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Ungrammatical Syntax-based In-context Example Selection for Grammatical Error Correction


Core Concepts
Selecting in-context examples based on the similarity of ungrammatical syntactic structures can effectively boost the performance of large language models on the task of grammatical error correction.
Abstract
The paper proposes a novel in-context learning (ICL) workflow for grammatical error correction (GEC) that leverages ungrammatical syntactic similarity to select the most relevant examples as demonstrations for large language models (LLMs). Key highlights: Existing ICL example selection methods do not consider syntactic information, which is crucial for the syntax-oriented GEC task. The authors apply two syntactic similarity algorithms, Tree Kernel and Polynomial Distance, on dependency trees generated by a GEC-oriented parser to identify the most similar examples to the test input. They also explore a two-stage selection strategy, where a fast and general method (BM25 or BERT representation) is used in the first stage to filter out irrelevant instances, followed by the more powerful syntax-based method in the second stage. Experiments on English GEC datasets show that the proposed ungrammatical-syntax-based selection strategies outperform conventional word-matching or semantics-based methods, improving the performance of LLMs by around 3 F0.5 points on average. The authors emphasize the importance of syntactic information for syntax-related tasks and believe their methods can be transferred to other areas like machine translation and information extraction.
Stats
The GEC datasets used have around 34,000 training sentences with 66% error rate, 4,477 BEA-2019 test sentences, and 1,312 CoNLL-2014 test sentences with 72% error rate. The LLMs used are LLaMA-2 (7B and 13B) and GPT-3.5.
Quotes
"To the best of our knowledge, no existing work on ICL example selection has taken syntactic information into consideration. However, GEC aims to correct grammatical errors and is a typical syntax-oriented task." "Comparing with semantic similarity, syntactic similarity of text is less-studied." "We want to re-draw the natural language processing (NLP) community's attention to the significance of syntactic information. In this work, we show that syntax-related knowledge helps LLMs correct grammatical errors better."

Deeper Inquiries

How can the proposed ungrammatical-syntax-based selection strategy be extended to other syntax-related tasks beyond GEC, such as machine translation or information extraction?

The ungrammatical-syntax-based selection strategy proposed for GEC can be extended to other syntax-related tasks by adapting the approach to the specific requirements of each task. For machine translation, the selection strategy can focus on identifying syntactic structures that are common between source and target languages to improve translation accuracy. By selecting in-context examples based on syntactic similarities, the model can learn to generate more linguistically accurate translations. In the case of information extraction, the strategy can be applied to identify and extract relevant information based on syntactic patterns in the text. By selecting examples that share similar syntactic structures, the model can improve its ability to extract key information accurately.

How can the proposed ungrammatical-syntax-based selection strategy be extended to other syntax-related tasks beyond GEC, such as machine translation or information extraction?

To further improve the in-context example selection for GEC, other types of syntactic information beyond dependency trees can be leveraged. One potential approach is to incorporate constituent trees, which provide a different perspective on the syntactic structure of sentences. By considering both dependency and constituent trees, the model can capture a more comprehensive view of the syntax and make more informed decisions when selecting in-context examples. Additionally, syntactic features such as part-of-speech tags, syntactic dependencies, and syntactic roles can be utilized to enhance the selection process. By incorporating a wider range of syntactic information, the model can improve its ability to identify relevant examples for GEC.

Can the two-stage selection framework be generalized to other NLP tasks, or is it specific to the GEC problem?

The two-stage selection framework can be generalized to other NLP tasks beyond GEC. The concept of first selecting a candidate set based on general features and then refining the selection using more specific criteria can be applied to various tasks. For tasks like sentiment analysis, text classification, or named entity recognition, the framework can be adapted to first filter out irrelevant examples based on word similarity or semantic features and then rank the remaining examples based on more task-specific criteria. By tailoring the selection process to the requirements of each task, the two-stage framework can enhance the performance of models across a wide range of NLP tasks.
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