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The Alexa Prize Proceedings publishes the research in Conversational AI resulting from the pursuit of the Alexa Prize competition goals. Amazon works closely with university teams to provide a testbed for research to address the challenges with Dialog Management, Natural Language Understanding (NLU), Contextual Modeling, Commonsense Reasoning and Response Generation, and these proceedings seek to capture the advances in those areas that result from these efforts. Authors are free to make additional hardcopy publishing arrangements, but Amazon will not produce hardcopies of these volumes.

Learn more about how the 2017 Alexa Prize Finals was executed.

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Abstract
Conversational agents are exploding in popularity. However, much work remains in the area of social conversation as well as free - form conversation over a broad range of domains and topics. To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5-million-dollar university competition where sixteen selected university teams were challenged to build conversational agents, known as “socialbots”, to converse coherently and engagingly with humans on popular topics such as Sports, Politics, Entertainment, Fashion and Technology for 20 minutes. The Alexa Prize offers the academic community a unique opportunity to perform research with a live system used by millions of users. The competition provided university teams with real user conversational data at scale, along with the user-provided ratings and feedback augmented with annotations by the Alexa team. This enabled teams to effectively iterate and make improvements throughout the competition while being evaluated in real-time through live user interactions. To build their socialbots, university teams combined state-of-the-art techniques with novel strategies in the areas of Natural Language Understanding, Context Modeling, Dialog Management, Response Generation, and Knowledge Acquisition. To support the teams’ efforts, the Alexa Prize team made significant scientific and engineering investments to build and improve Conversational Speech Recognition, Topic Tracking, Dialog Evaluation, Voice User Experience, and tools for traffic management and scalability. This paper outlines the advances created by the university teams as well as the Alexa Prize team to achieve the common goal of solving the problem of Conversational AI.

Authors
Handbag TOSCA Blue TOSCA BLU BLU Handbag qPa4wIUBrick Handbag EBARRITO Handbag EBARRITO red xqwt4CXfAshwin Ram, Rohit Prasad, Chandra Khatri, Anu Venkatesh, Raefer Gabriel, Qing Liu, Jeff Nunn, Behnam Hedayatnia, Ming Cheng, Ashish Nagar, Eric King, Kate Bland, Amanda Wartick, Yi Pan, Han Song, Sk Jayadevan, Gene Hwang, Art Pettigrue

Tags: TBD

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Abstract
Recent years have seen a surge in consumer usage of spoken dialog systems, due to the popularity of voice assistants. While these systems are capable of answering factual questions or executing basic tasks, they do not yet have the capability to hold multi-turn conversations. The Alexa Prize challenge provides us a great opportunity to explore various approaches and dialog strategies for building a multi-turn conversational agent. In this report we identify key challenges to build a social conversational dialog system, and present CMU Magnus, an intelligent interactive spoken dialog system that can hold conversations over a range of topics. The system learns and updates itself over time, and can handle argumentative or subjective conversations.

Authors
Shrimai Prabhumoye*, Fadi Botros*, Khyathi Chandu*, Samridhi Choudhary*, Esha Keni* Chaitanya Malaviya*, bag body Dark brown Across GATTINONI qZCEcZgU*, Rama Pasumarthi*, Shivani Poddar*, Abhilasha Ravichander*, Zhou Yu, Alan Black
*All authors contributed equally to this article.

Tags

  1. Ranker/Selector: Supervised
  2. Hybrid Dialogue Manager: FST

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Abstract
RubyStar is a dialog system designed to create “human-like” conversation by combining different response generation strategies. RubyStar conducts a non- task-oriented conversation on general topics by using an ensemble of rule-based, retrieval-based and generative methods. Topic detection, engagement monitoring, and context tracking are used for managing interaction. Predictable elements of conversation, such as the bot’s backstory and simple question answering are handled by separate modules. We describe a rating scheme we developed for evaluating response generation. We find that character-level RNN is an effective generation model for general responses, with proper parameter settings; however other kinds of conversation topics might benefit from using other models.

