GLUCOSE: GeneraLized and COntextualized Story Explanations

Mostafazadeh, Nasrin; Kalyanpur, Aditya; Moon, Lori; Buchanan, D. W.; Berkowitz, Lauren; Biran, Or; Chu-Carroll, Jennifer

doi:10.18653/v1/2020.emnlp-main.370

Cited by 64 publications

(72 citation statements)

References 33 publications

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“…To address the insufficient annotated example problem, we employ a large number of external causal statements (Sap et al, 2018;Mostafazadeh et al, 2020) that can support adequate evidence of context-specific causal patterns for understanding event causalities. For example in Figure 1, the context-specific causal pattern support by an external causal statement S 2 is helpful for identifying the causality of event noticed E1 and event alerted E3 in S 1 , which is unseen when only training with labeled data.…”

Section: Billy Finds His Childhood Teddy Bear >Causes/enables>mentioning

confidence: 99%

Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement

Zuo¹,

Cao²,

Chen³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Current models for event causality identification (ECI) mainly adopt a supervised framework, which heavily rely on labeled data for training. Unfortunately, the scale of current annotated datasets is relatively limited, which cannot provide sufficient support for models to capture useful indicators from causal statements, especially for handing those new, unseen cases. To alleviate this problem, we propose a novel approach, shortly named CauSeRL, which leverages external causal statements for event causality identification. First of all, we design a self-supervised framework to learn context-specific causal patterns from external causal statements. Then, we adopt a contrastive transfer strategy to incorporate the learned context-specific causal patterns into the target ECI model. Experimental results show that our method significantly outperforms previous methods on EventSto-ryLine and Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively).

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Section: Billy Finds His Childhood Teddy Bear >Causes/enables>mentioning

confidence: 99%

Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement

Zuo¹,

Cao²,

Chen³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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“…Recently, large pretrained language models (LMs) such as GPT-2 have shown remarkable performance on various generation tasks. While these pretrained LMs learn probabilistic associations between words and sentences, they still have difficulties in modeling causality (Mostafazadeh et al, 2020). Also, in narrative story generation, models need to be consistent with everyday commonsense norms.…”

Section: Implicit Inference Rules Effectmentioning

confidence: 99%

“…However, the generated knowledge from COMET is noncontextualized and hence, can be inconsistent. Recently, Mostafazadeh et al (2020) proposed GLU-COSE, a new resource and dataset that offers semistructured commonsense inference rules that are grounded in sentences of specific stories. They show that fine-tuning a pre-trained LM on the GLUCOSE dataset helps the model to better generate inferrable commonsense explanations given a complete story.…”

Section: Related Workmentioning

confidence: 99%

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COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion

Paul

Frank

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque. We posit that such models can be made more interpretable by explicitly generating interim inference rules, and using them to guide the generation of task-specific textual outputs. In this paper we present COINS, a recursive inference framework that i) iteratively reads context sentences, ii) dynamically generates contextualized inference rules, encodes them, and iii) uses them to guide task-specific output generation. We apply COINS to a Narrative Story Completion task that asks a model to complete a story with missing sentences, to produce a coherent story with plausible logical connections, causal relationships, and temporal dependencies. By modularizing inference and sentence generation steps in a recurrent model, we aim to make reasoning steps and their effects on next sentence generation transparent. Our automatic and manual evaluations show that the model generates better story sentences than SOTA baselines, especially in terms of coherence. We further demonstrate improved performance over strong pre-trained LMs in generating commonsense inference rules. The recursive nature of COINS holds the potential for controlled generation of longer sequences.

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“…ATOMIC is person centric, hence it can not be used in sentences describing events. Mostafazadeh et al (2020) constructs GLUCOSE (GeneraLized and COntextualized Story Explanations), a large-scale dataset of implicit commonsense causal knowledge, which sentences can describe any event/state. Each GLUCOSE entry is organized into a story-specific causal statement paired with an inference rule generalized from the statement.…”

Section: Benchmarksmentioning

confidence: 99%

Proceedings of the Third Workshop on Narrative Understanding

2021

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While natural language understanding of longform documents remains an open challenge, such documents often contain structural information that can inform the design of models encoding them. Movie scripts are an example of such richly structured text -scripts are segmented into scenes, which decompose into dialogue and descriptive components. In this work, we propose a neural architecture to encode this structure, which performs robustly on two multi-label tag classification tasks without using handcrafted features. We add a layer of insight by augmenting the encoder with an unsupervised 'interpretability' module, which can be used to extract and visualize narrative trajectories. Though this work specifically tackles screenplays, we discuss how the underlying approach can be generalized to a range of structured documents.

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GLUCOSE: GeneraLized and COntextualized Story Explanations

Abstract: Current affiliation Verneek, Inc.

Cited by 64 publications

References 33 publications

Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement

Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement

COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion

Proceedings of the Third Workshop on Narrative Understanding

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