Microsoft Icecaps: An Open-Source Toolkit for Conversation Modeling
By Vighnesh Leonardo Shiv, Chris Quirk, Anshuman Suri, Xiang Gao, Khuram Shahid, Nithya Govindarajan, Yizhe Zhang, Jianfeng Gao, Michel Galley, Chris Brockett and 2 more. 2019
7 citations recorded by the source corpus.
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 multitask 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.
Subfields: Artificial Intelligence, Natural Language Processing, Dialogue & Conversational AI
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jason Aaron Edward Weston
- Eric Michael Smith
- Y-Lan Boureau
- Jianfeng Gao
- Kurt Shuster
- Emily Dinan
- Mohit Bansal
- Arthur Szlam
In Natural Language Processing
- Jason Aaron Edward Weston
- Y-Lan Boureau
- Eric Michael Smith
- Emily Dinan
- Kurt Shuster
- Jianfeng Gao
- Arthur Szlam
- Stephen Roller
In Dialogue & Conversational AI
- Kurt Shuster
- Jason Aaron Edward Weston
- Jianfeng Gao
- Eric Michael Smith
- Stephen Roller
- Michel Galley
- Antoine Bordes
- Arthur Szlam
Citations in the corpus
- Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions (2020, methodology): “Other efforts in the software space include RASA (Bocklisch et al., 2017), PolyAI (Henderson et al., 2019), Uber’s Plato (Papangelis et al., 2019), Microsoft’s IceCaps (Shiv et al., 2019), and Huggingface Transformers library (Wolf et al., 2019a), not focused on dialogue per se, but used as a base i”
- DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation (2019, methodology): “We also attempted to optimize the reward R , P (Source|Hypothesis) using a policy gradient (Williams, 1992) with a sample-averaged baseline, following Zhang et al. (2018).”