How do you balance between exploring new ideas and building on existing ones in NLP research?

?? From my perspective, clear goals are the foundation of impactful NLP research. Without a defined objective, efforts can quickly become unfocused. ?? Define Objective Identify what you want to achieve and why. This clarity guides the research direction and ensures relevance. ?? Identify Gaps Understand the current literature to spot gaps. This ensures your work adds value and addresses real needs. ?? Focus Efforts With clear goals, avoid distractions and redundant ideas. Focused research leads to meaningful contributions and impactful results. ?? Clear goals not only direct research but also maximize its impact, ensuring the work contributes effectively to the field.

While considering any goals, we should always analyze its business value by answering following questions: - What is the business challenge that we are facing? - How do we think, our solution can solve the business challenge? - How much cost we can save or how much revenue we can generate? For example, Let's consider NLP project like Expert Diagnosis System which helps diagnosis of any diseases, will help pre-checks to be automated, saves doctors time and cost, patients get their answers quickly, helps patients save time In this way, it helps both the parties involved in an transaction as well as it brings reduction in cost and time involved earlier and hence it makes this a better business challenge to solve.

Think of NLP research as traveling. Sometimes, you build upon known routes (existing ideas) to reach a destination faster. Other times, you explore new trails (new ideas) for unique experiences. For instance, while BERT was groundbreaking, many subsequent models built upon its foundation. Yet, occasionally, fresh explorations lead to revolutionary discoveries. By understanding your goals, you can decide whether to pave familiar roads or blaze new trails, ensuring a meaningful journey in the vast NLP landscape.

When starting a new NLP project, it is important to have a clear idea of what you want to achieve and why. Define the research question you aim to answer and identify the gap in existing literature or the practical problem you seek to solve. Clearly articulate the expected contribution and impact of your work. This well-defined goal will help you stay focused and avoid distractions from irrelevant or redundant ideas. By maintaining this clarity, you can effectively direct your efforts towards addressing the research question or problem at hand.

Ideally, new ideas come when you read past work and get some hands-on experience for building the applications that are out there, tweaking the initial settings (parameters, fine-tuning, data preprocessing, etc.) to understand how the current system works. From there, you can try other approaches to solve the same problem and then compare which method works, why, and how. Reading research papers, whitepapers, and case studies could help understand the gaps in the research, along with the potential future work that you can build upon. Examine the worth of building something from scratch so you're not reinventing the wheel. The more you read and build, the better balance you will find.

In the dynamic world of NLP, understanding current trends is crucial. For example, when Transformer architectures gained traction, they overshadowed RNNs. Being in sync with these shifts helps in decision-making. Start by building upon established methods, but always keep an eye out for novel, uncharted territories. For instance, while many expanded upon BERT's success, innovations like GPT-3 charted new courses. Balancing the old and new ensures you're both grounded and innovative in your NLP journey.

There is one more website (https://paperswithcode.com) which helps in searching best models along with their repository links where we can actually see the model accuracy and its sample test methods.

Don't forget to network and meet other NLP researchers and engineers. Some of the best ideas I've had came over a cup of coffee with a peer ?

Balancing between exploring new ideas and building on existing ones in NLP research involves reviewing current trends. Stay updated on the latest developments, techniques, and breakthroughs in the field. Identify gaps or emerging challenges where new ideas could be explored. Simultaneously, assess the effectiveness of existing methods and consider opportunities for improvement or adaptation to new domains. Regularly reviewing the landscape helps prioritize between innovation and consolidation, ensuring a balanced approach that fosters both creativity and progress in NLP research.

This involves staying up-to-date with the latest advancements, methodologies, and breakthroughs in the field of natural language processing. By regularly reading research papers, attending conferences, and following reputable NLP communities and experts, you can gain insights into emerging topics, popular techniques, and areas of active research. Understanding the current landscape of NLP research allows you to identify gaps, opportunities, and areas where your expertise can make a meaningful impact. It also helps you to avoid reinventing the wheel by leveraging existing knowledge and methodologies while still leaving room for innovation and exploration.

