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ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
Experimental results demonstrate that multitask models that incorporate the hierarchical structure of if-then relation types lead to more accurate inference compared to models trained in isolation, as measured by both automatic and human evaluation.
Semi-supervised sequence tagging with bidirectional language models
A general semi-supervised approach for adding pretrained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks, surpassing previous systems that use other forms of transfer or joint learning with additional labeled data and task specific gazetteers.
WINOGRANDE: An Adversarial Winograd Schema Challenge at Scale
This work introduces WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset, and establishes new state-of-the-art results on five related benchmarks.
Abductive Commonsense Reasoning
This study introduces a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations, and conceptualizes two new tasks -- Abductive NLI: a multiple-choice question answering task for choosing the more likely explanation, and Abduction NLG: a conditional generation task for explaining given observations in natural language.
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
A constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning, and demonstrates that the learned generative Commonsense reasoning capability can be transferred to improve downstream tasks such as CommonsenseQA by generating additional context.
TabEL: Entity Linking in Web Tables
TabEL differs from previous work by weakening the assumption that the semantics of a table can be mapped to pre-defined types and relations found in the target KB, and enforces soft constraints in the form of a graphical model that assigns higher likelihood to sets of entities that tend to co-occur in Wikipedia documents and tables.
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning
This paper introduces Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions, and proposes a new architecture that improves over the competitive baselines.
COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs
It is proposed that manually constructed CSKGs will never achieve the coverage necessary to be applicable in all situations encountered by NLP agents, and a new evaluation framework for testing the utility of KGs based on how effectively implicit knowledge representations can be learned from them is proposed.
Construction of the Literature Graph in Semantic Scholar
This paper reduces literature graph construction into familiar NLP tasks, point out research challenges due to differences from standard formulations of these tasks, and report empirical results for each task.
Unsupervised Commonsense Question Answering with Self-Talk
- Vered Shwartz, Peter West, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi
- Computer ScienceEMNLP
- 11 April 2020
An unsupervised framework based on self-talk as a novel alternative to multiple-choice commonsense tasks, inspired by inquiry-based discovery learning, which improves performance on several benchmarks and competes with models that obtain knowledge from external KBs.