Visual Grounding of Inter-lingual Word-Embeddings
Wafaa Mohammed, Hassan Shahmohammadi, Hendrik P. A. Lensch, R. Harald, Baayen

TL;DR
This paper explores how visual grounding can enhance multilingual word embeddings by aligning visual and textual data across English, Arabic, and German, revealing mixed effects on different language pairs and benchmark tasks.
Contribution
It introduces an implicit alignment method for inter-lingual visual grounding of word embeddings and evaluates its impact across three languages on various benchmarks.
Findings
Inter-lingual grounding improves embeddings for similar languages like English and German.
Grounding with Arabic sometimes degrades performance on word similarity tasks.
Arabic shows significant improvement in categorization benchmarks when grounded with English.
Abstract
Visual grounding of Language aims at enriching textual representations of language with multiple sources of visual knowledge such as images and videos. Although visual grounding is an area of intense research, inter-lingual aspects of visual grounding have not received much attention. The present study investigates the inter-lingual visual grounding of word embeddings. We propose an implicit alignment technique between the two spaces of vision and language in which inter-lingual textual information interacts in order to enrich pre-trained textual word embeddings. We focus on three languages in our experiments, namely, English, Arabic, and German. We obtained visually grounded vector representations for these languages and studied whether visual grounding on one or multiple languages improved the performance of embeddings on word similarity and categorization benchmarks. Our experiments…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMultimodal Machine Learning Applications · Natural Language Processing Techniques · Language, Metaphor, and Cognition
