Search Results for author:
Found 7 papers, 3 papers with code
Topic Segmentation of Semi-Structured and Unstructured Conversational Datasets using Language Models
However, current works on topic segmentation often focus on segmentation of structured texts.
On Surgical Fine-tuning for Language Encoders
We show evidence that for different downstream language tasks, fine-tuning only a subset of layers is sufficient to obtain performance that is close to and often better than fine-tuning all the layers in the language encoder.
Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured Chats
Breaking down a document or a conversation into multiple contiguous segments based on its semantic structure is an important and challenging problem in NLP, which can assist many downstream tasks.
SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content
We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or "inked") notes on a virtual whiteboard.
On Optimizing Interventions in Shared Autonomy
Shared autonomy refers to approaches for enabling an autonomous agent to collaborate with a human with the aim of improving human performance.
Optimal Resource Allocation for Serverless Queries
We introduce a system for optimal resource allocation that can predict performance with aggressive trade-offs, for both new and past observed queries.
Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation
Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost.
