BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages (1710.02187v1)

Abstract: We present BPEmb, a collection of pre-trained subword unit embeddings in 275 languages, based on Byte-Pair Encoding (BPE). In an evaluation using fine-grained entity typing as testbed, BPEmb performs competitively, and for some languages bet- ter than alternative subword approaches, while requiring vastly fewer resources and no tokenization. BPEmb is available at https://github.com/bheinzerling/bpemb

• The paper presents BPEmb, a collection of pre-trained subword embeddings using Byte-Pair Encoding across 275 languages to address rare word modeling without tokenization.
• BPEmb uses BPE on symbol sequences from Wikipedia and GloVe embeddings, demonstrating performance and resource efficiency advantages over FastText in evaluations, particularly for resource-scarce languages.
• Practically, BPEmb enables resource-efficient multilingual NLP applications, though performance varies across languages, requiring further investigation into software compatibility and preprocessing problems.

Overview of BPEmb: Tokenization-Free Pre-trained Subword Embeddings

The paper in focus presents BPEmb, a compilation of pre-trained subword unit embeddings across 275 languages utilizing Byte-Pair Encoding (BPE). This research addresses the persistent challenge of modeling rare or unseen words in NLP—a challenge traditionally handled with the generic UNK token. BPEmb circumvents tokenization and boosts performance in certain languages over traditional methods like FastText, with significantly reduced resource requirements.

Contributions and Methodology

The central contributions of BPEmb are multifaceted:

1. Comprehensive Multilingual Embedding Release: BPEmb provides a vast collection of pre-trained embeddings covering various linguistic peculiarities across 275 languages.
2. Evaluation with Fine-grained Entity Typing: The utility of BPEmb is showcased through its application to a fine-grained entity typing task, highlighting its competency against existing subword approaches.
3. Resource Efficiency: BPEmb is designed to function effectively without the necessity for tokenized input, encouraging compact usage and reduced computational demand.

The research employs the BPE approach by treating text as a sequence of symbols. The process iteratively merges frequent symbol pairs into new symbols, resulting in a set of subword units. Applied across extensive Wikipedia datasets, these are subsequently embedded using the GloVe algorithm, available in a vast array of merge operations and dimensions for granular analysis.

Evaluation and Comparative Analysis

The evaluation results presented compare BPEmb with FastText and character embeddings, demonstrating compelling performance advantages. Notably, for English, BPEmb outperforms alternative embeddings in entity typing tasks, as evidenced by robust mean performance scores. Despite having a smaller vocabulary size than FastText, BPEmb efficiently balances frequent word representation with compositional subword inference, making it a feasible choice under resource constraints.

The results vary across languages. BPEmb is particularly effective in resource-scarce settings, such as Tibetan and Lao, suggesting its underlying frequency-based segmentation model is advantageous when language-specific tokenization is absent or less developed.

Skewed Performance and Limitations

While promising, BPEmb's performance isn't uniformly superior across all languages—most markedly in Khmer due to unicode processing inconsistencies. Such issues underscore the necessity for further investigation into software compatibility and preprocessing routines, particularly in resource-constrained languages.

Practical and Theoretical Implications

Practically speaking, BPEmb represents a leap towards resource-efficient multilingual NLP applications, offering a scalable solution to deployment in low-power environments, including mobile devices. Theoretically, the paper posits a compelling case for rethinking linguistic representation at the subword level, challenging traditional token-based approaches by showcasing the feasibility of subword compositionality.

Future Directions

Future work could explore integrating contextual information to further enhance the semantic representations BPEmb provides. Additionally, expanding hyper-parameter optimization methods and exploring hybrid models that combine token and subword approaches may yield improved performance across diverse NLP tasks.

BPEmb sets a benchmark for subsequent work in subword embedding research, offering a resourceful model that highlights the capacity for efficient language processing in an era increasingly focused on inclusive and accessible NLP solutions.