in static word embeddings model subatt @xcite but has not been previously used in contextualized models. in addition, we train spruce on post-processed embeddings, with top pca components removed. a diagram of spruce is shown in figure 1 . ## conclusion we propose spruce, an architecture that uses deep contextualized models to estimate new representations of rare/unknown words for use in those models. we show the strength of spruce in intrinsic and downstream tasks.