Misspelling Correction with Pre-trained Contextual Language Model (2101.03204v1)

Abstract: Spelling irregularities, known now as spelling mistakes, have been found for several centuries. As humans, we are able to understand most of the misspelled words based on their location in the sentence, perceived pronunciation, and context. Unlike humans, computer systems do not possess the convenient auto complete functionality of which human brains are capable. While many programs provide spelling correction functionality, many systems do not take context into account. Moreover, Artificial Intelligence systems function in the way they are trained on. With many current NLP systems trained on grammatically correct text data, many are vulnerable against adversarial examples, yet correctly spelled text processing is crucial for learning. In this paper, we investigate how spelling errors can be corrected in context, with a pre-trained LLM BERT. We present two experiments, based on BERT and the edit distance algorithm, for ranking and selecting candidate corrections. The results of our experiments demonstrated that when combined properly, contextual word embeddings of BERT and edit distance are capable of effectively correcting spelling errors.

• The paper introduces a novel approach combining BERT's masked word predictions with an edit distance algorithm for effective spell correction.
• It demonstrates improved correction accuracy of 73.25%, further enhanced to 84.91% through stringent edit distance constraints.
• The study highlights how integrating context-aware language models with traditional methods boosts NLP resilience to noisy, user-generated text.

Exploration of Spelling Correction with a Pre-trained Contextual LLM

The paper "Misspelling Correction with Pre-trained Contextual LLM" presents an investigation into utilizing BERT (Bidirectional Encoder Representations from Transformers), a pre-trained contextual LLM, for correcting misspelled words in textual data. The context sensitivity of language is often underestimated in traditional spell-checking algorithms, which predominantly rely on dictionary-based approaches and basic statistical models. This paper offers insights into leveraging the sophisticated capabilities of BERT, potentially advancing the robustness of NLP systems against informal, noisy input data.

Motivation and Background

The paper begins by identifying a critical gap in current NLP tools: their vulnerability to spelling anomalies that naturally occur in unstructured, user-generated content. These errors can disrupt tasks such as Named Entity Recognition (NER) and Semantic Role Labeling (SRL). The authors argue that many NLP models falter on recognizing and correcting these errors, underscoring the need for models that leverage both the syntactic and semantic context provided by sentences. This research positions itself to bridge this lacuna by combining BERT-based contextual word embeddings with the edit distance algorithm to effectively identify and rectify spelling mistakes.

Methodology

Two methodologies are put forward: the first involves generating candidate corrections using BERT's masked word predictions and selecting from these via the edit distance metric. The second method reverses this order, employing edit distance first to generate candidate words, followed by ranking these candidates using BERT to achieve contextual relevance. The dataset for experimentation was derived from the CLC FCE Dataset, curated to focus on spelling errors from non-native English compositions, thereby providing diverse error types encountered in natural writing.

Key Findings and Numerical Insights

The experiments revealed promising results, with BERT’s contextual predictions achieving a correction accuracy of up to 73.25% in the initial experimental setup. By incorporating corpus constraints and aggressive filtering based on a maximum edit distance of two, precision was enhanced to 84.91%. This definitive rise demonstrates the efficacy of integrating context-aware LLMs with traditional alignment metrics like edit distance, reflecting a nuanced step forward in spelling correction capabilities. The paper establishes probabilities P(top@1|top@N) as a metric to evaluate the conditions under which edit distance optimally selects the top-ranked corrections.

Theoretical and Practical Implications

From a theoretical standpoint, this work illustrates the utility of viewing spelling correction as a masked word prediction task, an approach which might be extended to other types of grammatical error corrections. Practically, the implications are significant for improving the accuracy of NLP applications in environments rich with informal text, such as social media, user forums, and chat interfaces. As BERT and its derivatives continue to evolve, there is potential to further refine these methodologies, especially as models incorporate expansive corpora through methods like fine-tuning and transfer learning.

Future Directions

The paper suggests future work focusing on addressing corrections for errors with larger edit distances and extending this masked prediction framework to other linguistic errors, such as syntactic corrections. Additionally, as pre-trained models become more accessible, investigating the feasibility of using BERT for both detection and correction of errors within a single pipeline could be an intriguing area of future exploration.

In conclusion, this research contributes to the ongoing development of more resilient NLP systems, highlighting the importance of contextual understanding in the domain of spelling correction. By integrating sophisticated models like BERT with traditional methods, it sets the stage for potential advancements in the preprocessing of linguistic data, improving the overall performance across various NLP tasks.