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

Released as a article .

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.
In text/plain format

Archived Files and Locations

application/pdf  1.0 MB
file_wbyattekvfct7hmhhmyznuwoz4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2021-06-01
Version   v1
Language   en ?
arXiv  2106.00510v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: d71fe90b-7b94-42bf-8cea-7e49d5b16ad3
API URL: JSON