Visual Grounding in Video for Unsupervised Word Translation (2003.05078v2)

Abstract: There are thousands of actively spoken languages on Earth, but a single visual world. Grounding in this visual world has the potential to bridge the gap between all these languages. Our goal is to use visual grounding to improve unsupervised word mapping between languages. The key idea is to establish a common visual representation between two languages by learning embeddings from unpaired instructional videos narrated in the native language. Given this shared embedding we demonstrate that (i) we can map words between the languages, particularly the 'visual' words; (ii) that the shared embedding provides a good initialization for existing unsupervised text-based word translation techniques, forming the basis for our proposed hybrid visual-text mapping algorithm, MUVE; and (iii) our approach achieves superior performance by addressing the shortcomings of text-based methods -- it is more robust, handles datasets with less commonality, and is applicable to low-resource languages. We apply these methods to translate words from English to French, Korean, and Japanese -- all without any parallel corpora and simply by watching many videos of people speaking while doing things.

Visual Grounding in Video for Unsupervised Word Translation: An Academic Overview

The paper "Visual Grounding in Video for Unsupervised Word Translation" presents an innovative approach to unsupervised word translation using the visual grounding provided by instructional videos. This research leverages the shared visual domain of the physical world to bridge the linguistic gap between languages, specifically focusing on unsupervised word mapping with no parallel corpora. The authors introduce a novel model that grounds language in visual context, responding to the long-standing 'symbol grounding problem' in artificial intelligence.

Methodology and Experimental Setup

The proposed model builds a shared visual representation between two languages, utilizing unpaired instructional videos. The architecture comprises two language encoders and a video encoder, wherein the visual encoder is shared between languages to create a unified embedding space. This embedding fosters the alignment of word vectors across languages, facilitating translation tasks.

The experimentation involves training on a newly curated large-scale dataset, termed HowToWorld, containing instructional videos in English, French, Korean, and Japanese. This dataset was meticulously filtered to eliminate any overlapping videos to ensure complete separation between language pairs. The evaluation was conducted using established bilingual dictionaries and novel datasets comprised of visually descriptive terms.

Results and Analysis

The empirical findings reveal significant improvements in word translation accuracy due to the visual grounding approach. Specifically, the Base Model achieves Recall@1 performance of 9.1% and 15.2% on general and visually observable English-French word translation tasks, respectively, demonstrating clear superiority over baselines such as random chance and video retrieval without shared visual representations. Further enhancement is observed through the MUVE algorithm, which integrates text-based word mapping methods refined with the visual model's output. MUVE records substantial gains, reflecting the robustness introduced by combining visual and textual data.

The nuanced analysis indicates that visual grounding mitigates the limitations faced by traditional text-based methods concerning dissimilar training corpora and low-resource languages. With datasets of varying sizes and differing corpora characteristics, MUVE exhibits superior adaptability and performance stability compared to text-only approaches.

Implications and Future Directions

The implications of this research are broad-ranging, providing insights into bilingual acquisition theories and advancing the field of unsupervised language translation. Practically, the integration of visual context in LLMs can enhance machine translation systems, particularly for languages with limited text resources. Theoretically, the research contributes to bridging semantic understanding with perceptual inputs, contributing to the discourse on multimodal machine learning.

Future developments could explore extending unsupervised translation models beyond isolated words to entire sentences or concepts. Additionally, advancing the fidelity of visual grounding models to encompass auditory signals directly may offer a holistic approach to learning from multimedia inputs.

This paper represents a significant stride toward harnessing the potential of visual grounding in linguistic applications, offering scope for further exploration in visual-linguistic resource integration in AI systems.