Experience Grounds Language

• URL: http://arxiv.org/abs/2004.10151v3
• Date: Mon, 2 Nov 2020 00:40:12 GMT
• Title: Experience Grounds Language
• Authors: Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, Nicolas Pinto, Joseph Turian
• Abstract summary: Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world.
• Score: 185.73483760454454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful. Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

Related papers

• Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language Use [16.425032085699698]
  It is desirable for embodied agents to have the ability to leverage human language to gain explicit or implicit knowledge for learning tasks. It's not clear how to incorporate rich language use to facilitate task learning. This paper studies different types of language inputs in facilitating reinforcement learning.
  arXiv  Detail & Related papers  (2024-10-31T17:59:52Z)
• Unveiling the pressures underlying language learning and use in neural networks, large language models, and humans: Lessons from emergent machine-to-machine communication [5.371337604556311]
  We review three cases where mismatches between the emergent linguistic behavior of neural agents and humans were resolved. We identify key pressures at play for language learning and emergence: communicative success, production effort, learnability, and other psycho-/sociolinguistic factors.
  arXiv  Detail & Related papers  (2024-03-21T14:33:34Z)
• Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training [56.74440457571821]
  We analyze tasks covering syntax, semantics and reasoning, across 2M pre-training steps and five seeds. We identify critical learning phases across tasks and time, during which subspaces emerge, share information, and later disentangle to specialize. Our findings have implications for model interpretability, multi-task learning, and learning from limited data.
  arXiv  Detail & Related papers  (2023-10-25T09:09:55Z)
• Multi-lingual and Multi-cultural Figurative Language Understanding [69.47641938200817]
  Figurative language permeates human communication, but is relatively understudied in NLP. We create a dataset for seven diverse languages associated with a variety of cultures: Hindi, Indonesian, Javanese, Kannada, Sundanese, Swahili and Yoruba. Our dataset reveals that each language relies on cultural and regional concepts for figurative expressions, with the highest overlap between languages originating from the same region. All languages exhibit a significant deficiency compared to English, with variations in performance reflecting the availability of pre-training and fine-tuning data.
  arXiv  Detail & Related papers  (2023-05-25T15:30:31Z)
• TalkUp: Paving the Way for Understanding Empowering Language [38.873632974397744]
  This work builds from linguistic and social psychology literature to explore what characterizes empowering language. We crowdsource a novel dataset of Reddit posts labeled for empowerment. Preliminary analyses show that this dataset can be used to train language models that capture empowering and disempowering language.
  arXiv  Detail & Related papers  (2023-05-23T17:55:34Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches [28.47300996711215]
  People rely heavily on context to enrich meaning beyond what is literally said. To interact successfully with people, user-facing artificial intelligence systems will require similar skills in pragmatics.
  arXiv  Detail & Related papers  (2022-11-15T18:21:46Z)
• Knowledge Representation for Conceptual, Motivational, and Affective Processes in Natural Language Communication [16.148949542951616]
  This paper capitalizes on the UGALRS (Unified General Autonomous and Language Reasoning System) framework and the CD+ (Conceptual Representation Plus) scheme to illustrate how social communication through language is supported by a knowledge representational scheme. Its main contribution is in articulating a general framework of knowledge representation and processing to link these aspects together in serving the purpose of natural language communication for an intelligent system.
  arXiv  Detail & Related papers  (2022-09-26T01:37:50Z)
• ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning [97.10875695679499]
  We propose a novel contrastive learning framework named ERICA in pre-training phase to obtain a deeper understanding of the entities and their relations in text. Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks.
  arXiv  Detail & Related papers  (2020-12-30T03:35:22Z)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [64.22926988297685]
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP) In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
  arXiv  Detail & Related papers  (2019-10-23T17:37:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.