We evaluate two dialogue state tracking models on SGD-X and observe that neither generalizes well across schema variations, measured by joint goal accuracy and a novel metric for measuring schema sensitivity.
We use a continuous depth version of the Residual Network (ResNet) model known as Neural ordinary differential equations (NODE) for the purpose of galaxy morphology classification.
Instrumentation and Methods for Astrophysics Astrophysics of Galaxies
We also benchmark a few state of the art dialogue state tracking models on the corrected dataset to facilitate comparison for future work.
no code implementations • 21 May 2020 • Manoj Kumar Kanakasabapathy, Prudhvi Thirumalaraju, Charles L Bormann, Raghav Gupta, Rohan Pooniwala, Hemanth Kandula, Irene Souter, Irene Dimitriadis, Hadi Shafiee
In conventional clinical in-vitro fertilization practices embryos are transferred either at the cleavage or blastocyst stages of development.
no code implementations • 21 May 2020 • Prudhvi Thirumalaraju, Manoj Kumar Kanakasabapathy, Charles L Bormann, Raghav Gupta, Rohan Pooniwala, Hemanth Kandula, Irene Souter, Irene Dimitriadis, Hadi Shafiee
A critical factor that influences the success of an in-vitro fertilization (IVF) procedure is the quality of the transferred embryo.
The goal of this task is to develop dialogue state tracking models suitable for large-scale virtual assistants, with a focus on data-efficient joint modeling across domains and zero-shot generalization to new APIs.
no code implementations • 14 Nov 2019 • Seokhwan Kim, Michel Galley, Chulaka Gunasekara, Sungjin Lee, Adam Atkinson, Baolin Peng, Hannes Schulz, Jianfeng Gao, Jinchao Li, Mahmoud Adada, Minlie Huang, Luis Lastras, Jonathan K. Kummerfeld, Walter S. Lasecki, Chiori Hori, Anoop Cherian, Tim K. Marks, Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta
This paper introduces the Eighth Dialog System Technology Challenge.
Pretrained language models like BERT have achieved good results on NLP tasks, but are impractical on resource-limited devices due to memory footprint.
In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains.
Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains.
This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems.
In task-oriented dialogue systems, spoken language understanding, or SLU, refers to the task of parsing natural language user utterances into semantic frames.
Tree-structured neural networks exploit valuable syntactic parse information as they interpret the meanings of sentences.
Ranked #83 on Natural Language Inference on SNLI
We consider partially observable Markov decision processes (POMDPs) with a set of target states and every transition is associated with an integer cost.