A Framework for Measuring Compositional Inductive Bias

• URL: http://arxiv.org/abs/2103.04180v1
• Date: Sat, 6 Mar 2021 19:25:37 GMT
• Title: A Framework for Measuring Compositional Inductive Bias
• Authors: Hugh Perkins
• Abstract summary: We present a framework for measuring the compositional inductive bias of models in emergent communications. We devise corrupted compositional grammars that probe for limitations in the compositional inductive bias of frequently used models. We propose a hierarchical model which might show an inductive bias towards relocatable atomic groups of tokens, thus potentially encouraging the emergence of words.
• Score: 0.30458514384586405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a framework for measuring the compositional inductive bias of models in the context of emergent communications. We devise corrupted compositional grammars that probe for limitations in the compositional inductive bias of frequently used models. We use these corrupted compositional grammars to compare and contrast a wide range of models, and to compare the choice of soft, Gumbel, and discrete representations. We propose a hierarchical model which might show an inductive bias towards relocatable atomic groups of tokens, thus potentially encouraging the emergence of words. We experiment with probing for the compositional inductive bias of sender and receiver networks in isolation, and also placed end-to-end, as an auto-encoder.

Related papers

• Comateformer: Combined Attention Transformer for Semantic Sentence Matching [11.746010399185437]
  We propose a novel semantic sentence matching model named Combined Attention Network based on Transformer model (Comateformer) In Comateformer model, we design a novel transformer-based quasi-attention mechanism with compositional properties. Our proposed approach builds on the intuition of similarity and dissimilarity (negative affinity) when calculating dual affinity scores.
  arXiv  Detail & Related papers  (2024-12-10T06:18:07Z)
• SHINE: Saliency-aware HIerarchical NEgative Ranking for Compositional Temporal Grounding [52.98133831401225]
  Temporal grounding, also known as video moment retrieval, aims at locating video segments corresponding to a given query sentence. We propose a large language model-driven method for negative query construction, utilizing GPT-3.5-Turbo. We introduce a coarse-to-fine saliency ranking strategy, which encourages the model to learn the multi-granularity semantic relationships between videos and hierarchical negative queries.
  arXiv  Detail & Related papers  (2024-07-06T16:08:17Z)
• Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC [102.64648158034568]
  diffusion models have quickly become the prevailing approach to generative modeling in many domains. We propose an energy-based parameterization of diffusion models which enables the use of new compositional operators. We find these samplers lead to notable improvements in compositional generation across a wide set of problems.
  arXiv  Detail & Related papers  (2023-02-22T18:48:46Z)
• Debiasing Vision-Language Models via Biased Prompts [79.04467131711775]
  We propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding. We show that debiasing only the text embedding with a calibrated projection matrix suffices to yield robust classifiers and fair generative models.
  arXiv  Detail & Related papers  (2023-01-31T20:09:33Z)
• Syntax-guided Neural Module Distillation to Probe Compositionality in Sentence Embeddings [0.0]
  We construct a neural module net based on its syntax parse and train it end-to-end to approximate the sentence's embedding. We find differences in the distillability of various sentence embedding models that broadly correlate with their performance. Preliminary evidence that much syntax-guided composition in sentence embedding models is linear.
  arXiv  Detail & Related papers  (2023-01-21T19:42:02Z)
• Pre-trained Sentence Embeddings for Implicit Discourse Relation Classification [26.973476248983477]
  Implicit discourse relations bind smaller linguistic units into coherent texts. We explore the utility of pre-trained sentence embeddings as base representations in a neural network for implicit discourse relation sense classification.
  arXiv  Detail & Related papers  (2022-10-20T04:17:03Z)
• Few-shot Text Classification with Dual Contrastive Consistency [31.141350717029358]
  In this paper, we explore how to utilize pre-trained language model to perform few-shot text classification. We adopt supervised contrastive learning on few labeled data and consistency-regularization on vast unlabeled data.
  arXiv  Detail & Related papers  (2022-09-29T19:26:23Z)
• Learning Disentangled Representations for Natural Language Definitions [0.0]
  We argue that recurrent syntactic and semantic regularities in textual data can be used to provide the models with both structural biases and generative factors. We leverage the semantic structures present in a representative and semantically dense category of sentence types, definitional sentences, for training a Variational Autoencoder to learn disentangled representations.
  arXiv  Detail & Related papers  (2022-09-22T14:31:55Z)
• Syntactic Inductive Biases for Deep Learning Methods [8.758273291015474]
  We propose two families of inductive biases, one for constituency structure and another one for dependency structure. The constituency inductive bias encourages deep learning models to use different units (or neurons) to separately process long-term and short-term information. The dependency inductive bias encourages models to find the latent relations between entities in the input sequence.
  arXiv  Detail & Related papers  (2022-06-08T11:18:39Z)
• Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation [59.01297461453444]
  We propose a hierarchical contrastive learning mechanism, which can unify hybrid granularities semantic meaning in the input text. Experiments demonstrate that our model outperforms competitive baselines on paraphrasing, dialogue generation, and storytelling tasks.
  arXiv  Detail & Related papers  (2022-05-26T13:26:03Z)
• Probing as Quantifying the Inductive Bias of Pre-trained Representations [99.93552997506438]
  We present a novel framework for probing where the goal is to evaluate the inductive bias of representations for a particular task. We apply our framework to a series of token-, arc-, and sentence-level tasks.
  arXiv  Detail & Related papers  (2021-10-15T22:01:16Z)
• Compositional Generalization via Semantic Tagging [81.24269148865555]
  We propose a new decoding framework that preserves the expressivity and generality of sequence-to-sequence models. We show that the proposed approach consistently improves compositional generalization across model architectures, domains, and semantic formalisms.
  arXiv  Detail & Related papers  (2020-10-22T15:55:15Z)

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.