Learning Fine-Grained Knowledge about Contingent Relations between
            Everyday Events

Rahimtoroghi, Elahe; Hernandez, Ernesto; Walker, Marilyn A.

doi:10.18653/v1/w16-3644

Cited by 16 publications

(23 citation statements)

References 15 publications

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“…This may be because the Drama category is a catch-all (over half of the films are categorized this way suggesting that it has low coherence as a genre). The poor performance on Drama would then be consistent with previous work that shows that topical coherence (genre in this case) improves causal relation learning (Rahimtoroghi et al, 2016;Riaz and Girju, 2010). We will return to this point in Section 3.4.…”

supporting

confidence: 84%

“…Theories of narrative posit that NARRATIVE CAUSAL-ITY underlies human understanding of a narrative (Warren et al, 1979;Trabasso et al, 1989;Van den Broek, 1990). However previous computational work on narrative schemas, scripts or event schemas learn "collections of events that tend to co-occur" (Chambers and Jurafsky, 2008; Balasubramanian et al, 2013;Pichotta and Mooney, 2014), rather than causal relations between events (Rahimtoroghi et al, 2016). Another limitation of previous work is that it has mostly been applied to newswire, limiting what is learned to relations between newsworthy events, rather than everyday events (Rahimtoroghi et al, 2016;Beamer and Girju, 2009;Manshadi et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…1 This paper learns causal pairs from a corpus of 955 films. Because previous work shows that more specific, detailed causal relations can be learned from topic-sorted corpora (Riaz and Girju, 2010;Rahimtoroghi et al, 2016), we explore differences in learning between genres of film, positing e.g. that horror films may feature very different types of events than comedies.…”

Section: Introductionmentioning

confidence: 99%

“…Since most previous work on narrative schemas, scripts, event schemas or rel-grams has only been applied to one large corpus of newswire (Gigaword corpus), these methods have only learned relations about newsworthy topics, and even then, perhaps only the most frequent, highly common news events. In contrast, both our approach and that of Rahimtoroghi et al (2016) learn fine-grained causal relations that underly narratives, which we believe are more in the spirit of Schank's original motivation for scripts Schank et al, 1977;de Jong, 1979 causality relations between events in PDTB in context (both verbs and nouns) (Prasad et al, 2008). They present a detailed formula for calculating contingency/causality that takes into account several different kinds of argument overlap between adjacent events.…”

mentioning

confidence: 97%

“…However previous computational work on narrative schemas, scripts or event schemas learn "collections of events that tend to co-occur" (Chambers and Jurafsky, 2008; Balasubramanian et al, 2013;Pichotta and Mooney, 2014), rather than causal relations between events (Rahimtoroghi et al, 2016). Another limitation of previous work is that it has mostly been applied to newswire, limiting what is learned to relations between newsworthy events, rather than everyday events (Rahimtoroghi et al, 2016;Beamer and Girju, 2009;Manshadi et al, 2008).Our focus here is on NARRATIVE CAUSAL-ITY (Trabasso et al, 1989;Van den Broek, 1990), the four different relations posited by narrative theories to underly narrative coherence:• PHYSICAL: Event A physically causes event B to happen• MOTIVATIONAL: Event A happens with B as a motivation• PSYCHOLOGICAL: Event A brings about emotions (expressed in event B)• ENABLING: Event A creates a state or condition for B to happen. A enables B.…”

mentioning

confidence: 99%

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Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

