The article discusses the limitations of current research in natural language processing (NLP) and the potential consequences of relying solely on industry-funded research. The authors argue that while NLP has made significant progress in recent years, much of this progress is due to advancements in techniques and algorithms, rather than any fundamental understanding of language itself. They contend that the current system of relying on industry funding for NLP research creates a bias towards practical applications over theoretical foundations, which can lead to a lack of transparency and reproducibility in research findings.
The authors highlight several limitations of the current research landscape, including the small sample size of papers analyzed, the lack of statistical significance in many findings, and the reliance on pre-existing labeled datasets for training NLP models. They also criticize the use of leaderboards to evaluate the performance of NLP models, as these can create a perception of objectivity when in reality, they are often biased towards models that are more practical rather than theoretically sound.
The article concludes by suggesting that in order to demystify language and improve our understanding of its fundamental workings, there is a need for a shift in the focus of NLP research away from practical applications and towards theoretical foundations. This may involve increasing funding for basic research, developing new methods for evaluating the quality of NLP models, and encouraging greater transparency and reproducibility in research findings. By taking these steps, the field of NLP can become more rigorous and robust, ultimately leading to more accurate and reliable language processing systems.
Key Points
- The current system of relying on industry-funded research for NLP creates a bias towards practical applications over theoretical foundations.
- Many of the recent advancements in NLP are due to improvements in techniques and algorithms, rather than any fundamental understanding of language itself.
- The use of leaderboards to evaluate the performance of NLP models can create a perception of objectivity when in reality, they are often biased towards practical models.
- There is a need for a shift in the focus of NLP research away from practical applications and towards theoretical foundations.
- This may involve increasing funding for basic research, developing new methods for evaluating the quality of NLP models, and encouraging greater transparency and reproducibility in research findings.
