26 citations · 44 across the 6 of their papers we have counts for
7 papers
Learning to Route LLMs with Confidence Tokens
Yu-Neng Chuang, Prathusha Kameswara Sarma, Parikshit Gopalan +4
Large language models (LLMs) have demonstrated impressive performance on several tasks and are increasingly deployed in real-world applications. However, especially in high-stakes…
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions
Ali Mottaghi, Prathusha K Sarma, Xavier Amatriain +2
We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking). A typical pa…
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis
Zhongkai Sun, Prathusha Sarma, William Sethares +1
Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform no…
Shallow Domain Adaptive Embeddings for Sentiment Analysis
Prathusha K Sarma, Yingyu Liang, William A Sethares
This paper proposes a way to improve the performance of existing algorithms for text classification in domains with strong language semantics. We propose a domain adaptation layer…
Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis
Zhongkai Sun, Prathusha K Sarma, William Sethares +1
This paper learns multi-modal embeddings from text, audio, and video views/modes of data in order to improve upon down-stream sentiment classification. The experimental framework a…
Domain Adapted Word Embeddings for Improved Sentiment Classification
Prathusha K Sarma, YIngyu Liang, William A Sethares
Generic word embeddings are trained on large-scale generic corpora; Domain Specific (DS) word embeddings are trained only on data from a domain of interest. This paper proposes a m…
Sentiment Analysis by Joint Learning of Word Embeddings and Classifier
Prathusha Kameswara Sarma, Bill Sethares
Word embeddings are representations of individual words of a text document in a vector space and they are often use- ful for performing natural language pro- cessing tasks. Current…