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Huiting Liu, Hanfei Sun, Weijian Lin, Chih-Wei Chang, Teng Zhong, Klein Seasonnal Calvin Black Crossbody Edge Small 4Tdqd

Tags

  1. Hybrid Dialogue Manager: Multi-Bot
  2. Ranker/Selector: Engagement

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Abstract
This paper describes a new open domain dialogue system Alquist developed as part of the Alexa Prize competition for the Amazon Echo line of products. The Alquist dialogue system is designed to conduct a coherent and engaging conversation on popular topics. We are presenting a hybrid system combining several machine learning and rule based approaches. We discuss and describe the Alquist pipeline, data acquisition, and processing, dialogue manager, NLG, knowledge aggregation and hierarchy of sub-dialogs. We present some of the experimental results.

Authors
Jan Pichl, Martin Matulík , Petr Marek , Hoang Long Nguyen, Jakub Konrád, Jan Šedivý

Tags

  1. Hybrid Dialogue Manager: Multi-Bot, Topic Manager
  2. Customer Experience: Context Tracker and Engagement Module

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Abstract
We describe an information-oriented conversational AI system, EmersonBot, developed for the Alexa Prize 2017 competition. The main goal of the system was informing users about current events, and answering their questions, while maintaining a fluent conversation. The main innovations of Emersonbot include the development of a federated multi-source information retrieval system that is aware of the conversation context; a general dialogue management system which uses machine learning to analyze the user responses, complemented with rule-based conversational logic component, specifically tailored for the Alexa Prize competition rules. This report provides a detailed description of the system, and includes extensive analysis of the contributions of the various components to user satisfaction and engagement with the system. Our results show a steady and substantial improvement in user ratings and user engagement over the period of the semi-finals as the system was refined, especially in the last weeks as entity-oriented search and recommendation features were introduced. We conclude the report by outlining promising research directions, which could improve the satisfaction of the users, while also advancing the state of the art in conversation-oriented search.

Authors
Zihao Wang, Ali Ahmadvand, Jason Ingyu Choi, Payam Karisani, Eugene Agichtein

Tags

  1. NLU: Intent classification
  2. Customer Experience: Bot Proactivness

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Abstract
We describe our Alexa prize system (called ‘Alana’) which consists of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose system responses. This paper reports on the version of the system developed and evaluated in the semi-finals of the competition (i.e. up to 15 August 2017), but not on subsequent enhancements. The ranker for this system was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition. In order to avoid initial problems of inappropriate and boring utterances coming from big datasets such as Reddit and Twitter, we later focussed on ‘clean’ data sources such as news and facts. We report on experiments with different ranking functions and versions of our NewsBot. We find that a multi- turn news strategy is beneficial, and that a ranker trained on the ratings feedback from users is also effective. Our system continuously improved using the data gathered over the course over the competition (1 July – 15 August) . Our final user score (averaged user rating over the whole semi-finals period) was 3.12, and we achieved 3.3 for the averaged user rating over the last week of the semi-finals (8-15 August 2017). We were also able to achieve long dialogues (average 10.7 turns) during the competition period. In subsequent weeks, after the end of the semi-final competition, we have achieved our highest scores of 3.52 (daily average, 18th October), 3.45 (weekly average on 23 and 24 October), and average dialogue lengths of 14.6 turns (1 October), and median dialogue length of 2.25 minutes (average for 7 days on 10th October).

Authors
Ioannis Papaioannou, Amanda Cercas Curry, Jose L. Part Igor Shalyminov, Leather Bark Satchel Slouchy Brown Tory Whipstitch Burch qARAO, Yanchao Yu, Ondrˇej Dušek, Verena Rieser, Oliver Lemon

Tags

  1. Hybrid Dialog Manager: Multi-Bot
  2. Ranker/Selector: Supervised
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Abstract
We present Pixie, a socialbot submitted to the 2017 Alexa Prize, developed with the objective of holding engaging casual conversations on open-ended topics. We discuss the modular, heterogeneous architecture of the Pixie system, and highlight the successes and shortcomings of our primarily template-based approach. We reflect on our findings about alternative paradigms used in the prototyping phase; in particular, we discuss the challenges of bridging recent breakthroughs in deep learning with the frontier of machine social dialogue.

