activity
20112022
most citedUncertainty-aware Self-training for Text Classification with Few Labels

41 citations · 103 across the 10 of their papers we have counts for

collaborators

21 papers

cs.CL2022

PREME: Preference-based Meeting Exploration through an Interactive Questionnaire

Negar Arabzadeh, Ali Ahmadvand, Julia Kiseleva +4

The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion an…

cs.CL2021

NL-EDIT: Correcting semantic parse errors through natural language interaction

Ahmed Elgohary, Christopher Meek, Matthew Richardson +3

We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feed…

cs.CL2020

Structure-Grounded Pretraining for Text-to-SQL

Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek +3

Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to grou…

cs.CL2020

Adaptive Self-training for Few-shot Neural Sequence Labeling

Yaqing Wang, Subhabrata Mukherjee, Haoda Chu +4

Sequence labeling is an important technique employed for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot tagging for dialog systems and s…

cs.CL2020★ 41 cited

Uncertainty-aware Self-training for Text Classification with Few Labels

Subhabrata Mukherjee, Ahmed Hassan Awadallah

Recent success of large-scale pre-trained language models crucially hinge on fine-tuning them on large amounts of labeled data for the downstream task, that are typically expensive…

cs.CL2020★ 1 cited

Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback

Ahmed Elgohary, Saghar Hosseini, Ahmed Hassan Awadallah

We study the task of semantic parse correction with natural language feedback. Given a natural language utterance, most semantic parsing systems pose the problem as one-shot transl…