The Event StoryLine Corpus: A New Benchmark for Causal and Temporal
            Relation Extraction

Caselli, Tommaso; Vossen, Piek

doi:10.18653/v1/w17-2711

Cited by 86 publications

(75 citation statements)

References 17 publications

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“…The resulting dataset contains 2,470 sentences, of which 244 are causal. Event StoryLine (EventSL): In this dataset (Caselli and Vossen, 2017), we detected causal sentences by examining the CAUSES and CAUSED BY attributes in the PLOT LINK tags, again following Li and Mao (2019). Again, we discarded causal relationships between entities from different sentences.…”

Section: Datasetsmentioning

confidence: 99%

Transfer Learning for Causal Sentence Detection

Kyriakakis¹,

Androutsopoulos²,

Saudabayev³

et al. 2019

Proceedings of the 18th BioNLP Workshop and Shared Task

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We consider the task of detecting sentences that express causality, as a step towards mining causal relations from texts. To bypass the scarcity of causal instances in relation extraction datasets, we exploit transfer learning, namely ELMO and BERT, using a bidirectional GRU with self-attention (BIGRUATT) as a baseline. We experiment with both generic public relation extraction datasets and a new biomedical causal sentence detection dataset, a subset of which we make publicly available. We find that transfer learning helps only in very small datasets. With larger datasets, BIGRU-ATT reaches a performance plateau, then larger datasets and transfer learning do not help.

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Section: Datasetsmentioning

confidence: 99%

Transfer Learning for Causal Sentence Detection

Kyriakakis¹,

Androutsopoulos²,

Saudabayev³

et al. 2019

Proceedings of the 18th BioNLP Workshop and Shared Task

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“…The EventStoryLine corpus is the largest dataset for causal relation identification till now with comprehensive event causal relations annotated, both intra-sentence and cross-sentence, which presents unique challenges for causal relation identification. Caselli and Vossen (2017) showed that only 117 annotated causal relations in this dataset are indicated by explicit causal cue phrases while the others are implicit. We conduct experiments on the EventStoryLine dataset.…”

Section: Related Workmentioning

confidence: 92%

“…Our approach innovates on modeling other aspects of document-level causal structures, especially heavy involvements of main events in causal relations, that facilitate resolving multiple causal relations. Table 1 shows the statistics of the corpus EventStoryLine v0.9 1 (Caselli and Vossen, 2017 Causal relations annotated in EventStoryLine are between two event mentions. Different causal relations are annotated in EventStoryLine, called "rising action" and "falling action", which indicate the directions of causal relations and intuitively correspond to "precondition" and "consequence" relations.…”

Section: Related Workmentioning

confidence: 99%

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Modeling Document-level Causal Structures for Event Causal Relation Identification

Gao¹,

Choubey²,

Huang³

2019

Proceedings of the 2019 Conference of the North

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We aim to comprehensively identify all the event causal relations in a document, both within a sentence and across sentences, which is important for reconstructing pivotal event structures. The challenges we identified are two: 1) event causal relations are sparse among all possible event pairs in a document, in addition, 2) few causal relations are explicitly stated. Both challenges are especially true for identifying causal relations between events across sentences. To address these challenges, we model rich aspects of document-level causal structures for achieving comprehensive causal relation identification. The causal structures include heavy involvements of document-level main events in causal relations as well as several types of finegrained constraints that capture implications from certain sentential syntactic relations and discourse relations as well as interactions between event causal relations and event coreference relations. Our experimental results show that modeling the global and fine-grained aspects of causal structures using Integer Linear Programming (ILP) greatly improves the performance of causal relation identification, especially in identifying cross-sentence causal relations.

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“…Of various efforts in guided generation [Ammanabrolu et al, 2019;Tang et al, 2019;Clark et al, 2018;Hu et al, 2019b], Sentential Causal Resource # CE Pairs TCR [Ning et al, 2018] 172 SemEval-2007Task4 [Girju et al, 2007 220 Causal-TimeBank [Mirza et al, 2014] 318 CaTeRS [Mostafazadeh et al, 2016] 488 EventCausalityData [Do et al, 2011] 580 RED [O'Gorman et al, 2016] 1,147 SemEval2010 Task8 [Hendrickx et al, 2009] 1,331 BECauSE 2.0 [Dunietz et al, 2017b] 1,803 EventStoryLine [Caselli and Vossen, 2017] 5,519 PDTB 2.0 [Prasad et al, 2008] 8,042 Altlex [Hidey and McKeown, 2016] 9,190 PDTB 3.0 [Webber et al, 2019] 13 K DisSent [Nie et al, 2019] 167 K CausalBank (Ours) 314 M…”

Section: Related Workmentioning

confidence: 99%

Guided Generation of Cause and Effect

Ding

Liu

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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We present a conditional text generation framework that posits sentential expressions of possible causes and effects. This framework depends on two novel resources we develop in the course of this work: a very large-scale collection of English sentences expressing causal patterns (CausalBank); and a refinement over previous work on constructing large lexical causal knowledge graphs (Cause Effect Graph). Further, we extend prior work in lexically-constrained decoding to support disjunctive positive constraints. Human assessment conﬁrms that our approach gives high-quality and diverse outputs. Finally, we use CausalBank to perform continued training of an encoder supporting a recent state-of-the-art model for causal reasoning, leading to a 3-point improvement on the COPA challenge set, with no change in model architecture.

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The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction

Cited by 86 publications

References 17 publications

Transfer Learning for Causal Sentence Detection

Transfer Learning for Causal Sentence Detection

Modeling Document-level Causal Structures for Event Causal Relation Identification

Guided Generation of Cause and Effect

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