Authors
Oluwatosin Adewale, Alex Beatson, Davit Buniatyan, Jason Ge, CALVIN Handbag CALVIN KLEIN KLEIN Yellow PqxwcSRzHg, Holden Lee, Niranjani Prasad, Nikunj Saunshi, Ari Seff, Karan Singh, Daniel Suo, Cyril Zhang, Sanjeev Arora

Tags

  1. Hybrid Dialog Manager: Multi-Bot
  2. Ranker/Selector: Supervised
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Abstract
In recent years, chat-bots that assist people’s daily life gained tremendous popular- ity, such as Siri, Cortana, Alexia and Google assistant. Typically, conversations with chat-bots are task orientated and brief. In this work, we explore creating social bot that can complete task and entertain user.

We proposed a two-layered dialogue framework which allows for flexible reuse and reorganization of individual task based dialogue modules. Utilizing this framework, we experimented with multi-module dialogue frameworks to analyze how dialogue functions and user characteristics affect user engagement.

Authors
Jieming Ji, Qingyun Wang, Zev Battad, Jiashun Gou, Jingfei Zhou, Rahul Divekar, Craig Carlson, Red party Parfois Parfois savon Red clutch E0xq84qYw

Tags

  1. Customer Experience: Narrative Game
  2. Hybrid Dialogue Manager: Multi-Bot

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Abstract
In this work, we designed a conversational system by combining a finite state method, a retrieval model and a machine-initiative dialogue strategy. By using the machine-initiative strategy, most of the user queries are handled by the finite state machine which models predefined dialogues. If the user’s utterance is out of the range of modeled dialogues, the retrieval model processes the input. On comparative experiments, our retrieval model showed a better user satisfaction than the neural-network-based model. The experiments also showed that our context handling method and keyword emphasizing are key components of a retrieval model for the superior performance. In addition, dialogue strategies to retain initiative to machine was better to make a sustaining conversational system than to be strictly constrained to input sentences.

Authors
Sanghyun Yi, Kyomin Jung

Tags

  1. Hybrid Dialogue Manager: FSM, Retrieval, Intent

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Abstract
In this document we outline the data collection methods, system, and experiments we ran during the course of this project. We aim to shed light on what drove the development of our bot and the techniques we used to power our bot in the course of the conversation.

Authors
William H. Guss, James Bartlett, Phillip Kuznetsov, Piyush Patil

Tags

  1. Ranker/Selector: Supervised
  2. Parfois Pink wallet benur benur wallet wallet Pink Pink Parfois benur Parfois Parfois Data Collection: Crowdsourcing data
  3. Hybrid Dialogue Manager: Generative Models

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Abstract
In this paper we introduce a novel, open domain socialbot for the Amazon Alexa Prize competition, aimed at carrying on friendly conversations with users on a variety of topics. We present our modular system, highlighting our different data sources and how we use the human mind as a model for data management. Additionally we build and employ natural language understanding and information retrieval tools and APIs to expand our knowledge bases. We describe our semi- structured, scalable framework for crafting topic-specific dialogue flows, and give details on our dialogue management schemes and scoring mechanisms. Finally we briefly evaluate the performance of our system and observe the challenges that an open domain socialbot faces.

Authors
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Tags

  1. Sophisticated Data Processing/Storage
  2. Hybrid Dialogue Manager: Multi-Bot
  3. Ranker/Selector: Context

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Abstract
Edina is a conversational agent whose responses utilize data harvested from Amazon Mechanical Turk (AMT) through an innovative new technique we call self-dialogues. These are conversations in which a single AMT Worker plays both participants in a dialogue. Such dialogues are surprisingly natural, efficient to collect and reflective of relevant and/or trending topics. These self-dialogues provide training data for a generative neural network as well as a basis for soft rules used by a matching score component. We present methodology for combining rule-based, retrieval, and generative methods to effectively leverage our data. Our hybrid data-driven methodology thus addresses both coverage limitations of a strictly rule-based approach and the lack of guarantees of a strictly machine-learning approach.

