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Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks

Zi-Yi Dou, Keyi Yu, Antonios Anastasopoulos

arXiv:1908.10423v1cs.CL

TL;DR

General language-representation methods may be sub-optimal for low-resource NLU, and multi-task learning may favor tasks with more data. This paper tests MAML and its variants, reporting stronger baseline comparisons on four low-resource GLUE tasks and more effective adaptation to a new task.

  • Problem

    Multi-task learning may favor tasks with substantially more data, motivating approaches for low-resource NLU.

    Source: §1 · p. 1

  • Method

    The paper investigates MAML, first-order MAML, and Reptile for learning initializations adaptable to NLU tasks with minimal training data.

    Source: §1 · p. 2

  • Results

    The approaches outperform strong baselines on four low-resource GLUE tasks, and their representations adapt to a new task more effectively than baseline representations.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The study suggests promising applications of optimization-based meta-learning algorithms in NLU.

    Source: §5 · p. 5

    The authors identify validating the algorithms on other datasets as future work.

    Source: §5 · p. 5

Abstract

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Learning general representations of text is a fundamental problem for many natural language understanding (NLU) tasks. Previously, researchers have proposed to use language model pre-training and multi-task learning to learn robust representations. However, these methods can achieve sub-optimal performance in low-resource scenarios. Inspired by the recent success of optimization-based meta-learning algorithms, in this paper, we explore the model-agnostic meta-learning algorithm (MAML) and its variants for low-resource NLU tasks. We validate our methods on the GLUE benchmark and show that our proposed models can outperform several strong baselines. We further empirically demonstrate that the learned representations can be adapted to new tasks efficiently and effectively.

1 Introduction

The introduction frames general, adaptable language representations as important for NLU, while noting that multi-task learning may favor higher-resource tasks. It motivates MAML variants and reports gains on four low-resource GLUE tasks and adaptation to a new task.

  • NLU research seeks robust, flexible language representations that can adapt efficiently to new tasks or domains.Source: §1 · p. 1
  • BERT exemplifies language-model pre-training as an approach to learning universal language representations.Source: §1 · p. 1
  • Multi-task learning may favor tasks with more data, while MT-DNN combines BERT pre-training with multi-task learning.Source: §1 · p. 1, Figure 1 · p. 1
  • Meta-learning seeks initializations useful for fine-tuning across tasks with minimal training data; the paper investigates MAML, first-order MAML, and Reptile.Source: §1 · p. 2
  • The approaches outperform strong baselines on four low-resource GLUE tasks, and their representations adapt to a new task more effectively than baseline representations.Source: §1 · p. 2

2 Proposed Approaches

The proposed approach applies optimization-based meta-learning to language representations for NLU, aiming to support fast adaptation to target tasks with few samples. Its framework combines pre-training, meta-learning, and fine-tuning, with task sampling and MetaUpdate choices shaping the learning process.

2 Proposed Approaches

  • The section introduces meta-learning concepts and then describes how the algorithms are applied to language representation learning.Source: §2 · p. 2

2.1 Background: Meta Learning

  • The approach investigates optimization-based meta-learning for NLU, where learning across tasks aims to support fast adaptation to new training data with few samples.The paper focuses on MAML and its variants.Source: §2.1 · p. 2

2.2 General Framework

  • The framework has three stages: BERT-style pre-training, meta-learning, and fine-tuning on the target task.During meta-learning, it samples tasks and applies k gradient steps per task; the data for MetaUpdate differs from the data used for those steps.Source: §2.2 · p. 2

2.3 The MetaUpdate Step

  • MAML updates a metaobjective; FOMAML omits second derivatives, while Reptile moves weights toward task-adapted parameters and has reported competitive or superior performance to MAML.MAML's second derivatives can be computationally and memory intensive.Source: §2.3 · p. 3

2.4 Choosing the Task Distributions

  • The authors test uniform, dataset-size-proportional (PPS), and mixed task sampling, with mixed sampling selecting the target task exclusively after uniform sampling each epoch.The table reports results on GLUE test sets, with metrics varying by task and the best result highlighted.Source: §2.4 · p. 3, Table 1 · p. 3

3 Experiments

The experiments compare meta-learning algorithms with strong baselines on low-resource GLUE tasks, examine task-distribution and meta-gradient settings, and test transfer to SciTail. The reported results favor meta-learning, especially Reptile, and show stronger transfer performance when less data are available.

