CIDER: Commonsense Inference for Dialogue Explanation and Reasoning
release_rc3rnsib4bgjjir4jizpfzhbea
by
Deepanway Ghosal and Pengfei Hong and Siqi Shen and Navonil Majumder and Rada Mihalcea and Soujanya Poria
2021
Abstract
Commonsense inference to understand and explain human language is a
fundamental research problem in natural language processing. Explaining human
conversations poses a great challenge as it requires contextual understanding,
planning, inference, and several aspects of reasoning including causal,
temporal, and commonsense reasoning. In this work, we introduce CIDER -- a
manually curated dataset that contains dyadic dialogue explanations in the form
of implicit and explicit knowledge triplets inferred using contextual
commonsense inference. Extracting such rich explanations from conversations can
be conducive to improving several downstream applications. The annotated
triplets are categorized by the type of commonsense knowledge present (e.g.,
causal, conditional, temporal). We set up three different tasks conditioned on
the annotated dataset: Dialogue-level Natural Language Inference, Span
Extraction, and Multi-choice Span Selection. Baseline results obtained with
transformer-based models reveal that the tasks are difficult, paving the way
for promising future research. The dataset and the baseline implementations are
publicly available at https://github.com/declare-lab/CIDER.
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