Authors
Ben Krause, Marco Damonte*, Mihai Dobre*, GATTINONI GATTINONI Handbag Handbag GATTINONI Silver Silver Handbag qtwTgg*, Federico Fancellu*†, Emmanuel Kahembwe*, Jianpeng Cheng, Joachim Fainberg*, Bonnie Webber
* equal contribution; † team leader; ‡ faculty advisor

Tags

  1. Hybrid Dialogue Manager: Multi-Bot
  2. Ranker/Selector: Matching
  3. Data Collection: Crowdsourcing data

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Abstract
We present a novel, large-scale ensemble-based system for the Amazon Alexa Prize competition. Our system leverages state-of-the-art methods from deep learning and reinforcement learning. We carry out A/B testing experiments with real-world users and demonstrate that our approach yields substantial improvements over several baseline systems. During the competition semi-finals, our best performing system obtained a substantially higher average Alexa user score and number of back-and-forth turns compared to the average of all teams. Due to its machine learning architecture, our system is likely to improve with additional data.

Authors
Iulian V. Serban, Chinnadhurai Sankar, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Across SALAR KIO bag Garnet body v5xq8Ppxw, Nan Rosemary Ke, Sai Rajeswar, Alexandre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Yoshua Bengio1
1CIFAR Fellow

Tags

  1. Hybrid Dialogue Manager: Multi-Bot
  2. Ranker/Selector: Reinforcement Learning

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Abstract
Open–domain dialogue systems should be able to cover a very large set of domains and at the same time keep the user engaged in the interaction. Current approaches to dialogue modeling are divided between domain–independent, non–modular approaches using sequence–to–sequence models and the domain–specific modular systems developed for task–based dialogue. Furthermore, user engagement in dialogue, addressed in this bot challenge, is a rather new research area. In this work we describe Roving Mind, a dialogue system which combines domain– independence with a modular architecture for open–domain spoken conversation, with a specific module for user engagement. Our architecture takes a balanced approach between human expert design and data–driven approaches, adapting the traditional task–based architecture relying on slots and intents to open–domain conversation using entities and domain-independent dialogue acts. The system was tested extensively during the Alexa Prize semifinals and received ratings from a large number of users, which allowed us to draw statistically relevant facts on how users interact with an open domain, non task-based social bot. Our experiments show that on average users give higher ratings to task-driven conversations (using user–entertaining strategies) compared to completely open–topic conversations. We also find a correlation between cumulative sentiment (using sentiment and dialogue acts) and user ratings and argue that this could be investigated to estimate an error–signal for strategy computation.

Authors
A. Cervone, G. Tortoreto, S. Mezza, E. Gambi, G. Riccardi

Tags

  1. Hybrid Dialogue Manager: Ensemble of Retrieval Modules

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Abstract
This paper introduces the University of Washington’s Alexa Prize socialbot, Sound- ing Board, which is designed to engage users with a wide variety of content. The system models the user utterance using a multi-dimensional representation. A hierarchical dialogue manager is employed where a master manages the overall conversation and a collection of miniskills manage different conversation segments. The system constructs responses using speech acts selected by the dialogue man- ager, where each speech act is instantiated with randomness to introduce variation to the conversation. Further, we analyze the impact of miniskill variety, user personality, and speech recognition performance on user interaction ratings.

Authors
Hao Fang1, Hao Cheng1, Elizabeth Clark2, Ariel Holtzman2, Maarten Sap2, Mari Ostendorf1, LIPTSTICK Stachel Small Girlfriend Guess Felix nHBPAqwx7Z2, Noah A. Smith2

Tags

  1. Hybrid Dialogue Manager: Miniskills, Hierarchical
  2. Customer Experience: Intent, Personality

You can also download all of the papers in one .zip file.

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