3 Experiments

  • English GLUE experiments omit WNLI, use SST-2, QQP, MNLI, and QNLI as auxiliary tasks and CoLA, MRPC, STS-B, and RTE as targets, and compare against BERT and MT-DNN.The proposed algorithms are implemented upon BERTBASE.Source: §3 · p. 3

3.1 Results

  • On GLUE test results, meta-learning generally outperforms strong baselines, with Reptile performing best; MT-DNN's matching PPS sampling supports comparison with multi-task learning.The authors interpret the improvements as evidence of better representations than multi-task learning and report Reptile as more effective and efficient than MAML in their setting.Source: §3.1 · p. 4

3.2 Ablation Studies

  • Reptile with PPS sampling performs best among the tested task distributions, and larger amounts of auxiliary-task data generally correspond to better performance.The uniform-sampling comparison uses the same number of training steps as PPS; mixed sampling tests 2:1 and 5:1 ratios.Source: §3.2 · p. 4

3.3 Transferring to New Tasks

  • On unseen SciTail, the model beats MT-DNN across 0.1%, 1%, 10%, and 100% training-data settings, with stronger relative gains at low data, especially against BERT.SciTail was not encountered during meta-learning.Source: Figure 2 · p. 4, §3.3 · pp. 4–5

Effect of Hyperparameters for Meta-Gradients

  • k=5 is optimal; fewer steps resemble joint training, while larger k can make gradients uninformative and larger α may degrade performance.The authors conclude that both ablations demonstrate the importance of informative meta-gradients.Source: Effect of Hyperparameters for… · p. 4

4 Related Work

Related work spans general language representations, multi-task learning, and meta-learning. The paper positions its study against limited prior exploration of meta-learning for NLU and comparatively little attention to Reptile.

  • Representation work progressed from word and sentence representations to contextualized embeddings, including BERT's bidirectional-Transformer approach.Source: §4 · p. 5
  • Liu et al. (2019b) combine multi-task learning with language-model pre-training and report that the approaches are complementary.Source: §4 · p. 5
  • Meta-learning for NLU remained underexplored; prior work applied first-order MAML to machine translation and MAML to dialogue domain adaptation, while Reptile received less attention.Source: §4 · p. 5

5 Conclusion

The study investigates three optimization-based meta-learning algorithms for low-resource NLU, reports effective adaptation to new tasks, and suggests promising applications in NLU. Future work includes more sophisticated training strategies and validation on other datasets.

5 Conclusion

  • The study investigates three optimization-based meta-learning algorithms for low-resource NLU and reports that learned representations adapt effectively to new tasks.The authors describe the study as suggesting promising applications of meta-learning in NLU.Source: §5 · p. 5
  • Future work includes integrating more sophisticated meta-learning training strategies and validating the algorithms on other datasets.Source: §5 · p. 5

A The GLUE and SciTail Datasets

The section describes GLUE task categories and their prediction goals, alongside SciTail’s entailment task. It also notes that WNLI is excluded and that Table 4 summarizes dataset information and statistics.

A The GLUE and SciTail Datasets

B Implementation Details

The implementation starts from pre-trained BERTBASE and uses specified optimization and sampling settings for meta-learning and fine-tuning. It omits MT-DNN’s stochastic answer network for efficiency.

B Implementation Details

C Linguistic Information

The paper probes linguistic information across model layers using ten tasks, training only a classifier while freezing model parameters. Results contrast BERT’s broader linguistic representations with task-tailored alternatives and report that layer-wise patterns are not always monotonic.

C Linguistic Information

  • The study uses ten probing tasks to examine linguistic information captured by each model layer, with model parameters frozen and only the classification layer trained.The probes test surface, syntactic, or semantic information; Table 5 reports accuracy numbers on the ten tasks.Source: C Linguistic Information · p. 8, Table 5 · p. 9
  • BERT captures more surface, syntactic, and semantic information than the other models, while MT-DNN and the proposed models learn representations more tailored to GLUE tasks.Source: C Linguistic Information · p. 8
  • The proposed models outperform MT-DNN on probing tasks, and Reptile captures more general linguistic information than the other two meta-learning algorithms.Because Reptile also outperformed the other two models on GLUE, the authors suggest it may be more suitable for NLU tasks.Source: C Linguistic Information · p. 8
  • Layer-wise linguistic information does not always follow a monotonic trend, contrary to findings that BERT’s middle layers are more transferable and contain more syntactic and semantic information.The authors conjecture that broad definitions of syntax and semantics and limited probing-task coverage may explain the difference; SOMO is monotonic, while middle layers do better on SubNum.Source: C Linguistic Information · p. 8
  • Lower layers perform rather poorly on the word content task, which tests recovery of information about the original words.Source: C Linguistic Information · p. 8
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