2017

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We are excited to welcome you to this year's SIGdial Conference, the 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue. We are pleased to hold the conference in Saarbrücken on August 15-17th, co-located with SemDial 2017, and in close proximity to both INTERSPEECH 2017 and YRRSDS 2017, the Young Researchers' Roundtable on Spoken Dialog Systems.The SIGdial conference remains a premier publication venue for research in discourse and dialogue. This year, the program includes oral presentations, poster sessions, and one demo session. SIGdial 2017 also hosts three special sessions, two joint with SemDial 2017.We received 113 submissions this year. In only one previous year has there been a greater number of submissions to SIGdial. All long and short papers received at least 3 reviews. We carefully considered both the numeric ratings and the tenor of the comments in making our selections for the program. Overall, the members of the Program Committee did an excellent job in reviewing the submitted papers. We thank them for their important role in selecting the accepted papers and for helping to come up with a high quality program for the conference. We also thank Pierre Lison, Mentoring Chair for SIGdial 2017, for his dedicated work on the mentoring process. The goal of mentoring is to assist authors of papers that contain important ideas but lack clarity. In line with the SIGdial tradition, our aim has been to create a balanced program that accommodates as many favorably rated papers as possible. We accepted 29 long papers, 11 short papers and 7 demo presentations. These numbers give an overall acceptance rate of 41.6%. The rates separately for types of papers are 42% for long papers, 30% for short and 87% for demo submissions. Of the long papers, 18 were presented as oral presentations. The remaining 11 long papers and all the short papers were presented as posters, split into two poster sessions.This year SIGdial has three special sessions on topics of growing interest. The chosen sessions were (i) the Special Session on Negotiation Dialog organized by Amanda Stent, Aasish Pappu, Diane Litman and Marilyn Walker; (ii) the Second WOCHAT Special Session on Chatbots and Conversational Agents organized by Ryuichiro Higashinaka, Ron Artstein, Rafael E. Banchs, and Wolfgang Minker; and (iii) Special Session on Natural Language Generation for Dialog Systems organized by Marilyn Walker, Verena Rieser, Vera Demberg, Dietrich Klakow, Dilek Hakkani-Tur, David M. Howcroft and Shereen Oraby. These specialized topics brought diverse paper submissions to our technical program. At the conference, the special sessions also featured panel discussions and position talks, allowing for active engagement of the conference participants. This year, two of these special sessions, the one on negotiation and on conversational agents, are part of the joint SIGdial/SemDial program at the conference bringing both communities to participate in them. This year's SIGdial conference runs 3 full days compared to previous year...

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supporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 97%

mentioning

confidence: 99%

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Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

2017

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A deep learning model for mining and detecting causally related events in tweets

Kayesh

Islam

Wang

et al. 2020

Concurrency and Computation

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Nowadays, public gatherings and social events are an integral part of a modern city life. To run such events seamlessly, it requires real time mining and monitoring of causally related events so that the management can make informed decisions and take appropriate actions. The automatic detection of event causality from short text such as tweets could be useful for event management in this context. However, detecting event causality from tweets is a challenging task. Tweets are short, unstructured, and often written in highly informal language which lacks enough contextual information to detect causality. The existing approaches apply different techniques including hand‐crafted linguistic rules and machine learning models. However, none of the approaches tackle the issue related to the lack of contextual information. In this paper, we detect event causality in tweets by applying a context word extension technique and a deep causal event detection model. The context word extension technique is driven by background knowledge extracted from one million news articles. Our model achieves 79.35% recall and 67.28% f1‐score, which are 17.39% and 2.33% improvements to the state‐of‐the‐art approach.

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Edge‐featured graph attention network with dependency features for causality detection of events

et al. 2023

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Causality detection, as a more fine‐grained task than causality extraction, which aims to detect the components that represent the cause and effect in sentence‐level texts with causality, is a significant task in the field of Natural Language Processing (NLP). Previous research on causality detection has concentrated on text token features whilst ignoring the dependency attributes between tokens. In this paper, we propose a model that uses the Edge‐featured Graph Attention Network based on dependency‐directed graphs for the causality detection task. To begin, we convert the texts with causality into the representation of dependency‐directed graphs (DDG), which regard the dependency attributes between tokens as edge features. Then we use Edge‐featured Graph Attention Network to aggregate the node and edge features of DDG. Finally, we put the graph embedding into Bi‐directional Long Short‐Term Memory (BiLSTM) layer to learn the dependencies between forward and backward long‐distance nodes in DDG. Experiments on three datasets prove that this method achieves better performance in precision, recall, and other evaluation metrics compared with other methods.

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Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events

Cited by 16 publications

References 15 publications

Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

A deep learning model for mining and detecting causally related events in tweets

Edge‐featured graph attention network with dependency features for causality detection of events

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