@inproceedings{shiv2019microsoft,
author = {Shiv, Vighnesh Leonardo and Quirk, Chris and Suri, Anshuman and Gao, Xiang and Shahid, Khuram and Govindarajan, Nithya and Zhang, Yizhe and Gao, Jianfeng and Galley, Michel and Brockett, Chris and Menon, Tulasi and Dolan, Bill},
title = {Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling},
organization = {Microsoft},
booktitle = {Association for Computational Linguistics 2019},
year = {2019},
month = {July},
abstract = {The Intelligent Conversation Engine: Code and Pre-trained Systems (Microsoft Icecaps) is an upcoming open-source natural language processing repository. Icecaps wraps TensorFlow functionality in a modular component-based architecture, presenting an intuitive and flexible paradigm for constructing sophisticated learning setups. Capabilities include multi-task learning between models with shared parameters, upgraded language model decoding features, a range of built-in architectures, and a user-friendly data processing pipeline. The system is targeted toward conversational tasks, exploring diverse response generation, coherence, and knowledge grounding. Icecaps also provides pre-trained conversational models that can be either used directly or loaded for fine-tuning or bootstrapping other models; these models power an online demo of our framework. (These pre-trained models and demo will be included in a future update of Icecaps.)},
url = {http://approjects.co.za/?big=en-us/research/publication/microsoft-icecaps-an-open-source-toolkit-for-conversation-modeling-2/},
}