Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations

Abstract: Average word embeddings are a common baseline for more sophisticated sentence embedding techniques. However, they typically fall short of the performances of more complex models such as InferSent. Here, we generalize the concept of average word embeddings to power mean word embeddings. We show that the concatenation of different types of power mean word embeddings considerably closes the gap to state-of-the-art methods monolingually and substantially outperforms these more complex techniques cross-lingually. In addition, our proposed method outperforms different recently proposed baselines such as SIF and Sent2Vec by a solid margin, thus constituting a much harder-to-beat monolingual baseline. Our data and code are publicly available.

• The paper introduces a novel concatenated power mean approach that combines diverse word embeddings to enhance sentence representations.
• It extends traditional averaging by computing power means, offering a universal and efficient method applicable to multilingual NLP tasks.
• Empirical evaluations demonstrate superior monolingual and cross-lingual performance while reducing computational complexity.

Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations

The paper "Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations" investigates a novel methodology for deriving sentence embeddings that accurately encapsulate the potential meanings and features of sentences across different languages. Sentence embeddings, dense vectors summarizing various properties of a sentence, are pivotal to numerous NLP tasks. The authors aim to advance the ubiquitous average word embeddings, which often serve as baselines for more sophisticated models but tend to underperform against models like InferSent.

Conceptual Framework and Methodology

The authors propose a generalization of the average word embeddings framework through a novel approach that employs power mean word embeddings. This generalization extends the traditional notion of arithmetic mean to power means, which encompass various means including the arithmetic, geometric, and harmonic means among others, determined by different power values. By concatenating representations across multiple power means, the authors argue this method effectively narrows the performance chasm between simpler word average baselines and complex models monolingually and outperforms them cross-lingually.

The primary methodology involves:

1. Embedding Diversity: The method starts with the concatenation of different types of word embeddings that each capture distinct semantic, syntactic, or sentiment dimensions. By leveraging embeddings such as GloVe, Word2Vec, Attract-Repel, and MorphSpecialized, the proposed method encodes a richer diversity of information.
2. Power Mean Calculations: The authors extend the representation by computing power means for each set of embeddings. Power means provide a generalized framework to capture different facets of the combined data by varying the power values, which adjust how the data is summarized dimensionally. This mixture of embeddings offers a universal and flexible method for capturing sentence semantics across languages.
3. Sentence Representation and Universality: The resultant embeddings aim to be universally effective across a multitude of tasks and languages. The authors underscore the need for a task and language-agnostic method to learn embeddings that perform robustly even with minimal labeled data.

Empirical Evaluation and Findings

The paper rigorously tests the proposed methods across nine evaluation tasks, with both monolingual and cross-lingual datasets, to assess their potential as universal sentence encoders:

• Monolingual Performance: The concatenated power mean word embeddings show marked improvements over baseline averages and align closely with the performance of advanced models such as InferSent. Despite being lower-dimensional and computationally cheaper to produce, these embeddings outperform complex models typically reliant on resource-intensive training datasets.
• Cross-Lingual Proficiency: Demonstrating significant utility, the methodology outpaces various cross-lingual adaptations of InferSent, maintaining strong task performance across different language pairs. This feature illustrates the method's broader applicability to languages with scarce resources by bypassing the necessity for high-quality training data.

Implications and Future Directions

The findings present significant implications for both theoretical and practical aspects of NLP, especially in crafting representations that are consistently reliable across domains and languages. This advance diminishes reliance on architecture complexity and training resource intensity, thus offering a scalable and efficient alternative.

The research prompts further exploration into the capabilities of power means by suggesting automatic learning and tuning of power mean values tailored to specific task needs. Moreover, an evolving frontier could be the dimensional tailoring of embeddings based on particular language characteristics or task-specific requirements, possibly enriching versatility and adoption in real-world applications.

In conclusion, this paper offers a comprehensive and detailed framework for improving the universality and efficacy of sentence embeddings with practical benefits for multilingual NLP applications.