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Found 21 papers, 12 papers with code
Feature Sensitive Label Fusion with Random Walker for Atlas-based Image Segmentation
This label fusion method is formulated on a graph, which embraces both label priors from atlases and anatomical priors from target image.
Familia: An Open-Source Toolkit for Industrial Topic Modeling
Familia is an open-source toolkit for pragmatic topic modeling in industry.
3D Randomized Connection Network with Graph-based Label Inference
In this paper, a novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the network capacity.
Familia: A Configurable Topic Modeling Framework for Industrial Text Engineering
In order to relieve burdens of software engineers without knowledge of Bayesian networks, Familia is able to conduct automatic parameter inference for a variety of topic models.
Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment
In this paper, a novel Generation-Evaluation framework is developed for multi-turn conversations with the objective of letting both participants know more about each other.
DAL: Dual Adversarial Learning for Dialogue Generation
DAL is the first work to innovatively utilizes the duality between query generation and response generation to avoid safe responses and increase the diversity of the generated responses.
3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation
Recent advancements in medical image segmentation techniques have achieved compelling results.
PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable
Pre-training models have been proved effective for a wide range of natural language processing tasks.
PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning
To build a high-quality open-domain chatbot, we introduce the effective training process of PLATO-2 via curriculum learning.
Learning to Select External Knowledge with Multi-Scale Negative Sampling
The Track-1 of DSTC9 aims to effectively answer user requests or questions during task-oriented dialogues, which are out of the scope of APIs/DB.
A Unified Pre-training Framework for Conversational AI
In this work, we explore the application of PLATO-2 on various dialogue systems, including open-domain conversation, knowledge grounded dialogue, and task-oriented conversation.
PLATO-XL: Exploring the Large-scale Pre-training of Dialogue Generation
To explore the limit of dialogue generation pre-training, we present the models of PLATO-XL with up to 11 billion parameters, trained on both Chinese and English social media conversations.
Amendable Generation for Dialogue State Tracking
In this paper, we propose a novel Amendable Generation for Dialogue State Tracking (AG-DST), which contains a two-pass generation process: (1) generating a primitive dialogue state based on the dialogue of the current turn and the previous dialogue state, and (2) amending the primitive dialogue state from the first pass.
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TOD-DA: Towards Boosting the Robustness of Task-oriented Dialogue Modeling on Spoken Conversations
Task-oriented dialogue systems have been plagued by the difficulties of obtaining large-scale and high-quality annotated conversations.
Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes
In recent years, Internet memes have been widely used in online chatting.
Link the World: Improving Open-domain Conversation with Dynamic Spatiotemporal-aware Knowledge
Making chatbots world aware in a conversation like a human is a crucial challenge, where the world may contain dynamic knowledge and spatiotemporal state.
Towards Boosting the Open-Domain Chatbot with Human Feedback
Many open-domain dialogue models pre-trained with social media comments can generate coherent replies but have difficulties producing engaging responses when interacting with real users.
Q-TOD: A Query-driven Task-oriented Dialogue System
Firstly, as the query is in the form of natural language and not confined to the schema of the knowledge base, the issue of domain adaption is alleviated remarkably in Q-TOD.
PLATO-K: Internal and External Knowledge Enhanced Dialogue Generation
Recently, the practical deployment of open-domain dialogue systems has been plagued by the knowledge issue of information deficiency and factual inaccuracy.
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling
In this paper, we propose an unsupervised query enhanced approach for knowledge-intensive conversations, namely QKConv.
PLATO-KAG: Unsupervised Knowledge-Grounded Conversation via Joint Modeling
Large-scale conversation models are turning to leveraging external knowledge to improve the factual accuracy in response generation.
