Machine-Created Universal Language for Cross-lingual Transfer

Abstract: There are two primary approaches to addressing cross-lingual transfer: multilingual pre-training, which implicitly aligns the hidden representations of various languages, and translate-test, which explicitly translates different languages into an intermediate language, such as English. Translate-test offers better interpretability compared to multilingual pre-training. However, it has lower performance than multilingual pre-training(Conneau and Lample, 2019; Conneau et al, 2020) and struggles with word-level tasks due to translation altering word order. As a result, we propose a new Machine-created Universal Language (MUL) as an alternative intermediate language. MUL comprises a set of discrete symbols forming a universal vocabulary and a natural language to MUL translator for converting multiple natural languages to MUL. MUL unifies shared concepts from various languages into a single universal word, enhancing cross-language transfer. Additionally, MUL retains language-specific words and word order, allowing the model to be easily applied to word-level tasks. Our experiments demonstrate that translating into MUL yields improved performance compared to multilingual pre-training, and our analysis indicates that MUL possesses strong interpretability. The code is at: https://github.com/microsoft/Unicoder/tree/master/MCUL.

• The paper introduces MUL as a machine-created universal language that unifies shared linguistic concepts to boost cross-lingual NLP tasks.
• It leverages multilingual masked language modeling, inter-sentence contrastive learning, and vector quantization for precise cross-lingual alignment.
• Experiments demonstrate competitive performance on tasks like XNLI and NER while efficiently supporting low-resource languages with fewer parameters.

Machine-Created Universal Language for Cross-lingual Transfer

The paper "Machine-Created Universal Language for Cross-lingual Transfer" presents an innovative approach to overcoming the limitations associated with cross-lingual transfer in NLP. Traditionally, cross-lingual transfer has been facilitated through two dominant strategies: multilingual pre-training and the translate-test approach. While the former implicitly aligns hidden representations for multiple languages, the latter translates various languages into a common intermediary language, typically English. This research proposes an alternative: the Machine-created Universal Language (MUL).

Overview of MUL and Its Objectives

MUL is introduced as a novel intermediary language designed to enhance both performance and interpretability in cross-lingual NLP tasks. MUL consists of a universal vocabulary formed by discrete symbols and includes a translator between multiple natural languages and MUL. The core objective of MUL is to unify shared linguistic concepts, retaining language-specific characteristics and word order, thus facilitating improved cross-lingual transfer performance. MUL addresses the interpretability provided by the translate-test method without sacrificing the performance seen in multilingual pre-training.

Methodological Approach

The creation of MUL involves a multi-step process focusing on pre-training, alignment, and interpretability enhancement:

1. Multilingual Masked Language Modeling (MLM): Initially, an encoder is pre-trained using multilingual MLM loss, which supports the generation of word alignment supervision in an unsupervised manner. This step establishes the foundational contextualized word embeddings.
2. Inter-sentence Contrastive Learning: To ensure precise alignment of contextual embeddings across languages, inter-sentence contrastive learning is adopted. This method enhances the model's capacity to align similar meaning words while differentiating words of the same type but different meanings.
3. Vector Quantization with Cross-lingual Alignment (VQ-CA): Finally, VQ-CA is implemented to ensure the interpretability of the universal vocabulary. This component refines the embeddings so that each symbol corresponds to a distinct concept, aiding in effortless word disambiguation.

Experimental Results and Implications

Experiments were conducted on various cross-lingual tasks including XNLI, NER, MLQA, and Tatoeba. The results demonstrated that MUL, particularly in its base configuration, achieves competitive performance on par with well-established models like XLM-R and InfoXLM. Notably, MUL shows advancements in handling low-resource languages and delivers comparable results with a reduced parameter count.

Implications for NLP and Future Work

MUL's introduction suggests significant practical implications. By reducing vocabulary size while preserving word order and language-specific features, MUL enables efficient resource utilization and enhanced interpretability. Its successful integration into NLP workflows could streamline bilingual and multilingual processing, reducing the necessity for resource-intensive models.

Theoretically, MUL contributes to the understanding of how machine-created languages can mediate complex linguistic tasks. The insights from this work could inform future endeavors into automatic language generation and the representation of semantic concepts.

Looking ahead, exploring extensions of MUL to additional languages and more diverse linguistic tasks could reveal even broader applications and insights. Analyzing the impact of expanding the universal vocabulary and refining alignment strategies remains a promising avenue for further enhancing the potential of machine-generated universal languages in AI-driven linguistic research.