# Learning semantic sentence representations from visually grounded language without lexical knowledge

## TL;DR

This paper introduces a multimodal approach to learning sentence semantics directly from visual data, eliminating the need for lexical word embeddings, and achieves state-of-the-art results on several benchmarks.

## Contribution

The study demonstrates that visual grounding alone can produce high-quality sentence embeddings without relying on prior lexical knowledge.

## Key findings

- Achieves state-of-the-art results on image-caption retrieval benchmarks.
- Correlates well with human semantic similarity judgments.
- Shows that visual information alone can capture sentence-level semantics.

## Abstract

Current approaches to learning semantic representations of sentences often use prior word-level knowledge. The current study aims to leverage visual information in order to capture sentence level semantics without the need for word embeddings. We use a multimodal sentence encoder trained on a corpus of images with matching text captions to produce visually grounded sentence embeddings. Deep Neural Networks are trained to map the two modalities to a common embedding space such that for an image the corresponding caption can be retrieved and vice versa. We show that our model achieves results comparable to the current state-of-the-art on two popular image-caption retrieval benchmark data sets: MSCOCO and Flickr8k. We evaluate the semantic content of the resulting sentence embeddings using the data from the Semantic Textual Similarity benchmark task and show that the multimodal embeddings correlate well with human semantic similarity judgements. The system achieves state-of-the-art results on several of these benchmarks, which shows that a system trained solely on multimodal data, without assuming any word representations, is able to capture sentence level semantics. Importantly, this result shows that we do not need prior knowledge of lexical level semantics in order to model sentence level semantics. These findings demonstrate the importance of visual information in semantics.