XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge (2109.12573v3)

Abstract: Cross-lingual pre-training has achieved great successes using monolingual and bilingual plain text corpora. However, most pre-trained models neglect multilingual knowledge, which is language agnostic but comprises abundant cross-lingual structure alignment. In this paper, we propose XLM-K, a cross-lingual LLM incorporating multilingual knowledge in pre-training. XLM-K augments existing multilingual pre-training with two knowledge tasks, namely Masked Entity Prediction Task and Object Entailment Task. We evaluate XLM-K on MLQA, NER and XNLI. Experimental results clearly demonstrate significant improvements over existing multilingual LLMs. The results on MLQA and NER exhibit the superiority of XLM-K in knowledge related tasks. The success in XNLI shows a better cross-lingual transferability obtained in XLM-K. What is more, we provide a detailed probing analysis to confirm the desired knowledge captured in our pre-training regimen. The code is available at https://github.com/microsoft/Unicoder/tree/master/pretraining/xlmk.

Evaluation of XLM-K's Contribution to Cross-Lingual LLM Pre-training

The paper "XLM-K: Improving Cross-Lingual LLM Pre-training with Multilingual Knowledge" presents a methodological enhancement of cross-lingual LLMs by incorporating multilingual knowledge into pre-training processes. The authors introduce XLM-K, a model designed to address the limitations of existing cross-lingual models that typically neglect multilingual knowledge, which is language agnostic while providing substantial cross-lingual structural alignment.

Methodology and Novel Contributions

XLM-K extends the pre-training of cross-lingual LLMs with two newly devised tasks: the Masked Entity Prediction (MEP) and the Object Entailment (OE) tasks. The MEP task focuses on linking contextualized entity embeddings with multilingual knowledge base descriptions, enhancing the model's capabilities to distinguish between entities with similar surface forms across languages. Contrastingly, the OE task interlinks subjects and objects using their description and relation triplets, further integrating structured and context-specific knowledge into XLM-K.

These tasks address the need for contextually enriched and semantically aligned multilingual embeddings by utilizing Wikipedia and Wikidata. By capturing both descriptive and structured semantics, XLM-K demonstrates improved alignment across languages, thereby enhancing cross-lingual transferability.

Experimental Evaluation

The evaluation of XLM-K is conducted on three standard tasks: MLQA, NER, and XNLI. The results show significant improvements compared to existing multilingual models. Specifically, XLM-K outperforms the baseline XLM-R by achieving a 2.0 F1-score improvement on MLQA and demonstrating superiority in both MLQA and NER's knowledge-related tasks. The improvements are also evident on XNLI, with XLM-K demonstrating enhanced cross-lingual transferability, albeit with marginal gains compared to its performance on knowledge-centric MLQA and NER tasks.

The probing analysis conducted reveals that XLM-K effectively captures and retains the desired multilingual knowledge during the pre-training phase. This capability is reflected in substantial improvements when evaluated on tasks such as Google-RE and T-REx, which assess the model's proficiency in recalling factual knowledge embedded during pre-training.

Methodological Implications

The integration of multilingual knowledge into pre-training paradigms, as demonstrated by XLM-K, provides key insights into enhancing cross-lingual LLM architectures. These findings suggest that explicitly incorporating structured and descriptive knowledge can significantly boost a model's performance in multilingual applications. Furthermore, XLM-K's design can inform future explorations into multilingual models that seek to bridge semantic gaps across different languages using extensive interlanguage alignment and knowledge base integration.

Future Directions

While XLM-K's contributions mark a notable advancement in cross-lingual model pre-training, there remains further scope for exploration. Potential research avenues include extending XLM-K's methodology to encompass a broader range of knowledge sources beyond Wikipedia and Wikidata and integrating this approach with more recent advancements in contrastive learning strategies.

In conclusion, XLM-K sets a precedent for leveraging multilingual knowledge bases in pre-training LLMs, showcasing notable enhancements in cross-lingual adaptability and knowledge retention, which are critical for the continued evolution and efficacy of multilingual AI systems.