Find out more about our Capstone projects
Natural Language Processing (NLP) is a rapidly growing field with applications in many of the technologies we are all accustomed to using every day, from virtual assistants and smart speakers to autocorrect functions.
Our Masters program at UCSC balances theory with practice including a 15 unit Capstone project to enable you to apply the skills you’ve acquired on the program to a real-world issue or challenge. You’ll gain valuable employability skills and practical experience both from working in a team with your peers and from the insights you’ll gain from your academic or organizational mentor(s). Recent student teams have collaborated with experts from industry giants like Bloomberg, IBM, Interactions, LinkedIn, Microsoft and others to address industry-relevant and research-focused topics in the NLP field.
The Capstone project is a great way for you to scope out the NLP field, looking at projects that interest you and identifying organizations that you may wish to work with in future. Working closely with your mentor during the project you’ll extend your networks and gain practical employability skills that will support your future job search. Many organizations value their involvement in the Capstone projects as this can be a useful way to informally evaluate students’ skills and find a good fit for workplace vacancies.
The NLP Capstone experience offers a great opportunity to extend your networks and put yourself in front of potential employers. Registration is open to the professional NLP community and invitations are sent to a range of interested parties who are able to attend either in person or remotely.
Since our program launched in 2020 our NLP students have collaborated with industry partners at Adobe, Amazon, Bloomberg, CDIAL, IBM, Intel, Interactions, Google, LinkedIn, Meta, Microsoft, ModelCode and UCSC. Scroll down to see the projects, and follow us on LinkedIn for updates from our Capstone teams.
2024 Capstone Projects
Summarization for Long Document Input using Multiple LLMs
Alex Liu, Ethan Liu, June Kim, Michael Fang, Nikhil Singh, Yash Bhedaru
Adobe (Hanieh Deilamsalehy)
GPS4LLM: Graph-based Planning System for Large Language Models
Anish Pahilajani, Devasha Trivedi, Jincen Shuai, Khin Yone, Neng Wan, Samyak Rajesh Jain
Meta (Namyoun Park)
Personalized Graph-based Retrieval for Large Language Models
Cameron Dimacali, Ojasmitha Pedirappagari, Steven Au
Intel (Nesreen Ahmed)
GUT-Bench: Maintainability and Performance
Decker Krogh, Ethan Sin, Esha Ubale, Sreekar Molakalapalli, Sujit Noronha
ModelCode (Antoine Raux)
2023 Capstone Projects
Model Interpretability For Hallucination Detection In Summaries
Dhananjay Sonavane, Bhrigu Garg and Devavrat Joshi, Dhananjay Sonavane, Meenal Chavan, Parsa Mazaheri and Priyesh Vakharia
UCSC (Leilani Gilpin & Ian Lane)
Dialogue Act Labelling for Open Domain Dialogue Systems
Haoran Zhang, Nan Qiang, Neha Pullabhotla,
Sidharth Babu and Xin Zhang
UCSC (Angela Ramirez & Marilyn Walker)
Adapters and Reinforcement Learning for Data-Efficient Machine Translation
Abigail Kufeldt, Malini Kar, Pranjali Basmatkar, Kushagra Seth
Shashwat Pandey, Vignesh S and Parikshith Honnegowda
CDIAL (Olayinka Iyinolakan)
Temperature Guided Text-to-Image Generation with Reinforcement Learning
Cookie Pan, Haolong Jia, Zoe Gupta, Mridul Pankaj Khanna,
Vijay Chilaka, Chengxuan Xia and Sree Latha
Adobe (Ryan Rossi)
2022 Capstone Projects
Exploring Code Style Transfer with Neural Networks
IBM Research
Neural Models of Supertagging for Semantic Role Labeling and Beyond
Interactions
Comparing Dictionaries and Word Embeddings
Multimodal Knowledge Extraction and Question Answering in Farming
UCSC and Google X
2021 Capstone Projects
Amazon Alexa Prize Discourse Model & Coreference Resolution
UCSC
Identifying Errors in SRL using Weak Supervision
IBM
Domain Adaptation for Question Answering on COVID-19
Amazon and IBM
MKD-Ultra: Compressing Causal Language Models in Multiple Steps
Microsoft
Informers: Evaluating Explanation Quality
IBM Research
Information Extraction of Corporate Events from the Web
Bloomberg