Proceedings of the First Workshop on Subword and Character Level Models in NLP

Abstract

IntroductionTraditional NLP starts with a hand-engineered layer of representation, the level of tokens or words. A tokenization component first breaks up the text into units using manually designed rules. Tokens are then processed by components such as word segmentation, morphological analysis and multiword recognition. The heterogeneity of these components makes it hard to create integrated models of both structure within tokens (e.g., morphology) and structure across multiple tokens (e.g., multi-word expressions). This approach can perform poorly (i) for morphologically rich languages, (ii) for noisy text, (iii) for languages in which the recognition of words is difficult and (iv) for adaptation to new domains; and (v) it can impede the optimization of preprocessing in end-to-end learning.The workshop provides a forum for discussing recent advances as well as future directions on sub-word and character-level natural language processing and representation learning that address these problems.We received 37 submissions, out of which we accepted 24 as papers and 4 as extended abstracts. AbstractMost of neural language models use different kinds of embeddings for word prediction. While word embeddings can be associated to each word in the vocabulary or derived from characters as well as factored morphological decomposition, these word representations are mainly used to parametrize the input, i.e. the context of prediction. This work investigates the effect of using subword units (character and factored morphological decomposition) to build output representations for neural language modeling. We present a case study on Czech, a morphologically-rich language, experimenting with different input and output representations. When working with the full training vocabulary, despite unstable training, our experiments show that augmenting the output word representations with character-based embeddings can significantly improve the performance of the model. Moreover, reducing the size of the output look-up table, to let the character-based embeddings represent rare words, brings further improvement. IntroductionMost of neural language models, such as n-gram models (Bengio et al., 2003) are word based and rely on the definition of a finite vocabulary V. Therefore, a look-up table maps each wordw ∈ V to a vector of real features, and is stored in a matrix. While this approach yields significant improvement for a variety of tasks and languages, see for instance (Schwenk, 2007) in speech recognition and (Le et al., 2012; Devlin et al., 2014; in machine translation, it induces several limitations.For morphologically-rich languages, like Czech or German, the lexical coverage is still an important issue, since there is a combinatorial explosion of word forms, most of which are hardly observed on training data. On the one hand, growing the look-up table is not a solution, since it would increase the number of parameters without having enough training examples for a proper estimation. On the other hand, rare words can be replaced...