Generative Event Schema Induction with Entity Disambiguation

Nguyen, Kiem-Hieu; Tannier, Xavier; Ferret, Olivier; Besançon, Romaric

doi:10.3115/v1/p15-1019

Cited by 62 publications

(70 citation statements)

References 18 publications

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“…Such methods for script-learning also include Bayesian approaches (Bejan, 2008;Frermann et al, 2014), sequence alignment algorithms (Regneri et al, 2010) and neural networks (Modi and Titov, 2014;Granroth-Wilding and Clark, 2016;Pichotta and Mooney, 2016). There has also been work on representing events in a structured manner using schemas, which are learned probabilistically (Chambers, 2013;Cheung et al, 2013;Nguyen et al, 2015), using graphs (Balasubramanian et al, 2013) or neural approaches (Titov and Khoddam, 2015). Recently, Ferraro and Durme (2016) presented a unified Bayesian model for scripts and frames.…”

Section: Events-centered Learningmentioning

confidence: 99%

Story Comprehension for Predicting What Happens Next

Chaturvedi¹,

Peng²,

Roth³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Automatic story comprehension is a fundamental challenge in Natural Language Understanding, and can enable computers to learn about social norms, human behavior and commonsense. In this paper, we present a story comprehension model that explores three distinct semantic aspects: (i) the sequence of events described in the story, (ii) its emotional trajectory, and (iii) its plot consistency. We judge the model's understanding of real-world stories by inquiring if, like humans, it can develop an expectation of what will happen next in a given story. Specifically, we use it to predict the correct ending of a given short story from possible alternatives. The model uses a hidden variable to weigh the semantic aspects in the context of the story. Our experiments demonstrate the potential of our approach to characterize these semantic aspects, and the strength of the hidden variable based approach. The model outperforms the stateof-the-art approaches and achieves best results on a publicly available dataset.

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Section: Events-centered Learningmentioning

confidence: 99%

Story Comprehension for Predicting What Happens Next

Chaturvedi¹,

Peng²,

Roth³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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“…Thus what is learned is not evaluated for contingency (Chambers and Jurafsky, 2008;Chambers and Jurafsky, 2009;Manshadi et al, 2008;Nguyen et al, 2015;Balasubramanian et al, 2013;Pichotta and Mooney, 2014). Historically, work on scripts explicitly modeled causality (Lehnert, 1981;Mooney and DeJong, 1985) inter alia.…”

Section: Stormmentioning

confidence: 99%

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Katagiri

Nakano

Fernández

et al. 2016

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We extend special thanks to our Local co-Chairs, Ron Artstein and Alesia Gainer, and their team of student volunteers. We know SIGDIAL 2016 would not have been possible without Ron and Alesia, who invested so much effort in arranging the conference venue and accommodations, handling registration, making banquet arrangements, and handling numerous other preparations for the conference. The student volunteers for on-site assistance also deserve our appreciation.Ethan Selfridge, Sponsorships Chair, has earned our appreciation for recruiting and liaising with our conference sponsors, many of whom continue to contribute year after year. Sponsorships support valuable aspects of the program, such as the invited speakers and conference banquet. In recognition of this, we gratefully acknowledge the support of our sponsors: (Platinum level) Microsoft Research, Xerox and PARC, Intel, (Gold level) Facebook, (Silver level) Amazon Alexa, Interactions, Educational Testing Service, Honda Research Institute, and Yahoo!. At the same time, we thank Priscilla Rasmussen at the ACL for tirelessly handling the financial aspects of sponsorship for SIGDIAL 2016, and for securing our ISBN.iii We also thank the SIGdial board, especially officers Amanda Stent, Jason Williams and Kristiina Jokinen for their advice and support from beginning to end.Finally, we thank all the authors of the papers in this volume, and all the conference participants for making this stimulating event a valuable opportunity for growth in the research areas of discourse and dialogue. AbstractThis paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent QNetworks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state. IntroductionTask-oriented dialog systems have been an important branch of spoken dialog system (SDS) research (Raux et al., 2005; Young, 2006; Bohus and Rudnicky, 2003). The SDS agent has to achieve some predefined targets (e.g. booking a flight) through natural language interaction with the users. The typical structure of a task-oriented dialog system is outlined in Figure 1 (Young, 2006). This pipeline consists of several independently-developed modules: natural language understanding (the NLU) maps the user utterances to some semantic representation. This information is further processed by the dialog state tracker (DST), which accumulates the input of the turn along with the dialog history. The DST outputs the current dialog state and the dialog policy selects the next system action based on the dialog state. Then natural language gene...

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“…Chambers and Jurafsky (2008) first proposed an unsupervised approach to learn partially ordered sets of events from raw text. Many expansions have been introduced later, including unsupervisedly learning narrative schemas and scripts (Chambers and Jurafsky, 2009;Regneri et al, 2011), event schemas and frames (Chambers and Jurafsky, 2011;Balasubramanian et al, 2013;Sha et al, 2016;Huang et al, 2016;Mostafazadeh et al, 2016b), and some generative models to learn latent structures of event knowledge (Cheung et al, 2013;Chambers, 2013;Bamman et al, 2014;Nguyen et al, 2015). Another direction for learning event-centred knowledge is causality identification (Do et al, 2011;Radinsky et al, 2012;Berant et al, 2014;Hashimoto et al, 2015;Gui et al, 2016), which tried to identify the causality relation in text.…”

Section: Related Workmentioning

confidence: 99%

Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension

Lin¹,

Sun²,

Han³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Reasoning with commonsense knowledge is critical for natural language understanding.Traditional methods for commonsense machine comprehension mostly only focus on one specific kind of knowledge, neglecting the fact that commonsense reasoning requires simultaneously considering different kinds of commonsense knowledge. In this paper, we propose a multi-knowledge reasoning method, which can exploit heterogeneous knowledge for commonsense machine comprehension. Specifically, we first mine different kinds of knowledge (including event narrative knowledge, entity semantic knowledge and sentiment coherent knowledge) and encode them as inference rules with costs. Then we propose a multiknowledge reasoning model, which selects inference rules for a specific reasoning context using attention mechanism, and reasons by summarizing all valid inference rules. Experiments on RocStories show that our method outperforms traditional models significantly.

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Generative Event Schema Induction with Entity Disambiguation

Cited by 62 publications

References 18 publications

Story Comprehension for Predicting What Happens Next

Story Comprehension for Predicting What Happens Next

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension

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