14 citations · 25 across the 9 of their papers we have counts for
8 papers · 1 filter
An Empirical Evaluation of Encoder Architectures for Fast Real-Time Long Conversational Understanding
Annamalai Senthilnathan, Kristjan Arumae, Mohammed Khalilia +2
Analyzing long text data such as customer call transcripts is a cost-intensive and tedious task. Machine learning methods, namely Transformers, are leveraged to model agent-custome…
Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking
Denis Jered McInerney, Luyang Kong, Kristjan Arumae +2
Multi-task learning is useful in NLP because it is often practically desirable to have a single model that works across a range of tasks. In the medical domain, sequential training…
An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training
Kristjan Arumae, Qing Sun, Parminder Bhatia
Pre-training large language models has become a standard in the natural language processing community. Such models are pre-trained on generic data (e.g. BookCorpus and English Wiki…
CALM: Continuous Adaptive Learning for Language Modeling
Kristjan Arumae, Parminder Bhatia
Training large language representation models has become a standard in the natural language processing community. This allows for fine tuning on any number of specific tasks, howev…
Towards Annotating and Creating Sub-Sentence Summary Highlights
Kristjan Arumae, Parminder Bhatia, Fei Liu
Highlighting is a powerful tool to pick out important content and emphasize. Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences…
Guiding Extractive Summarization with Question-Answering Rewards
Kristjan Arumae, Fei Liu
Highlighting while reading is a natural behavior for people to track salient content of a document. It would be desirable to teach an extractive summarizer to do the same. However,…
Reinforced Extractive Summarization with Question-Focused Rewards
Kristjan Arumae, Fei Liu
We investigate a new training paradigm for extractive summarization. Traditionally, human abstracts are used to derive goldstandard labels for extraction units. However, the labels…
A Study of Question Effectiveness Using Reddit "Ask Me Anything" Threads
Kristjan Arumae, Guo-Jun Qi, Fei Liu
Asking effective questions is a powerful social skill. In this paper we seek to build computational models that learn to discriminate effective questions from ineffective ones. Arm…