Context-Aware Text Generation: Reducing Vagueness in Generated Sentences

• Published in: 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET) pp. 1 - 6
• Main Authors: Patkar, Uday, Kulkarni, Vedant, Bhavar, Hrishikesh, Atpadkar, Samir, Malkhede, Sanskar, Wanjari, Shefali, Bala, Kumkum
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.01.2025
• Subjects: Accuracy; Annotations; Attention Mechanism; Attention mechanisms; BERT; Coherence; Context modeling; Context-Aware Mechanisms; Data Annotation; Natural language processing; Prevention and mitigation; Readability; Semantics; Testing; Text Generation; Vagueness Mitigation
• DOI: 10.1109/ICAET63349.2025.10932221

In the world of NLP, we often run into a tricky issue. This is due to the confusion and fuzziness that can happen when we generate text. It's especially noticeable with tasks like chat-bots, creating content, or interactive storytelling. Here, we bring up a smart solution to tackle these challenges. We use advanced semantic tools that aren't in the latest models like GPT-3. With this approach, models can understand context better from the text and user inputs around them. This makes language generation much clearer and tidier. To achieve this goal, our research aims to highlight collections of vague sentence datasets. Then, we'll build context-aware models using BERT embeddings. Through this method, new algorithms will help models focus better on the text and produce smoother sentences. We've worked hard on these improved versions, testing them a lot! They show some great performance highlights with 54 objective scores! Plus, they've been evaluated for readability through user studies. As far as we know, this is the very first study to address excessive vagueness for crafting better text generation systems. We're paving the way for NLP by making accurate and user-friendly models that meet specific output needs.