Abstract
We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test. Fine-tuning the embeddings of a pre-trained state-of-the-art bidirectional language model (BERT) in the language modeling framework yields a 3.5% improvement. The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, Delta-RNN, as well as those that use BERT embeddings). Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment. This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.- Anthology ID:
- P19-1506
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5127–5136
- Language:
- URL:
- https://aclanthology.org/P19-1506
- DOI:
- 10.18653/v1/P19-1506
- Cite (ACL):
- Alexander Ororbia, Ankur Mali, Matthew Kelly, and David Reitter. 2019. Like a Baby: Visually Situated Neural Language Acquisition. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5127–5136, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Like a Baby: Visually Situated Neural Language Acquisition (Ororbia et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/P19-1506.pdf