Exploring Alignment in Shared Cross-Lingual Spaces

Authors: basel  mousi
nadir  durrani
fahim  dalvi
majd  hawasly
ahmed  abdelali
Venue: EMNLP_2024_Workshop_BlackBoxNLP
Published: 2024-08-15 17:11:25

Abstract: Despite their remarkable ability to capture linguistic nuances across diverse languages, questions persist regarding the degree of alignment between languages in multilingual embeddings. Drawing inspiration from research on high-dimensional representations in neural language models, we employ clustering to uncover latent concepts within multilingual models. Our analysis focuses on quantifying the alignment and overlap of these concepts across various languages within the latent space. To this end, we introduce two metrics CALIGN and COLAP aimed at quantifying these aspects, enabling a deeper exploration of multilingual embeddings. Our study encompasses three multilingual  models (mT5, mBERT, and XLM-RoBERTa) and three downstream tasks (Machine Translation, Named Entity Recognition, and Sentiment Analysis). Key findings from our research include: i) deeper layers in the network demonstrate increased cross-lingual alignment attributed to the presence of language-agnostic concepts, ii) fine-tuning of the models enhances alignment within the latent space, and iii) such task-specific calibration helps in explaining the emergence of zero-shot capabilities in the models.

Main paper: 
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 The emergence of multilingual contextualized embeddings has been a ground-breaking advancement, in the ever-evolving landscape of natural language processing. Adept at capturing the linguistic nuances across different languages, these embeddings have spurred a multitude of studies (Pires et al., 2019; Dufter and Schütze, 2020; Papadimitriou et al., 2021) seeking to understand the underlying mechanisms. How these models achieve multilinguality without explicit cross-lingual supervision during training is a particularly interesting question to answer.Cross-lingual embeddings are designed to encode linguistic concepts that bridge equivalent semantic meaning across diverse languages. The question is: how well is this achieved in practice? When considering two arbitrary languages, how well aligned are the embeddings of those languages? and how language agnostic are these multilingual embeddings in reality? Addressing these questions necessitates a comprehensive approach.In high-dimensional spaces, neural language models exhibit a capability to group words with shared linguistic associations, as highlighted by Mikolov et al. (2013) . Expanding upon this foundational insight, recent research endeavors (Michael et al., 2020; Dalvi et al., 2022; Fu and Lapata, 2022) delved into conducting representation analysis within pre-trained models. Our objective, in this work, is to uncover encoded concepts within multilingual models and analyze their alignment and overlap across various languages within the latent space. We discover latent concepts by applying clustering to the underlying contextualized representations. The premise is that these clusters potentially signify latent concepts, encapsulating the language knowledge assimilated by the model. We * Work was published at ACL24 † Ahmed contributed to the project while he was at QCRI. build our work on top of this foundation to quantify concept alignment and overlap within multilingual latent space. We propose two metrics CALIGN and COLAP to quantify these two aspects and carry out analysis to study the following questions:• To what extent do latent spaces across languages exhibit alignment and overlap in multilingual models?• How does this change as the models are tuned towards any downstream NLP task?• How do the multilingual latent spaces transform for zero-shot scenarios?Our approach focuses on discovering and analyzing latent concepts within multilingual neural language models to understand their alignment and overlap across languages. Figure 1 provides an overview of our methodology, which involves clustering contextualized embeddings to identify these encoded concepts (Dalvi et al., 2022) . We introduce two novel metrics to analyze the alignment and overlap within multilingual models for this purpose: CALIGN (Concept Alignment) and COLAP (Concept Overlap).CALIGN measures how well concepts in one language align with their counterparts in another, capturing the semantic coherence within the multilingual framework. This metric assesses the alignment of encoded concepts by identifying semantically equivalent tokens across different languages.Figure 2 shows sample aligned concepts.COLAP investigates overlapping cross-lingual latent spaces within the model's representation, highlighting multilingual concepts that bring together words from multiple languages into a close latent space. This metric helps in understanding the intricate relationships between concepts across languages. Figure 3 shows sample overlapping concepts.We conducted a study employing three multilingual transformer models: mT5 (Xue et al., 2021) , mBERT (Devlin et al., 2019) , and XLM-RoBERTa (Conneau et al., 2020) . These models were finetuned for three downstream NLP tasks: machine translation, named-entity recognition and sentiment analysis, spanning sequence generation, labeling and classification respectively. Our analysis revealed intriguing insights, including:• Deeper layers in multilingual models preserve semantic concepts, contrasting with languagedependent lexical learning in lower layers, resulting in a higher alignment.• Fine-tuning calibrates the latent space towards higher alignment and the task-specific calibration of the latent space facilitates zero-shot capabilities.• Divergent patterns emerge in the encoder and decoder latent spaces in seq2seq models. The final layers in the decoder tend to primarily retain language specific concepts. • The complexity of optimization function affects the extent of overlap in latent spaces• While many model concepts exhibit multilingual traits, later layers post fine-tuning tend to retain primarily language-specific characteristics. 
 
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Decision: Cross-submission of paper accepted to ACL 2024, with good fit for the workshop.