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Language Acquisition is Embodied, Interactive, Emotive: a Research Proposal

arXiv 2105.04633 ↗First posted 10 May 2021cs.CLcited by 0

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Abstract (arXiv)

Humans' experience of the world is profoundly multimodal from the beginning, so why do existing state-of-the-art language models only use text as a modality to learn and represent semantic meaning? In this paper we review the literature on the role of embodiment and emotion in the interactive setting of spoken dialogue as necessary prerequisites for language learning for human children, including how words in child vocabularies are largely concrete, then shift to become more abstract as the children get older. We sketch a model of semantics that leverages current transformer-based models and a word-level grounded model, then explain the robot-dialogue system that will make use of our semantic model, the setting for the system to learn language, and existing benchmarks for evaluation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 37 references that are in the map. First posted on arXiv 10 May 2021.

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10 of 37 references.

References · 10

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  3. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  4. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
  5. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  6. 2020A Primer in BERTology: What we know about how BERT workscited by 815
  7. 2020Experience Grounds Languagecited by 242
  8. 2019Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baselinecited by 61
  9. 2014The Latent Structure of Dictionariescited by 9
  10. 2015Resolving References to Objects in Photographs using the Words-As-Classifiers Modelcited by 9

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