Using narrative functions as a heuristic for relevance in story understanding

Tomai, Emmett; Forbus, Ken

doi:10.1145/1822309.1822318

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Similarly, advertisement systems on social media should be able to reason about the emotional reactions of people after events such as mass shootings and remove ads for guns which might increase social distress (Goel and Isaac, 2016). Also, pragmatic inference is a necessary step toward automatic narrative understanding and generation (Tomai and Forbus, 2010;Ding and Riloff, 2016;Ding et al, 2017). However, this type of social commonsense reasoning goes far beyond the widely studied entailment tasks (Bowman et al, 2015;Dagan et al, 2006) and thus falls outside the scope of existing benchmarks.…”

Section: Introductionmentioning

confidence: 99%

Event2Mind: Commonsense Inference on Events, Intents, and Reactions

Rashkin

Sap

Allaway

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

116

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We investigate a new commonsense inference task: given an event described in a short free-form text ("X drinks coffee in the morning"), a system reasons about the likely intents ("X wants to stay awake") and reactions ("X feels alert") of the event's participants. To support this study, we construct a new crowdsourced corpus of 25,000 event phrases covering a diverse range of everyday events and situations. We report baseline performance on this task, demonstrating that neural encoder-decoder models can successfully compose embedding representations of previously unseen events and reason about the likely intents and reactions of the event participants. In addition, we demonstrate how commonsense inference on people's intents and reactions can help unveil the implicit gender inequality prevalent in modern movie scripts.

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Section: Introductionmentioning

confidence: 99%

Event2Mind: Commonsense Inference on Events, Intents, and Reactions

Rashkin

Sap

Allaway

et al. 2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

116

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“…On another line of research, increasing attention to event understanding has been gained by downstream applications based on events. A non-exhaustive list includes, inter alia, Tomai and Forbus (2010); Mostafazadeh et al (2017); Chaturvedi, Peng, and Roth (2017) for narrative prediction, Berant et al (2014) for machine comprehension, Zhukov et al (2019);Fried et al (2020) for video segmentation. By means of STEPS, we provide a simple general-purpose architecture for event understanding which, besides its high performances achieved in intent prediction, can also be easily integrated into the aforementioned applications and those requiring commonsense reasoning based on chains of activities, thus potentially advancing the state of the art.…”

Section: Related Workmentioning

confidence: 99%

STEPS: Semantic Typing of Event Processes with a Sequence-to-Sequence Approach

Pepe

Barba

Blloshmi

et al. 2022

AAAI

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Enabling computers to comprehend the intent of human actions by processing language is one of the fundamental goals of Natural Language Understanding. An emerging task in this context is that of free-form event process typing, which aims at understanding the overall goal of a protagonist in terms of an action and an object, given a sequence of events. This task was initially treated as a learning-to-rank problem by exploiting the similarity between processes and action/object textual definitions. However, this approach appears to be overly complex, binds the output types to a fixed inventory for possible word definitions and, moreover, leaves space for further enhancements as regards performance. In this paper, we advance the field by reformulating the free-form event process typing task as a sequence generation problem and put forward STEPS, an end-to-end approach for producing user intent in terms of actions and objects only, dispensing with the need for their definitions. In addition to this, we eliminate several dataset constraints set by previous works, while at the same time significantly outperforming them. We release the data and software at https://github.com/SapienzaNLP/steps.

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“…Advertisement systems on social media should be able to reason about the emotional reactions of people after events such as mass shootings and remove ads for guns which might increase social distress Goel and Isaac 1 ; (Rashkin et al, 2018). Also, as one kind of pragmatic inference, emotion inference is a necessary step toward automatic narrative understanding and generation (Tomai and Forbus, 2010;Riloff, 2016, 2018).…”

Section: Introductionmentioning

confidence: 99%

Emotion Action Detection and Emotion Inference: the Task and Dataset

Liu,

Du,

Zhao

et al. 2019

Preprint

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Many NLP works on emotion analysis only focus on simple emotion classification without exploring the potentials of putting emotion into "event context", and ignore the analysis of emotion-related events.One main reason is the lack of this kind of corpus. Here we present CEAC (Cause-Emotion-Action Corpus), which manually annotates not only emotion, but also cause events and action events. For example, "After cause listening to what I said /cause , the teacher was emotion happy /emotion and then action joked with me /action ." And we propose two new tasks based on CEAC: emotion causality and emotion inference. The first task is to extract a triple (cause, emotion, action) as CEA relation. The second task is to infer the probable emotion(here tends to be "happy") given a tuple of cause and action events ("listening to what I said", "joked with me"). We are currently releasing CEAC with 10,603 samples and 15,892 events, basic statistic analysis and baseline on both emotion causality and emotion inference tasks. Baseline performance demonstrates that there is much room for both tasks to be improved.

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Using narrative functions as a heuristic for relevance in story understanding

Cited by 4 publications

References 12 publications

Event2Mind: Commonsense Inference on Events, Intents, and Reactions

Event2Mind: Commonsense Inference on Events, Intents, and Reactions

STEPS: Semantic Typing of Event Processes with a Sequence-to-Sequence Approach

Emotion Action Detection and Emotion Inference: the Task and Dataset

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