Keeping it simple: Implementation and performance of the proto-principle of adaptation and learning in the language sciences

• URL: http://arxiv.org/abs/2003.03813v2
• Date: Sat, 28 Aug 2021 10:55:04 GMT
• Title: Keeping it simple: Implementation and performance of the proto-principle of adaptation and learning in the language sciences
• Authors: Petar Milin, Harish Tayyar Madabushi, Michael Croucher, Dagmar Divjak
• Abstract summary: We present the Widrow-Hoff rule and its applications to language data. After contextualizing the rule historically and placing it in the chain of neurally inspired artificial learning models, we explain its rationale and implementational considerations.
• Score: 0.9845144212844665
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we present the Widrow-Hoff rule and its applications to language data. After contextualizing the rule historically and placing it in the chain of neurally inspired artificial learning models, we explain its rationale and implementational considerations. Using a number of case studies we illustrate how the Widrow-Hoff rule offers unexpected opportunities for the computational simulation of a range of language phenomena that make it possible to approach old problems from a novel perspective.

Related papers

• Learning Mathematical Rules with Large Language Models [10.285317818397298]
  We study the ability of large language models to learn specific mathematical rules such as distributivity or simplifying equations. We present an empirical analysis of their ability to generalize these rules, as well as to reuse them in the context of word problems.
  arXiv  Detail & Related papers  (2024-10-22T12:51:51Z)
• On the Proper Treatment of Tokenization in Psycholinguistics [53.960910019072436]
  The paper argues that token-level language models should be marginalized into character-level language models before they are used in psycholinguistic studies. We find various focal areas whose surprisal is a better psychometric predictor than the surprisal of the region of interest itself.
  arXiv  Detail & Related papers  (2024-10-03T17:18:03Z)
• Language Evolution with Deep Learning [49.879239655532324]
  Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models.
  arXiv  Detail & Related papers  (2024-03-18T16:52:54Z)
• Large Language Models as Analogical Reasoners [155.9617224350088]
  Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks. We introduce a new prompting approach, analogical prompting, designed to automatically guide the reasoning process of large language models.
  arXiv  Detail & Related papers  (2023-10-03T00:57:26Z)
• Explainability for Large Language Models: A Survey [59.67574757137078]
  Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. This paper introduces a taxonomy of explainability techniques and provides a structured overview of methods for explaining Transformer-based language models.
  arXiv  Detail & Related papers  (2023-09-02T22:14:26Z)
• Language Models as Inductive Reasoners [125.99461874008703]
  We propose a new paradigm (task) for inductive reasoning, which is to induce natural language rules from natural language facts. We create a dataset termed DEER containing 1.2k rule-fact pairs for the task, where rules and facts are written in natural language. We provide the first and comprehensive analysis of how well pretrained language models can induce natural language rules from natural language facts.
  arXiv  Detail & Related papers  (2022-12-21T11:12:14Z)
• Neural Unsupervised Reconstruction of Protolanguage Word Forms [34.66200889614538]
  We present a state-of-the-art neural approach to the unsupervised reconstruction of ancient word forms. We extend this work with neural models that can capture more complicated phonological and morphological changes.
  arXiv  Detail & Related papers  (2022-11-16T05:38:51Z)
• A Survey of Knowledge Enhanced Pre-trained Models [28.160826399552462]
  We refer to pre-trained language models with knowledge injection as knowledge-enhanced pre-trained language models (KEPLMs) These models demonstrate deep understanding and logical reasoning and introduce interpretability.
  arXiv  Detail & Related papers  (2021-10-01T08:51:58Z)
• Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models [27.11678023496321]
  This paper presents an initial study exploring the feasibility of zero-shot commonsense reasoning for the Winograd Challenge. We propose a novel self-supervised learning approach that refines the language model utilizing a set of linguistic perturbations of similar concept relationships. Empirical analysis of our conceptually simple framework demonstrates the viability of zero-shot commonsense reasoning on multiple benchmarks.
  arXiv  Detail & Related papers  (2021-09-10T21:02:24Z)
• Learning to Rationalize for Nonmonotonic Reasoning with Distant Supervision [44.32874972577682]
  We investigate the extent to which neural models can reason about natural language rationales that explain model predictions. We use pre-trained language models, neural knowledge models, and distant supervision from related tasks. Our model shows promises at generating post-hoc rationales explaining why an inference is more or less likely given the additional information.
  arXiv  Detail & Related papers  (2020-12-14T23:50:20Z)
• Learning Compositional Rules via Neural Program Synthesis [67.62112086708859]
  We present a neuro-symbolic model which learns entire rule systems from a small set of examples. Instead of directly predicting outputs from inputs, we train our model to induce the explicit system of rules governing a set of previously seen examples.
  arXiv  Detail & Related papers  (2020-03-12T01:06:48Z)

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.