A learning perspective on the emergence of abstractions: the curious
case of phonemes
- URL: http://arxiv.org/abs/2012.07499v3
- Date: Thu, 17 Dec 2020 14:06:59 GMT
- Title: A learning perspective on the emergence of abstractions: the curious
case of phonemes
- Authors: Petar Milin, Benjamin V. Tucker, and Dagmar Divjak
- Abstract summary: We test two opposing principles regarding the development of language knowledge in linguistically untrained language users.
We probed whether MBL and ECL could give rise to a type of language knowledge that resembles linguistic abstractions.
We show that ECL learning models can learn abstractions and that at least part of the phone inventory can be reliably identified from the input.
- Score: 2.580765958706854
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the present paper we use a range of modeling techniques to investigate
whether an abstract phone could emerge from exposure to speech sounds. We test
two opposing principles regarding the development of language knowledge in
linguistically untrained language users: Memory-Based Learning (MBL) and
Error-Correction Learning (ECL). A process of generalization underlies the
abstractions linguists operate with, and we probed whether MBL and ECL could
give rise to a type of language knowledge that resembles linguistic
abstractions. Each model was presented with a significant amount of
pre-processed speech produced by one speaker. We assessed the consistency or
stability of what the models have learned and their ability to give rise to
abstract categories. Both types of models fare differently with regard to these
tests. We show that ECL learning models can learn abstractions and that at
least part of the phone inventory can be reliably identified from the input.
Related papers
- AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph [62.685920585838616]
abstraction ability is essential in human intelligence, which remains under-explored in language models.
We present AbsPyramid, a unified entailment graph of 221K textual descriptions of abstraction knowledge.
arXiv Detail & Related papers (2023-11-15T18:11:23Z) - Pre-Trained Language Models for Interactive Decision-Making [72.77825666035203]
We describe a framework for imitation learning in which goals and observations are represented as a sequence of embeddings.
We demonstrate that this framework enables effective generalization across different environments.
For test tasks involving novel goals or novel scenes, initializing policies with language models improves task completion rates by 43.6%.
arXiv Detail & Related papers (2022-02-03T18:55:52Z) - Towards Language Modelling in the Speech Domain Using Sub-word
Linguistic Units [56.52704348773307]
We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes.
With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech.
We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
arXiv Detail & Related papers (2021-10-31T22:48:30Z) - Can phones, syllables, and words emerge as side-products of
cross-situational audiovisual learning? -- A computational investigation [2.28438857884398]
We study the so-called latent language hypothesis (LLH)
LLH connects linguistic representation learning to general predictive processing within and across sensory modalities.
We explore LLH further in extensive learning simulations with different neural network models for audiovisual cross-situational learning.
arXiv Detail & Related papers (2021-09-29T05:49:46Z) - Wav-BERT: Cooperative Acoustic and Linguistic Representation Learning
for Low-Resource Speech Recognition [159.9312272042253]
Wav-BERT is a cooperative acoustic and linguistic representation learning method.
We unify a pre-trained acoustic model (wav2vec 2.0) and a language model (BERT) into an end-to-end trainable framework.
arXiv Detail & Related papers (2021-09-19T16:39:22Z) - Uncovering Constraint-Based Behavior in Neural Models via Targeted
Fine-Tuning [9.391375268580806]
We show that competing linguistic processes within a language obscure underlying linguistic knowledge.
While human behavior has been found to be similar across languages, we find cross-linguistic variation in model behavior.
Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior.
arXiv Detail & Related papers (2021-06-02T14:52:11Z) - Read Like Humans: Autonomous, Bidirectional and Iterative Language
Modeling for Scene Text Recognition [80.446770909975]
Linguistic knowledge is of great benefit to scene text recognition.
How to effectively model linguistic rules in end-to-end deep networks remains a research challenge.
We propose an autonomous, bidirectional and iterative ABINet for scene text recognition.
arXiv Detail & Related papers (2021-03-11T06:47:45Z) - Discourse structure interacts with reference but not syntax in neural
language models [17.995905582226463]
We study the ability of language models (LMs) to learn interactions between different linguistic representations.
We find that, contrary to humans, implicit causality only influences LM behavior for reference, not syntax.
Our results suggest that LM behavior can contradict not only learned representations of discourse but also syntactic agreement.
arXiv Detail & Related papers (2020-10-10T03:14:00Z) - SPLAT: Speech-Language Joint Pre-Training for Spoken Language
Understanding [61.02342238771685]
Spoken language understanding requires a model to analyze input acoustic signal to understand its linguistic content and make predictions.
Various pre-training methods have been proposed to learn rich representations from large-scale unannotated speech and text.
We propose a novel semi-supervised learning framework, SPLAT, to jointly pre-train the speech and language modules.
arXiv Detail & Related papers (2020-10-05T19:29:49Z)
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.