In one of my NLP projects, I explored game theory and behavioral science. They seemed unrelated, but they acted like catalyst. I used their ideas to make a better way to model customers' behavior When I read about brainstorming and getting ideas from fields like psychology, it reminded me how great it is to mix different things together. The best NLP solutions often come from trying new things and exploring new places.

Balancing between exploring new ideas and building on existing ones in NLP research involves experimenting with new concepts while leveraging existing knowledge. Allocate dedicated time and resources to explore innovative approaches or technologies that could address emerging challenges or improve existing methods. At the same time, build upon established techniques by refining them, adapting them to new contexts, or combining them with novel ideas to enhance their effectiveness. Regular experimentation allows for the discovery of new insights and the evolution of NLP research while maintaining a solid foundation in established practices.

Always be creative with existing datasets that are not labelled as "datasets". The greatest success story I've heard from a senior leadership about an NLP project, speech recognition to be more specific, is that a video hosting platform to train on the subtitles of the shows. So when you get hold of such a "dataset", don't overlook it.

NLP is a tool. Users with experience and expertise (in other domains) often leads to new, creative uses of NLP tools. Currently there is a real lack of research and application in the socio economic impact and biases of language models, as well as lack of safety and philosophical perspective

While the focus has mostly been on the researcher's individual journey in experimenting, another dimension worth considering is the role of collaborative networks. In today's interconnected world, research is increasingly becoming a collaborative endeavor. Utilizing platforms for pre-publishing papers, like arXiv, or open-source coding repositories, like GitHub, can offer real-time feedback and potential partnerships that can help validate or elevate your experimental ideas.

Using well-known publicly available datasets for testing our model accuracy, makes it more relevant for the academic as well as commercial usage. Its better if our codebase is shared across some technical architects and they add reviews or stars to your repository.

Balancing between exploring new ideas and building on existing ones in NLP research involves evaluating results rigorously. Assess the outcomes of experiments, projects, or studies to determine their impact and effectiveness. For new ideas, evaluate their feasibility, novelty, and potential contributions to the field. For existing ones, assess their performance, scalability, and applicability to different contexts. By systematically evaluating results, researchers can make informed decisions about which ideas to further explore, refine, or discard, ensuring a balanced approach that fosters both innovation and consolidation in NLP research.

This step is the crucial step for any NLP solution, where success or failure hangs in the balance. While there's no universal yardstick to measure NLP solutions, some commonly used metrics like BLEU and ROUGE come into play. Yet, they should be applied with a bit of caution because language can't be neatly quantified. It's the art of blending science with a personal touch.

One underexplored aspect is the potential for real-time, collaborative evaluation. In the era of social coding and preprint archives, researchers have the option to share their methodologies and preliminary results for public scrutiny before formal peer review. This 'crowdsourced' approach could offer additional layers of evaluation, highlight unseen biases, and even facilitate multi-disciplinary input, significantly enriching the evaluation process.

Evaluating your NLP experiments is crucial for understanding their impact and how they measure up against current benchmarks. Empirical and theoretical evaluations help validate your ideas. When designing experiments, it’s important to choose data sets and metrics that align with your hypotheses and to apply statistical tests to assess the significance of your findings. This rigorous approach ensures that your results are both reliable and valid. Interpreting your findings within the context of existing NLP research allows you to articulate how your work pushes the field forward, highlighting its contribution and potential applications.

Balancing between exploring new ideas and building on existing ones in NLP research involves effective communication of your work. Share your findings, methodologies, and insights through publications, presentations, and collaborations with the research community. When exploring new ideas, communicate your hypotheses, experimental designs, and preliminary results to gather feedback and refine your approach. When building on existing ideas, highlight how your work extends or improves upon prior research and its implications for the field. By fostering open dialogue and collaboration, researchers can leverage both new and existing ideas to advance NLP research effectively.

As an NLP Researcher, it is indeed a challenging task to maintain the trade off between exploring new ideas and leveraging and building on existing tools. What has worked well for me is getting validation from the community by sharing work on open source platforms such as github/arxiv. This way it helps you build quick prototypes on ideas and get feedback from researchers on whether the idea is worth exploring further or not.