Decomposing Generalization: Models of Generic, Habitual, and Episodic                     Statements

Govindarajan, Venkata Subrahmanyan; Durme, Benjamin Van; White, Aaron Steven

doi:10.1162/tacl_a_00285

Cited by 12 publications

(12 citation statements)

References 42 publications

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“…Prior to analysis, we ridit score the confidence ratings by annotator to normalize them for differences in annotator scale use (see Govindarajan et al, 2019 for discussion of ridit scoring confidence ratings in a similar annotation protocol). This method maps ordinal labels to (0, 1) on the basis of the empirical CDF of each annotator's responses-with values closer to 0 implying lower confidence and those nearer 1 implying higher confidence.…”

Section: Interannotator Agreementmentioning

confidence: 99%

“…• FACTUALITY (Rudinger et al, 2018) factual: did the event happen? • GENERICITY (Govindarajan et al, 2019) kind: is the event generic? hypothetical: is the event hypothetical?…”

Section: Arxiv:210310387v1 [Cscl] 18 Mar 2021 2 Backgroundmentioning

confidence: 99%

“…• GENERICITY (Govindarajan et al, 2019) kind: is the event generic? hypothetical: is the event hypothetical?…”

Section: Introductionmentioning

confidence: 99%

“…• GENERICITY (Govindarajan et al, 2019) particular: is the entity a particular? kind: is the entity a kind?…”

Section: Introductionmentioning

confidence: 99%

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Decomposing and Recomposing Event Structure

Gantt

Glass

White

2022

Transactions of the Association for Computational Linguistics

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We present an event structure classification empirically derived from inferential properties annotated on sentence- and document-level Universal Decompositional Semantics (UDS) graphs. We induce this classification jointly with semantic role, entity, and event-event relation classifications using a document-level generative model structured by these graphs. To support this induction, we augment existing annotations found in the UDS1.0 dataset, which covers the entirety of the English Web Treebank, with an array of inferential properties capturing fine-grained aspects of the temporal and aspectual structure of events. The resulting dataset (available at decomp.io) is the largest annotation of event structure and (partial) event coreference to date.

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Section: Interannotator Agreementmentioning

confidence: 99%

“…• FACTUALITY (Rudinger et al, 2018) factual: did the event happen? • GENERICITY (Govindarajan et al, 2019) kind: is the event generic? hypothetical: is the event hypothetical?…”

Section: Arxiv:210310387v1 [Cscl] 18 Mar 2021 2 Backgroundmentioning

confidence: 99%

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Decomposing and Recomposing Event Structure

Gantt

Glass

White

2022

Transactions of the Association for Computational Linguistics

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“…Response For judging the response, we combine conversation acts with communicative intents as in Figure 2, in the spirit of the compositional semantic framework of Govindarajan et al (2019). The taxonomy is a result of a combination of expert involvement, data observation and user feedback.…”

Section: Rmentioning

confidence: 99%

Did they answer? Subjective acts and intents in conversational discourse

Ferracane¹,

Durrett²,

Li³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Discourse signals are often implicit, leaving it up to the interpreter to draw the required inferences. At the same time, discourse is embedded in a social context, meaning that interpreters apply their own assumptions and beliefs when resolving these inferences, leading to multiple, valid interpretations. However, current discourse data and frameworks ignore the social aspect, expecting only a single ground truth. We present the first discourse dataset with multiple and subjective interpretations of English conversation in the form of perceived conversation acts and intents. We carefully analyze our dataset and create computational models to (1) confirm our hypothesis that taking into account the bias of the interpreters leads to better predictions of the interpretations, (2) and show disagreements are nuanced and require a deeper understanding of the different contextual factors. We share our dataset and code at http://github.com/ elisaF/subjective_discourse.

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