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Source: https://youtu.be/f01J0Dri-6k
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We introduce attention, focusing on self-attention and its hidden layer representations of the inputs. Then, we introduce the key-value store paradigm and discuss how to represent queries, keys, and values as rotations of an input. Finally, we use attention to interpret the transformer architecture, taking a forward pass through a basic transformer, and comparing the encoder-decoder paradigm to sequential architectures.
0:01:09 – Attention
0:17:36 – Key-value store
0:35:14 – Transformer and PyTorch implementation
0:54:00 – Q&A
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Source: https://vimeo.com/437692128
Authors: Tsung-Hsien Wen, Pei-Hao Su, Paweł Budzianowski, Iñigo Casanueva and Ivan Vulić
Date: 3-NOV-2019
Location: SkyCity, AsiaWorld-Expo, HONG KONG
A fundamental long-term goal of conversational AI is to merge two main dialogue system paradigms into a standalone multi-purpose system. Such a system should be capable of conversing about arbitrary topics (Paradigm 1: open-domain dialogue systems), and simultaneously assist humans with completing a wide range of tasks with well-defined semantics such as restaurant search and booking, customer service applications, or ticket bookings (Paradigm 2: task-based dialogue systems).
The recent developmental leaps in conversational AI technology are undoubtedly linked to more and more sophisticated deep learning algorithms that capture patterns in increasing amounts of data generated by various data collection mechanisms. The goal of this tutorial is therefore twofold. First, it aims at familiarising the research community with the recent advances in algorithmic design of statistical dialogue systems for both open-domain and task-based dialogue paradigms. The focus of the tutorial is on recently introduced end-to-end learning for dialogue systems and their relation to more common modular systems. In theory, learning end-to-end from data offers seamless and unprecedented portability of dialogue systems to a wide spectrum of tasks and languages. From a practical point of view, there are still plenty of research challenges and opportunities remaining: in this tutorial we analyse this gap between theory and practice, and introduce the research community with the main advantages as well as with key practical limitations of current end-to-end dialogue learning.
The critical requirement of each statistical dialogue system is the data at hand. The system cannot provide assistance for the task without having appropriate task-related data to learn from. Therefore, the second major goal of this tutorial is to provide a comprehensive overview of the current
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Video is reposted for educational purposes
Abstract:
Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself. These approaches have shown good performance gain, especially in complicated dialogue domains with dynamic slot values. However, they fall short in two aspects: (1) they do not allow models to explicitly learn signals across domains and slots to detect potential dependencies among \textit{(domain, slot)} pairs; and (2) existing models follow auto-regressive approaches which incur high time cost when the dialogue evolves over multiple domains and multiple turns. In this paper, we propose a novel framework of Non-Autoregressive Dialog State Tracking (NADST) which can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set rather than separate slots. In particular, the non-autoregressive nature of our method not only enables decoding in parallel to significantly reduce the latency of DST for real-time dialogue response generation, but also detect dependencies among slots at token level in addition to slot and domain level. Our empirical results show that our model achieves the state-of-the-art joint accuracy across all domains on the MultiWOZ 2.1 corpus, and the latency of our model is an order of magnitude lower than the previous state of the art as the dialogue history extends over time.
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Title: Contributed talk 7 – Selection Bias Invalidates Fairness
Author(s): Margarita Boyarskaya
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