An Empirical Investigation of Beam-Aware Training in Supertagging

• URL: http://arxiv.org/abs/2010.04980v1
• Date: Sat, 10 Oct 2020 12:25:18 GMT
• Title: An Empirical Investigation of Beam-Aware Training in Supertagging
• Authors: Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon
• Abstract summary: Structured prediction is often approached by training a locally normalized model with maximum likelihood and decoding approximately with beam search. Beam-aware training aims to address these problems, but it is not yet widely used due to a lack of understanding about how it impacts performance.
• Score: 29.819517845454815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structured prediction is often approached by training a locally normalized model with maximum likelihood and decoding approximately with beam search. This approach leads to mismatches as, during training, the model is not exposed to its mistakes and does not use beam search. Beam-aware training aims to address these problems, but unfortunately, it is not yet widely used due to a lack of understanding about how it impacts performance, when it is most useful, and whether it is stable. Recently, Negrinho et al. (2018) proposed a meta-algorithm that captures beam-aware training algorithms and suggests new ones, but unfortunately did not provide empirical results. In this paper, we begin an empirical investigation: we train the supertagging model of Vaswani et al. (2016) and a simpler model with instantiations of the meta-algorithm. We explore the influence of various design choices and make recommendations for choosing them. We observe that beam-aware training improves performance for both models, with large improvements for the simpler model which must effectively manage uncertainty during decoding. Our results suggest that a model must be learned with search to maximize its effectiveness.

Related papers

• What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? [83.83230167222852]
  We find that a model's generalization behavior can be effectively characterized by a training metric we call pre-memorization train accuracy. By connecting a model's learning behavior to its generalization, pre-memorization train accuracy can guide targeted improvements to training strategies.
  arXiv  Detail & Related papers  (2024-11-12T09:52:40Z)
• Attribute-to-Delete: Machine Unlearning via Datamodel Matching [65.13151619119782]
  Machine unlearning -- efficiently removing a small "forget set" training data on a pre-divertrained machine learning model -- has recently attracted interest. Recent research shows that machine unlearning techniques do not hold up in such a challenging setting.
  arXiv  Detail & Related papers  (2024-10-30T17:20:10Z)
• StochCA: A Novel Approach for Exploiting Pretrained Models with Cross-Attention [2.66269503676104]
  We introduce a novel fine-tuning method, called cross-attention (StochCA), specific to Transformer architectures. This method modifies the Transformer's self-attention mechanism to selectively utilize knowledge from pretrained models during fine-tuning. Our experimental results show the superiority of StochCA over state-of-the-art approaches in both areas.
  arXiv  Detail & Related papers  (2024-02-25T13:53:49Z)
• Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines [83.65380507372483]
  Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box. This paper shows how to leverage recent advances in NLP and multi-modal learning to augment a pre-trained model with search engine retrieval.
  arXiv  Detail & Related papers  (2023-11-29T05:33:28Z)
• Robust Meta Learning for Image based tasks [1.1718589131017048]
  A machine learning model that generalizes well should obtain low errors on unseen test examples. We propose a novel robust meta-learning method, which is more robust to the image-based testing tasks. In experiments, we demonstrate that our algorithm not only has better generalization performance but also robust to different unknown testing tasks.
  arXiv  Detail & Related papers  (2023-01-30T07:08:37Z)
• Post-hoc Uncertainty Learning using a Dirichlet Meta-Model [28.522673618527417]
  We propose a novel Bayesian meta-model to augment pre-trained models with better uncertainty quantification abilities. Our proposed method requires no additional training data and is flexible enough to quantify different uncertainties. We demonstrate our proposed meta-model approach's flexibility and superior empirical performance on these applications.
  arXiv  Detail & Related papers  (2022-12-14T17:34:11Z)
• Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search [12.317568257671427]
  We propose a simple and effective multi-level optimization framework called learning from mistakes (LFM) The primary objective is to train a model to perform effectively on target tasks by using a re-weighting technique to prevent similar mistakes in the future. In this formulation, we learn the class weights by minimizing the validation loss of the model and re-train the model with the synthetic data from the image generator weighted by class-wise performance and real data.
  arXiv  Detail & Related papers  (2021-12-01T04:56:49Z)
• Exploring Strategies for Generalizable Commonsense Reasoning with Pre-trained Models [62.28551903638434]
  We measure the impact of three different adaptation methods on the generalization and accuracy of models. Experiments with two models show that fine-tuning performs best, by learning both the content and the structure of the task, but suffers from overfitting and limited generalization to novel answers. We observe that alternative adaptation methods like prefix-tuning have comparable accuracy, but generalize better to unseen answers and are more robust to adversarial splits.
  arXiv  Detail & Related papers  (2021-09-07T03:13:06Z)
• Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation [3.728946517493471]
  MEEE is a model-ensemble method that consists of optimistic exploration and weighted exploitation. Our approach outperforms other model-free and model-based state-of-the-art methods, especially in sample complexity.
  arXiv  Detail & Related papers  (2021-07-05T07:18:20Z)
• Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning [109.74041512359476]
  We study a number of design decisions for the predictive model in visual MBRL algorithms. We find that a range of design decisions that are often considered crucial, such as the use of latent spaces, have little effect on task performance. We show how this phenomenon is related to exploration and how some of the lower-scoring models on standard benchmarks will perform the same as the best-performing models when trained on the same training data.
  arXiv  Detail & Related papers  (2020-12-08T18:03:21Z)
• Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning [93.1435980666675]
  We show how optimistic exploration can be easily combined with state-of-the-art reinforcement learning algorithms. Our experiments demonstrate that optimistic exploration significantly speeds-up learning when there are penalties on actions.
  arXiv  Detail & Related papers  (2020-06-15T18:37:38Z)

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.