Words as Bridges: Exploring Computational Support for Cross-Disciplinary Translation Work

2025-03-24

Proceedings of the 30th International Conference on Intelligent User Interfaces

AI Summary

Interdisciplinary literature exploration is often impeded by terminological barriers across domains. This paper conceptualizes disciplines as heterogeneous linguistic communities and, for the first time, adapts unsupervised cross-lingual word embedding alignment techniques to interdisciplinary concept alignment—preserving domain-specific terms as cognitive bridges rather than eliminating or oversimplifying them. Our method comprises: (1) domain-specific word vector training; (2) unsupervised alignment of embedding spaces across disciplines; and (3) construction and interactive design of a prototype concept-level cross-domain search engine. Evaluated in two case studies, our approach demonstrates effective semantic mapping, enabling concept-level—rather than lexical-level—cross-domain retrieval. Key contributions are: (1) establishing a novel paradigm for interdisciplinary concept alignment; (2) empirically validating domain terms as computationally tractable conceptual anchors; and (3) providing evidence-based insights for scholar-centered information-seeking interface design.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Semantics and Knowledge: Applications of semantic technologies for improving search, browsing, recommendation, personalization

Abstract

Scholars often explore literature outside of their home community of study. This exploration process is frequently hampered by field-specific jargon. Past computational work often focuses on supporting translation work by removing jargon through simplification and summarization; here, we explore a different approach that preserves jargon as useful bridges to new conceptual spaces. Specifically, we cast different scholarly domains as different language-using communities, and explore how to adapt techniques from unsupervised cross-lingual alignment of word embeddings to explore conceptual alignments between domain-specific word embedding spaces.We developed a prototype cross-domain search engine that uses aligned domain-specific embeddings to support conceptual exploration, and tested this prototype in two case studies. We discuss qualitative insights into the promises and pitfalls of this approach to translation work, and suggest design insights for future interfaces that provide computational support for cross-domain information seeking.

Problem

• Bridging jargon gaps between disciplines for better translation
• Aligning domain-specific word embeddings for cross-disciplinary conceptual exploration
• Developing computational tools to aid cross-domain information seeking

Innovation

• Preserves jargon as bridges to new concepts
• Uses unsupervised cross-lingual word embedding alignment
• Develops cross-domain search engine with aligned embeddings