Luke, I am Your Father: Dealing with Out-of-Domain Requests by Using Movies Subtitles

Ameixa, David; Coheur, Luísa; Fialho, Pedro; Quaresma, Paulo

doi:10.1007/978-3-319-09767-1_2

Cited by 65 publications

(49 citation statements)

References 7 publications

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“…One of the biggest challenges with building SB is the requirement for up-to-date topic-oriented content. Many open-domain systems try to cover a range of topics by retrieving system responses from large dialogic corpora, such as Open Subtitles, Twitter, and Reddit [1,3,13,17,26,32,53]. These corpora are often noisy, and lack detailed annotations that might constrain retrieval, such as dialogue acts, emotion, or humor.…”

Section: Related Workmentioning

confidence: 99%

Entertaining and opinionated but too controlling

Bowden

Cui

et al. 2019

Proceedings of the 1st International Conference on Conversational User Interfaces

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Conversational systems typically focus on functional tasks such as scheduling appointments or creating todo lists. Instead we design and evaluate SlugBot (SB), one of 8 semifinalists in the 2018 Alexa Prize, whose goal is to support casual open-domain social interaction. This novel application requires both broad topic coverage and engaging interactive skills. We developed a new technical approach to meet this demanding situation by crowd-sourcing novel content and introducing playful conversational strategies based on storytelling and games. We collected over 10,000 conversations during August 2018 as part of the Alexa Prize competition. We also conducted an in-lab follow-up qualitative evaluation. Overall users found SB moderately engaging; conversations averaged 3.6 minutes and involved 26 user turns. However, users reacted very differently to different conversation subtypes. Storytelling and games were evaluated positively; these were seen as entertaining with predictable interactive structure. They also led users to impute personality and intelligence to SB. In contrast, search and general Chit-Chat induced coverage problems; here users found it hard to infer what topics SB could understand, with these conversations seen as being too system-driven. Theoretical and design implications suggest a move away from conversational systems that simply provide factual information. Future systems should be designed to have their own opinions with personal stories to share, and SB provides an example of how we might achieve this.

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Section: Related Workmentioning

confidence: 99%

Entertaining and opinionated but too controlling

Bowden

Cui

et al. 2019

Proceedings of the 1st International Conference on Conversational User Interfaces

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“…Neural dialogue generation aims at generating natural-sounding replies automatically to exchange information, e.g., knowledge [7,42,58]. As a core component of both task-oriented and non-taskoriented dialogue systems, neural dialogue generation has received a lot attention in recent years [1,3,36,58]. Among all these approaches, sequence-to-sequence structure neural generation models [6,7,23,24,39,41,44,45] have been proved to be capable in multiple dialogue systems with promising performance.…”

Section: Neural Dialogue Generationmentioning

confidence: 99%

“…In recent years, dialogue systems have received increasing attention in numerous web applications [1,3,36,58]. Existing dialogue systems can fall into two categories: non-task-oriented dialogue systems and task-oriented dialogue systems.…”

Section: Introductionmentioning

confidence: 99%

Explicit State Tracking with Semi-Supervisionfor Neural Dialogue Generation

Jin

Lei

Zhang

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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The task of dialogue generation aims to automatically provide responses given previous utterances. Tracking dialogue states is an important ingredient in dialogue generation for estimating users' intention. However, the expensive nature of state labeling and the weak interpretability make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states.In this paper, we propose the semi-supervised explicit dialogue state tracker (SEDST) for neural dialogue generation. To this end, our approach has two core ingredients: CopyFlowNet and posterior regularization. Specifically, we propose an encoder-decoder architecture, named CopyFlowNet, to represent an explicit dialogue state with a probabilistic distribution over the vocabulary space. To optimize the training procedure, we apply a posterior regularization strategy to integrate indirect supervision. Extensive experiments conducted on both task-oriented and non-task-oriented dialogue corpora demonstrate the effectiveness of our proposed model. Moreover, we find that our proposed semi-supervised dialogue state tracker achieves a comparable performance as state-of-the-art supervised learning baselines in state tracking procedure.

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“…This paper focuses on our basic investigations on the initial prototype. While we are aware of other retrieval based chatbot systems [6,14,3,2], Debbie is novel in that it is the first to deal with argument retrieval.…”

Section: Introductionmentioning

confidence: 99%

Debbie, the Debate Bot of the Future

Rakshit

Bowden

Reed

et al. 2018

Lecture Notes in Electrical Engineering

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Chatbots are a rapidly expanding application of dialogue systems with companies switching to bot services for customer support, and new applications for users interested in casual conversation. One style of casual conversation is argument; many people love nothing more than a good argument. Moreover, there are a number of existing corpora of argumentative dialogues, annotated for agreement and disagreement, stance, sarcasm and argument quality. This paper introduces Debbie, a novel arguing bot, that selects arguments from conversational corpora, and aims to use them appropriately in context. We present an initial working prototype of Debbie, with some preliminary evaluation and describe future work.

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Luke, I am Your Father: Dealing with Out-of-Domain Requests by Using Movies Subtitles

Cited by 65 publications

References 7 publications

Entertaining and opinionated but too controlling

Entertaining and opinionated but too controlling

Explicit State Tracking with Semi-Supervisionfor Neural Dialogue Generation

Debbie, the Debate Bot of the Future

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