# Language Features Matter: Effective Language Representations for Vision-Language Tasks

## TL;DR

This paper emphasizes the importance of sophisticated language features in vision-language tasks, demonstrating that advanced embeddings like BERT may underperform and proposing best practices and a new multi-task trained embedding to improve performance.

## Contribution

It systematically compares various language representations in VL tasks, reveals limitations of current models, and introduces a novel multi-task trained embedding, GrOVLE, for better language feature integration.

## Key findings

- Average language models outperform LSTMs on retrieval tasks
- State-of-the-art embeddings like BERT perform poorly on VL tasks
- Multi-task training improves language feature effectiveness

## Abstract

Shouldn't language and vision features be treated equally in vision-language (VL) tasks? Many VL approaches treat the language component as an afterthought, using simple language models that are either built upon fixed word embeddings trained on text-only data or are learned from scratch. We believe that language features deserve more attention, and conduct experiments which compare different word embeddings, language models, and embedding augmentation steps on five common VL tasks: image-sentence retrieval, image captioning, visual question answering, phrase grounding, and text-to-clip retrieval. Our experiments provide some striking results; an average embedding language model outperforms an LSTM on retrieval-style tasks; state-of-the-art representations such as BERT perform relatively poorly on vision-language tasks. From this comprehensive set of experiments we propose a set of best practices for incorporating the language component of VL tasks. To further elevate language features, we also show that knowledge in vision-language problems can be transferred across tasks to gain performance with multi-task training. This multi-task training is applied to a new Graph Oriented Vision-Language Embedding (GrOVLE), which we adapt from Word2Vec using WordNet and an original visual-language graph built from Visual Genome, providing a ready-to-use vision-language embedding: http://ai.bu.edu/grovle.