Generating Contextually Relevant Sentences with Transformer Models

Core Concepts

To develop a model that generates informative and contextually relevant sentence-contexts for given keywords, benefiting various natural language understanding and generation applications.

Abstract

In the era of information abundance, providing users with contextually relevant and concise information is crucial. The Keyword in Context (KIC) generation task plays a vital role in applications like search engines, personal assistants, and content summarization. This paper presents a novel approach using the T5 transformer model to generate unambiguous and brief sentence-contexts for specific keywords by leveraging data from the Context-Reverso API. The study involves creating datasets, training models, and developing an application for learning new English words with generated contexts. By utilizing external resources like APIs, the work aims to address challenges in generating short contexts while mitigating ambiguity in sentence construction. The experiments involve fine-tuning pre-trained models like T5-small and T5-base on custom datasets to generate context sentences that incorporate given keywords meaningfully and unambiguously. Evaluation metrics such as BLEU and METEOR are used to assess the quality of generated text compared to reference text.

Stats

Our dataset contains diverse sentences incorporating target keywords.
T5-small has 60 million parameters.
T5-base has 220 million parameters.
GPT-2 has 117 million parameters.

Quotes

Key Insights Distilled From

Contextual Clarity

by Ruslan Musae... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08103.pdf

Deeper Inquiries

How can the model's performance be enhanced beyond BLEU and METEOR scores

To enhance the model's performance beyond BLEU and METEOR scores, several strategies can be implemented. Firstly, incorporating human evaluation through crowd-sourcing platforms can provide qualitative insights into the generated sentences' relevance and coherence. Human annotators can assess factors like fluency, informativeness, and contextuality that automated metrics might overlook. Additionally, fine-tuning the model with domain-specific data or implementing reinforcement learning techniques to reward more accurate outputs could further improve performance. Utilizing advanced evaluation methods such as ROUGE for text summarization tasks or leveraging pre-trained language models like BERT for contextual understanding can also offer a comprehensive assessment of the model's capabilities.

What potential biases or limitations could arise from using external APIs for data sourcing

When using external APIs for data sourcing, potential biases and limitations may arise. One significant limitation is the quality and representativeness of the data obtained from these APIs. The bias present in the API's dataset could transfer to the trained model, impacting its generalizability across diverse contexts or demographics. Moreover, reliance on a single API source may introduce sampling bias if certain types of examples are overrepresented compared to others. Privacy concerns regarding user-generated content accessed through APIs should also be considered to ensure compliance with data protection regulations. Lastly, changes in API endpoints or policies could disrupt data retrieval processes and affect model training consistency.

How might incorporating additional datasets impact the model's performance

Incorporating additional datasets into the training process can have a profound impact on the model's performance by enriching its knowledge base and enhancing its ability to generalize across different scenarios. By introducing diverse datasets from various sources or domains, the model gains exposure to a wider range of linguistic patterns and contexts, thereby improving its adaptability when generating sentences for new keywords or topics outside its initial training set. However, careful curation and preprocessing of these additional datasets are crucial to avoid introducing noise or conflicting information that could degrade performance. Regular validation against benchmark datasets after integrating new sources is essential to monitor any changes in accuracy or efficiency resulting from dataset expansion.