Polyglot: Distributed Word Representations for Multilingual NLP (1307.1662v2)

Abstract: Distributed word representations (word embeddings) have recently contributed to competitive performance in LLMing and several NLP tasks. In this work, we train word embeddings for more than 100 languages using their corresponding Wikipedias. We quantitatively demonstrate the utility of our word embeddings by using them as the sole features for training a part of speech tagger for a subset of these languages. We find their performance to be competitive with near state-of-art methods in English, Danish and Swedish. Moreover, we investigate the semantic features captured by these embeddings through the proximity of word groupings. We will release these embeddings publicly to help researchers in the development and enhancement of multilingual applications.

• The paper introduces multilingual word embeddings for 117 languages generated from Wikipedia, significantly reducing the need for language-specific feature engineering.
• The methodology uses a deep learning architecture inspired by SENNA to achieve competitive PoS tagging performance and capture meaningful semantic relationships.
• The efficient implementation and public release of these embeddings empower researchers to advance multilingual NLP applications, particularly for low-resource languages.

Distributed Word Representations for Multilingual NLP: An Overview of "Polyglot"

The paper "Polyglot: Distributed Word Representations for Multilingual NLP" addresses the complexities of building multilingual NLP systems by generating word embeddings for over 100 languages using data from Wikipedia. The authors focus on overcoming the challenges associated with language-specific preprocessing and feature engineering, which traditionally necessitate expert knowledge and hinder system portability.

Contributions

The core contributions of the paper include:

1. Multilingual Word Embeddings: The authors create and publicly release word embeddings for 117 languages, emphasizing minimal text normalization to preserve language-specific features. This represents a significant resource for developing multilingual NLP applications without extensive prior linguistic knowledge of each language.
2. Quantitative and Qualitative Evaluations: The effectiveness of these embeddings is validated through their use as features in a part-of-speech (PoS) tagger. Results demonstrate competitive performance, particularly in English, Danish, and Swedish. Furthermore, the paper explores the semantic and syntactic properties captured by these embeddings.
3. Efficient Implementation: Contributions were made to the Theano library, optimizing the pipeline to facilitate further exploratory research in embedding generation across various data sources beyond Wikipedia.

Methodology

Utilizing a task-independent approach, the authors employ a deep learning architecture inspired by SENNA. The embeddings are trained to distinguish between genuine and corrupted phrases using a neural network model. Key characteristics of the training procedure include large-scale data processing with a focus on embedding dimensionality and language-specific vocabulary preservation.

Evaluation

The embeddings' quality is thoroughly assessed through both semantic proximity analysis and performance testing in PoS tagging across multiple languages. For semantic assessment, word groupings are analyzed, showcasing consistent and meaningful proximity relationships in the embedding space. The PoS tagging task serves as a quantitative benchmark, with performance closely mirroring or surpassing some state-of-the-art techniques in specific languages.

Implications and Future Directions

The availability of these embeddings unlocks new avenues for multilingual research and development, particularly in resource-constrained languages where traditional NLP tools are inadequate. By simplifying the integration of multilingual capabilities into language systems, these embeddings mitigate some of the barriers to entry in multilingual NLP research.

The paper anticipates future work focusing on improving embeddings by expanding contextual information, refining handling of out-of-vocabulary (OOV) words, and enhancing domain adaptability. Investigating cross-linguistic mapping relationships further presents a potential area to explore the embeddings' utility in translating and transferring knowledge between languages.

Overall, the research lays a foundation for comprehensive multilingual NLP developments, broadening accessibility and enabling more robust application design across diverse linguistic landscapes.