Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study (1808.05697v3)

Abstract: Several papers investigate Active Learning (AL) for mitigating the data dependence of deep learning for natural language processing. However, the applicability of AL to real-world problems remains an open question. While in supervised learning, practitioners can try many different methods, evaluating each against a validation set before selecting a model, AL affords no such luxury. Over the course of one AL run, an agent annotates its dataset exhausting its labeling budget. Thus, given a new task, an active learner has no opportunity to compare models and acquisition functions. This paper provides a large scale empirical study of deep active learning, addressing multiple tasks and, for each, multiple datasets, multiple models, and a full suite of acquisition functions. We find that across all settings, Bayesian active learning by disagreement, using uncertainty estimates provided either by Dropout or Bayes-by Backprop significantly improves over i.i.d. baselines and usually outperforms classic uncertainty sampling.

Deep Bayesian Active Learning for Natural Language Processing: An Empirical Study

The paper "Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study" by Aditya Siddhant and Zachary C. Lipton presents a comprehensive exploration into the viability of Active Learning (AL) within the scope of NLP. While deep learning methods have achieved significant advancements across a multitude of tasks, their inherent data dependency remains a challenge, particularly under constraints of limited annotation budgets. This research makes a significant contribution by examining whether Bayesian Active Learning (BAL) can be systematically leveraged to mitigate these challenges across various NLP tasks.

Overview

The focus of this paper is a large-scale empirical investigation into deep active learning strategies, particularly Bayesian approaches, across multiple tasks including Sentiment Classification (SC), Named Entity Recognition (NER), and Semantic Role Labeling (SRL). The authors evaluate different models and acquisition functions, focusing on Bayesian methods like Monte Carlo Dropout and Bayes-by-Backprop for uncertainty estimation. The primary emphasis is on whether such approaches can consistently outperform independent and identically distributed (i.i.d) baselines when applied to unseen datasets without pre-tuning hyperparameters based on labels—a necessity given the exhaustive annotation required per AL iteration.

Methodology and Experiments

Several key methodologies are implemented:

• Bayesian Uncertainty Estimation: Bayesian formulations like Dropout and Bayes-by-Backprop provide avenues to estimate uncertainty in deep networks, a critical aspect of AL where uncertain samples are prioritized for annotation.
• Acquisition Functions: Acquisition strategies such as Bayesian Active Learning by Disagreement (BALD) are central to this paper. BALD measures the informativeness of samples by quantifying model disagreements over multiple stochastic forward passes. This is benchmarked against classical uncertainty sampling techniques.

The experimental design involved a rigorous assessment across varied tasks and datasets. For SC, datasets like TrecQA and a sentiment analysis collection were utilized. NER was explored through datasets like CoNLL 2003 and OntoNotes 5.0, while SRL assessments used datasets such as CoNLL 2005 and 2012. Hyper-parameters were consistently set on warm-started data to maintain an unbiased evaluation, and the experimental framework spanned over 40 distinct configurations, averaging results across multiple runs.

Results and Implications

The findings indicate that deep Bayesian AL methods not only outperform shallow baselines but also consistently achieve better data efficiency than i.i.d. baselines. Notably, the Bayesian variants DO-BALD and BB-BALD demonstrate significant improvements in most settings, effectively confirming the practical utility of employing sophisticated uncertainty measures in a wide array of NLP applications.

The results suggest compelling implications for the integration of BAL in real-world NLP tasks, particularly in environments where data annotation is limited or expensive. This research provides a methodological pathway for initiating AL without pre-emptive trial and error on model configurations—an essential attribute for practical deployment.

Conclusion and Future Developments

This paper positions Bayesian Active Learning as a reliable strategy for optimizing deep learning-driven NLP tasks within constrained annotation budgets. Moving forward, the integration of more advanced Bayesian neural architectures might further enhance the efficacy of AL in NLP. There is potential to extend such analyses to more complex, higher-dimensional NLP datasets, and future work could also explore the combination of Bayesian techniques with semi-supervised learning paradigms to further alleviate data dependency.

The research presented illustrates the effectiveness of Bayesian Active Learning not only in reducing the data requirements for NLP tasks but in providing a reliable framework applicable to diverse and unforeseen challenges. Such practical insights form a robust groundwork for future advancements in active and semi-supervised learning methodologies within the AI field.