no code implementations • NAACL (ACL) 2022 • Minhua Chen, Badrinath Jayakumar, Michael Johnston, S. Eman Mahmoodi, Daniel Pressel
A key challenge in the creation and refinement of virtual assistants is the ability to mine unlabeled utterance data to discover common intents.
no code implementations • NAACL (ACL) 2022 • Daniel Pressel, Wenshuo Liu, Michael Johnston, Minhua Chen
To understand how training on conversational language impacts performance of pre-trained models on downstream dialogue tasks, we build compact Transformer-based Language Models from scratch on several large corpora of conversational data.
no code implementations • 9 Aug 2023 • Hangjie Shi, Leslie Ball, Govind Thattai, Desheng Zhang, Lucy Hu, Qiaozi Gao, Suhaila Shakiah, Xiaofeng Gao, Aishwarya Padmakumar, Bofei Yang, Cadence Chung, Dinakar Guthy, Gaurav Sukhatme, Karthika Arumugam, Matthew Wen, Osman Ipek, Patrick Lange, Rohan Khanna, Shreyas Pansare, Vasu Sharma, Chao Zhang, Cris Flagg, Daniel Pressel, Lavina Vaz, Luke Dai, Prasoon Goyal, Sattvik Sahai, Shaohua Liu, Yao Lu, Anna Gottardi, Shui Hu, Yang Liu, Dilek Hakkani-Tur, Kate Bland, Heather Rocker, James Jeun, Yadunandana Rao, Michael Johnston, Akshaya Iyengar, Arindam Mandal, Prem Natarajan, Reza Ghanadan
The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge.
no code implementations • 20 Apr 2022 • Karan Singla, Daniel Pressel, Ryan Price, Bhargav Srinivas Chinnari, Yeon-Jun Kim, Srinivas Bangalore
In this paper, we propose a novel architecture for multi-modal speech and text input.
no code implementations • 29 Mar 2022 • Karan Singla, Shahab Jalalvand, Yeon-Jun Kim, Ryan Price, Daniel Pressel, Srinivas Bangalore
Person name capture from human speech is a difficult task in human-machine conversations.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Brian Lester, Daniel Pressel, Amy Hemmeter, Sagnik Ray Choudhury, Srinivas Bangalore
Current state-of-the-art models for named entity recognition (NER) are neural models with a conditional random field (CRF) as the final layer.
1 code implementation • 30 Sep 2020 • Brian Lester, Daniel Pressel, Amy Hemmeter, Sagnik Ray Choudhury, Srinivas Bangalore
Most state-of-the-art models in natural language processing (NLP) are neural models built on top of large, pre-trained, contextual language models that generate representations of words in context and are fine-tuned for the task at hand.
1 code implementation • 5 Jan 2020 • Brian Lester, Daniel Pressel, Amy Hemmeter, Sagnik Ray Choudhury
The CRF layer is used to facilitate global coherence between labels, and the contextual embeddings provide a better representation of words in context.
no code implementations • NAACL 2019 • Ishan Jindal, Daniel Pressel, Brian Lester, Matthew Nokleby
In this paper, we propose an approach to training deep networks that is robust to label noise.
1 code implementation • WS 2018 • Daniel Pressel, Sagnik Ray Choudhury, Brian Lester, Yanjie Zhao, Matt Barta
We introduce Baseline: a library for reproducible deep learning research and fast model development for NLP.