no code implementations • COLING 2022 • Ernie Chang, Alex Marin, Vera Demberg
To this end, we propose a novel data programming framework that can jointly construct labeled data for language generation and understanding tasks – by allowing the annotators to modify an automatically-inferred alignment rule set between sequence labels and text, instead of writing rules from scratch.
no code implementations • COLING 2022 • Ernie Chang, Alex Marin, Vera Demberg
The demand for multilingual dialogue systems often requires a costly labeling process, where human translators derive utterances in low resource languages from resource rich language annotation.
no code implementations • 8 Jan 2024 • William R. Kearns, Jessica Bertram, Myra Divina, Lauren Kemp, Yinzhou Wang, Alex Marin, Trevor Cohen, Weichao Yuwen
We studied providers with and without expertise in mental health treatment delivering a therapy session using the platform with (intervention) and without (control) AI-assistance features.
1 code implementation • NLP4ConvAI (ACL) 2022 • Zhilin Wang, Xuhui Zhou, Rik Koncel-Kedziorski, Alex Marin, Fei Xia
Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes.
no code implementations • INLG (ACL) 2021 • Ernie Chang, Xiaoyu Shen, Alex Marin, Vera Demberg
We propose a shared task on training instance selection for few-shot neural text generation.
no code implementations • EACL 2021 • Ernie Chang, Vera Demberg, Alex Marin
Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive.
no code implementations • COLING 2020 • Ernie Chang, Jeriah Caplinger, Alex Marin, Xiaoyu Shen, Vera Demberg
We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions.
no code implementations • CONLL 2019 • Kunho Kim, Rahul Jha, Kyle Williams, Alex Marin, Imed Zitouni
In this work, we extend the task oriented LU problem to human-to-human (H2H) conversations, focusing on the slot tagging task.
no code implementations • NAACL 2018 • Rahul Jha, Alex Marin, Suvamsh Shivaprasad, Imed Zitouni
Slot tagging, the task of detecting entities in input user utterances, is a key component of natural language understanding systems for personal digital assistants.
no code implementations • NAACL 2016 • Paul Crook, Alex Marin, Vipul Agarwal, Khushboo Aggarwal, Tasos Anastasakos, Ravi Bikkula, Daniel Boies, Asli Celikyilmaz, Ch, Senthilkumar ramohan, Zhaleh Feizollahi, Roman Holenstein, Minwoo Jeong, Omar Khan, Young-Bum Kim, Elizabeth Krawczyk, Xiaohu Liu, Danko Panic, Vasiliy Radostev, Nikhil Ramesh, Jean-Phillipe Robichaud, Alex Rochette, re, Logan Stromberg, Ruhi Sarikaya