Another Look at Causality: Discovering Scenario-Specific Contingency Relationships with No Supervision

Riaz, Muhammad Sohail; Gîrju, Roxana

doi:10.1109/icsc.2010.19

Cited by 58 publications

(68 citation statements)

References 16 publications

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“…We hypothesize that personal stories are a valuable resource to learn common-sense knowledge about relations between everyday events and that finergrained knowledge can be learned from topicsorted stories (Riaz and Girju, 2010) that share a particular theme, so we construct two different sets of stories: General-Domain Set. We created a random subset from the Spinn3r corpus from personal blog domains: livejournal.com, wordpress.com, blogspot.com, spaces.live.com, typepad.com, travelpod.com.…”

Section: A Corpus Of Everyday Eventsmentioning

confidence: 99%

“…Historically, work on scripts explicitly modeled causality (Lehnert, 1981;Mooney and DeJong, 1985) inter alia. Our work is motivated by Penn Discourse Treebank (PDTB) definition of CONTINGENCY that has two types: CAUSE and CONDITION, and is more similar to approaches that learn specific event relations such as contingency or causality (Hu et al, 2013;Do et al, 2011;Girju, 2003;Riaz and Girju, 2010;Rink et al, 2010;Chklovski and Pantel, 2004). Our contributions are as follows:…”

Section: Stormmentioning

confidence: 99%

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Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events

Rahimtoroghi

Hernandez²,

Walker

2016

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

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Much of the user-generated content on social media is provided by ordinary people telling stories about their daily lives. We develop and test a novel method for learning fine-grained common-sense knowledge from these stories about contingent (causal and conditional) relationships between everyday events. This type of knowledge is useful for text and story understanding, information extraction, question answering, and text summarization. We test and compare different methods for learning contingency relation, and compare what is learned from topic-sorted story collections vs. general-domain stories. Our experiments show that using topic-specific datasets enables learning finer-grained knowledge about events and results in significant improvement over the baselines. An evaluation on Amazon Mechanical Turk shows 82% of the relations between events that we learn from topicsorted stories are judged as contingent.

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Section: A Corpus Of Everyday Eventsmentioning

confidence: 99%

Section: Stormmentioning

confidence: 99%

Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events

Rahimtoroghi

Hernandez²,

Walker

2016

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

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“…For event causality extraction, clues used by previous methods can roughly be categorized as lexico-syntactic patterns (Abe et al, 2008;Radinsky et al, 2012), words in context , associations among words (Torisawa, 2006;Riaz and Girju, 2010;Do et al, 2011), and predicate semantics (Hashimoto et al, 2012). Besides features similar to those described above, we propose semantic relation features 3 that include those that are not obviously related to causality.…”

Section: Related Workmentioning

confidence: 99%

Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features

Hashimoto

Torisawa

Kloetzer

et al. 2014

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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We propose a supervised method of extracting event causalities like conduct slash-and-burn agriculture→exacerbate desertification from the web using semantic relation (between nouns), context, and association features. Experiments show that our method outperforms baselines that are based on state-of-the-art methods. We also propose methods of generating future scenarios like conduct slash-and-burn agriculture→exacerbate desertification→increase Asian dust (from China)→asthma gets worse.Experiments show that we can generate 50,000 scenarios with 68% precision. We also generated a scenario deforestation continues→global warming worsens→sea temperatures rise→vibrio parahaemolyticus fouls (water), which is written in no document in our input web corpus crawled in 2007. But the vibrio risk due to global warming was observed in Baker-Austin et al. (2013). Thus, we "predicted" the future event sequence in a sense.

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“…Historically, work on scripts explicitly modeled causality (Lehnert, 1981;Mooney and DeJong, 1985) inter alia. Our work is motivated by Penn Discourse Treebank (PDTB) definition of CONTINGENCY that has two types: CAUSE and CONDITION, and is more similar to approaches that learn specific event relations such as contingency or causality Do et al, 2011;Girju, 2003;Riaz and Girju, 2010;Rink et al, 2010;Chklovski and Pantel, 2004). Our contributions are as follows:…”

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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Another Look at Causality: Discovering Scenario-Specific Contingency Relationships with No Supervision

Cited by 58 publications

References 16 publications

Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events

Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events

Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features

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

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