Rising Stars: Meet Jennifer Hu!

Dear Readers,

For several years, we have featured linguists with established careers and interesting stories to tell. This year, we will also be highlighting “Rising Stars” throughout our Fund Drive, undergraduates who were nominated by their mentors for their exceptional interest in linguistics and eager participation in the global community of language researchers.

Selected nominees were asked to share their view of the field of linguistics: what topics they see emerging as important or especially interesting, what role they see the field filling in the coming decades, and how they plan to contribute. We hope you will enjoy the perspectives of these students, who represent the bright future of our field.

Today we are excited to share with you the perspective of Jennifer Hu, a senior at Harvard University. Jennifer studies Linguistics and Mathematics, and is highly involved in several research projects. Her own honors thesis focuses on cross-linguistic investigation of Bayesian models of pragmatics.


With the recent revolution in robotics and machine learning, linguistics is playing an increasingly important role as we develop and interact with systems of artificial intelligence. Just as we communicate with other humans through language, it is most natural for us to communicate with robots and other automated systems through speech, text, and sign. These new types of interactions will demand a robust understanding of linguistics, as language processing poses many unique challenges for machines.

We have already made significant progress in developing systems for speech recognition, question answering, and other language processing tasks. If one analyzes the errors produced by state-of-the-art systems, however, one finds that many of these models – while obtaining high performance on the tasks for which they are designed – are not fully capable of language understanding. For example, the Story Cloze Test requires a system to choose the correct ending to a simple four-sentence story as a way of approximating understanding of causal relationships between events. The best model achieves an impressive 75% accuracy on the Story Cloze Test, but is able to achieve 72% accuracy without even being exposed to the stories! These results suggest that the success of the model might not reflect genuine understanding of the events in the stories, but other confounds latent in the task. This should lead us to inquire whether other models have truly learned the linguistic abilities that their tasks were designed to measure. Similarly, the type of training data that these models require to achieve reasonable performance is cognitively implausible, given what we know about the input to which human learners are exposed. With very little exposure to negative data, children produce linguistic errors in a systematic, predictable way. These two issues in the design of current models suggest that knowledge of the theoretical underpinnings of language can help bring us closer to building systems that truly approximate human intelligence.

There is no better time for linguists to take advantage of and contribute to concurrent advances in the computer and cognitive sciences. With increasing large-scale datasets, computing power, and understanding of the human brain, linguists have more tools than ever to pursue the scientific study of language. In the coming years, I expect and hope to see growth in the subfields of computational linguistics and psycholinguistics. I am excited by the prospect of being able to reverse engineer our capacity for language, and through collaboration with computer science and cognitive science, I believe we can achieve this goal in the coming decades.

By studying linguistics, we can not only develop new insights into the structure of language, but also shape the way humans will interact with systems of artificial intelligence in the years to come. I plan to continue contributing to this exciting field by obtaining a PhD and ultimately pursuing a career focused on research, education, and outreach.


If you have a student who you believe is a “Rising Star” in linguistics, we would love to hear about them! We are still accepting nominations for exceptional young linguists. Please see the call for nominations for more information.

If you have not yet–please visit our Fund Drive page to learn more about us and why we need your help! The LINGUIST List relies on your generous donations to continue its support of linguists around the world.

The LINGUIST List Team

Leave a Reply