Improving Split and Rephrase through Data Refinement: Reducing Hallucinations and Increasing Sentence Splits

Core Concepts

A simple and practical data refinement approach using natural language inference (NLI) classification and reversing the order of simple sentences can improve the performance of Split and Rephrase models by reducing hallucinations and increasing the number of sentence splits.

Abstract

The paper presents a data refinement approach to improve the performance of Split and Rephrase, a text simplification task that breaks down a complex sentence into shorter, simpler ones without altering the meaning.
The key highlights are:

Data Refinement:

Removing instances where the complex sentence does not entail at least one of the simpler sentences, using an NLI classifier. This helps suppress hallucinations.
Reversing the order of the simple sentences to prevent the model from simply reproducing the input complex sentence.

Experiments:

The proposed approach, applied to the WikiSplit dataset, creates a new dataset called WikiSplit++.
Evaluations on manually curated datasets (HSplit, Wiki-BM, Cont-BM) show that T5 models trained on WikiSplit++ outperform baselines in terms of reducing hallucinations and increasing the number of sentence splits, even with fewer training instances.

Generalization:

The data refinement approach is also applied to other datasets like MinWikiSplit and BiSECT, demonstrating its generality.

Ablation Study:

Both NLI filtering and sentence-order reversing contribute to the improvements, with the combination of the two techniques yielding the best results.

Overall, the paper presents a simple yet effective data refinement method that can significantly improve the quality of Split and Rephrase models.

Stats

The complex sentence "In such event, IBM reserves the right to modify the terms of the Special Bid or to cancel your Special Bid authorisation." can be split into two simple sentences: "In such an event, IBM reserves the right to modify the terms of the Special Bid." and "IBM can also cancel your Special Bid authorisation."
The complex sentence "It debuted at number 24 on the US "Billboard" 200, and at number 70 in Canada." can be split into two simple sentences: "It debuted at number 24 on the "Billboard" 200, one of the top debuts of that week." and "The album debuted at number 70 in Canada."
The complex sentence "A pink Hippo-like diplodorian, he can produce bubbles from his mouth." can be split into two simple sentences: "A pink Hippo-like diplodorian." and "A blue diplodorian who can produce staples from his mouth."

Quotes

"Hallucinations, defined as the generation of unfaithful or nonsensical text (Ji et al., 2023), are commonly observed in natural language generation and may be caused by low-quality training datasets, as illustrated in the table."
"To address these issues, we propose a simple and practical dataset refinement approach."

Key Insights Distilled From

WikiSplit++: Easy Data Refinement for Split and Rephrase

by Hayato Tsuka... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09002.pdf

Deeper Inquiries

How could the data refinement approach be extended to other text simplification tasks beyond Split and Rephrase

The data refinement approach used in Split and Rephrase tasks can be extended to other text simplification tasks by adapting the filtering criteria and dataset modifications to suit the specific requirements of those tasks. For instance, in tasks like text summarization or paraphrasing, the data refinement process can involve removing instances where the summary or paraphrase does not capture the essence of the original text accurately. Additionally, techniques such as domain-specific filtering can be applied to ensure that the simplified versions maintain the domain-specific terminology and context. By tailoring the data refinement approach to the nuances of different text simplification tasks, the quality and accuracy of the generated outputs can be significantly improved.

What other techniques, beyond NLI classification and sentence-order reversing, could be explored to further improve the quality of Split and Rephrase models

Beyond NLI classification and sentence-order reversing, several other techniques can be explored to enhance the quality of Split and Rephrase models:

Semantic Similarity Measures: Utilizing advanced semantic similarity algorithms to ensure that the simplified sentences retain the core meaning of the original text.
Domain-Specific Language Models: Fine-tuning pre-trained language models on domain-specific data to improve the generation of contextually relevant and accurate simplifications.
Adversarial Training: Incorporating adversarial training techniques to encourage the model to generate diverse and accurate simplifications while penalizing hallucinations.
Multi-Task Learning: Training the model on multiple related tasks simultaneously, such as paraphrasing or text summarization, to improve the overall understanding and generation of simplified text.
Human-in-the-Loop Approaches: Integrating human feedback loops to validate and refine the generated simplifications, ensuring high quality and accuracy.

How might the data refinement approach be adapted to handle domain-specific language, such as legal or medical texts, where the vocabulary and sentence structures may differ significantly from the general domain

Adapting the data refinement approach to handle domain-specific language, such as legal or medical texts, involves customizing the filtering criteria and dataset modifications to suit the unique characteristics of these domains:

Domain-Specific NLI Classifiers: Training NLI classifiers on domain-specific data to accurately assess the entailment between complex and simplified sentences in legal or medical contexts.
Specialized Vocabulary Filtering: Implementing filters to ensure that the simplified sentences contain domain-specific terminology and adhere to the language conventions of legal or medical texts.
Rule-Based Constraints: Incorporating rule-based constraints specific to legal or medical language structures to guide the generation of accurate and contextually relevant simplifications.
Expert Annotation: Involving domain experts to annotate and validate the dataset, ensuring that the simplified versions maintain the legal or medical accuracy and integrity.
Fine-Tuning on Domain-Specific Data: Fine-tuning the models on large-scale legal or medical text corpora to improve the understanding and generation of simplified text within these specialized domains.