0. Research Topic and Academic Contributions

This post delves into how to carefully craft the research topic and articulate the academic contributions of your paper. By structuring your topic and contributions effectively, you can clearly convey the core value of your study to readers and outline the structure of your paper.

0-1. Formulating the Research Topic

1. Defining the Problem: 'Why is this research necessary?'
   The first step is to clearly define the problem you aim to solve, emphasizing its significance both academically and practically.

   • Context of the Problem:
     Briefly describe the general phenomenon or problem related to your topic.
     Example: "While recent advancements in natural language processing (NLP) have significantly improved translation systems, models that incorporate contextual similarity across multiple languages remain limited."

   • Specific Problem:
     Identify unresolved issues or limitations in existing approaches.
     Example: "Existing translation models rely heavily on single-language data, making it challenging to learn contextual similarities across languages."

   • Real-World Examples:
     Demonstrate the severity of the problem with concrete data or examples.
     Example: "In English, the word 'bank' can mean both 'financial institution' and 'riverbank,' but current models struggle to distinguish between these meanings based on context."

2. Motivation: 'Why should this problem be solved?'
   Emphasize the necessity and value of addressing this problem by reviewing prior research and identifying gaps.

   • Differentiation from Prior Studies:
     "Current translation models depend on word-level one-to-one mappings and fail to effectively learn cross-linguistic context."
   • Importance of the Research:
     "Incorporating multilingual contextual similarity into models can significantly enhance global communication and the development of language technologies."

3. Formulating Research Questions (RQs)
   Clearly define the central questions your research addresses in measurable terms.

   • RQ1: "Does the proposed multilingual translation model outperform existing models in terms of translation quality by leveraging contextual similarity?"
   • RQ2: "Does the proposed model exhibit consistent performance across diverse datasets?"

0-2. Academic Contributions

The academic contributions of a paper clarify its originality and value, showcasing how the study advances the field.

1. Defining Contributions
   Structure your contributions by addressing the following questions:

   • What problem does the study solve?
   • What new knowledge or insights does it provide?
   • How does it improve upon or extend prior work?

   Examples:

   • "This study proposes, designs, and implements a novel deep learning-based model capable of learning multilingual context."
   • "A large-scale dataset for evaluating multilingual translation performance is constructed and made publicly available."
   • "The effectiveness of the proposed model is experimentally validated across various datasets."

2. Categorizing Contributions
   Break contributions into distinct types to enhance clarity:

   • New Algorithms/Models: Technical innovations that overcome limitations in prior studies.
     Example: "A transformer-based translation model designed to learn contextual similarity in multilingual environments."
   • Dataset Development: Datasets that enhance reproducibility and facilitate future research.
     Example: "A multilingual translation dataset covering 30 languages made publicly available."
   • Theoretical Analysis: Logical evaluation of limitations in prior work and the strengths of the new approach.
     Example: "A theoretical review of how the proposed model learns contextual similarity during training."

3. Organizing Contributions in a List
   Summarize contributions in a structured format for clarity:

   • Propose a new model for learning multilingual context.
   • Validate the model's performance using diverse datasets.
   • Demonstrate consistent performance regardless of dataset size.

4. Highlighting Academic Significance
   Explain the importance of these contributions in the broader academic context.

   • Example:
     • "This study significantly improves translation quality and introduces a new direction for global language technology research."
     • "The proposed dataset serves as a standard benchmark for future multilingual studies."

5. Connecting Contributions to Research Questions
   Explicitly link contributions to your research questions for coherence.
   Examples:

   • "In response to RQ1, the proposed model achieved 20% higher BLEU scores than existing models."
   • "For RQ2, the model demonstrated consistent performance across small-scale datasets, addressing concerns about dataset dependency."

Integrated Example: Comprehensive Explanation

Research Topic Example:
"Design and Performance Evaluation of a Context-Aware Multilingual Translation System"

Contribution Examples:

• Developed a transformer-based model for learning multilingual context.
• Provided a 30-language multilingual translation dataset to enhance reproducibility.
• Achieved a 20% improvement in BLEU scores compared to existing models.
• Designed a model structure that maintains consistent performance on small-scale datasets.