Neural Network Acceptability Judgments (1805.12471v3)

Abstract: This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set of 10,657 English sentences labeled as grammatical or ungrammatical from published linguistics literature. As baselines, we train several recurrent neural network models on acceptability classification, and find that our models outperform unsupervised models by Lau et al (2016) on CoLA. Error-analysis on specific grammatical phenomena reveals that both Lau et al.'s models and ours learn systematic generalizations like subject-verb-object order. However, all models we test perform far below human level on a wide range of grammatical constructions.

• The paper introduces CoLA, the largest dataset for grammatical acceptability judgments, enabling systematic evaluation of neural models.
• LSTM-based models trained on CoLA outperform unsupervised approaches but still fall short of human performance.
• Detailed error analysis reveals that current models struggle with generalizing diverse linguistic constructions beyond in-domain data.

Essay on "Neural Network Acceptability Judgments"

The paper "Neural Network Acceptability Judgments" by Warstadt, Singh, and Bowman investigates the ability of artificial neural networks (ANNs) to judge the grammatical acceptability of English sentences—a fundamental aspect of linguistic competence.

The authors introduce the Corpus of Linguistic Acceptability (CoLA), a dataset comprising 10,657 English sentences labeled as either grammatical or ungrammatical, curated from established linguistics literature. This compilation provides a substantial resource for evaluating the performance of neural networks on acceptability classification tasks.

Contributions and Baselines

The paper's contributions can be categorized as follows:

1. Creation of CoLA: CoLA is the largest dataset of its kind, designed specifically for grammatical acceptability tasks. It includes sentences from a range of linguistic sources, thus encompassing a wide variety of grammatical constructions.
2. Baseline Model Training: Several recurrent neural network models, including LSTM-based models, were trained on the CoLA dataset. These models showed superior performance compared to previous unsupervised models by Lau et al. (2016).
3. Error Analysis: Detailed error analysis on specific grammatical phenomena demonstrated that the trained models could generalize systematically, recognizing patterns such as subject-verb-object order, while still falling short of human-level performance on more varied constructions.
4. Impact of Supervised Training: The paper evaluated the impact of supervised training by differing the domain and quantity of training data, demonstrating that supervised learning significantly enhances the performance of grammatical acceptability classifiers.

Performance Metrics

The authors benchmark their models against several baselines and report metrics including accuracy and Matthews Correlation Coefficient (MCC). Key findings are summarized as follows:

• The best model achieved an MCC of 0.341 on the in-domain test set, significantly lower than human performance (MCC of 0.713).
• Models pre-trained on an auxiliary real/fake discrimination task leveraging ELMo-style contextualized word embeddings showed notable improvements.
• Models faced a considerable drop in performance when evaluated on out-of-domain data, highlighting potential overfitting to specific data pools rather than acquiring generalized grammatical knowledge.

Implications and Further Research

From a practical perspective, the development and refinement of grammatical acceptability classifiers can enhance various natural language understanding (NLU) tasks, ranging from machine translation to automated text generation. This work underscores the importance of extensive and varied datasets for training models that need to grasp intricate linguistic rules.

Theoretically, these results inform debates on the learnability of grammar. The performance gaps reveal the limitations of current ANN models in acquiring human-like linguistic competence purely through data-driven learning. This supports, albeit indirectly, the argument from the poverty of the stimulus (APS), which posits that human linguistic competence cannot be fully explained by exposure to language data alone.

Future Directions

Future research could explore different architectures and pretraining tasks to improve the generalization capabilities of acceptability classifiers. Enhanced training methodologies, such as multi-task learning with other syntactic or semantic tasks, might also provide richer linguistic insights to neural models.

Moreover, addressing the domain-specific overfitting problem remains a priority, potentially by employing more sophisticated regularization techniques or vastly larger and more diverse corpora to mitigate the biases inherent in training datasets.

Conclusion

"Neural Network Acceptability Judgments" establishes a foundation for the systematic evaluation of ANNs in a nuanced linguistic context. The introduction of CoLA, coupled with the comparative analysis of various models, provides crucial insights into where current ANNs stand relative to human linguistic competence. This work is a significant step toward understanding and advancing the capabilities of neural networks in the domain of grammatical acceptability.

As the field progresses, the integration of more context-aware, interpretable, and generalized models will be essential to bridging the gap between artificial and human linguistic performance. This research highlights critical areas for improvement and sets a benchmark for future studies in grammatical acceptability within computational linguistics.