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A Multi-Axis Annotation Scheme for Event Temporal Relations
TLDR
A new multi-axis modeling to better capture the temporal structure of events is proposed and it is identified that event end-points are a major source of confusion in annotation, so it is proposed to annotate TempRels based on start-points only.
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Joint Reasoning for Temporal and Causal Relations
TLDR
This paper forms the joint problem as an integer linear programming (ILP) problem, enforcing constraints that are inherent in the nature of time and causality, and shows that the joint inference framework results in statistically significant improvement in the extraction of both temporal and causal relations from text.
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“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding
TLDR
It is found that the best current methods used on MCTACO are still far behind human performance, by about 20%, and several directions for improvement are discussed.
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Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction
TLDR
Experiments show that the proposed method can improve both event extraction and temporal relation extraction over state-of-the-art systems, with the end-to-end F1 improved by 10% and 6.8% on two benchmark datasets respectively.
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Evaluating Models’ Local Decision Boundaries via Contrast Sets
TLDR
A more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data, and recommends that the dataset authors manually perturb the test instances in small but meaningful ways that (typically) change the gold label, creating contrast sets.
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A Structured Learning Approach to Temporal Relation Extraction
TLDR
It is suggested that it is important to take dependencies into account while learning to identify temporal relations between events and a structured learning approach is proposed to address this challenge.
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CogCompTime: A Tool for Understanding Time in Natural Language
TLDR
This paper introduces CogCompTime, a system that has these two important functionalities and incorporates the most recent progress, achieves state-of-the-art performance, and is publicly available at http://cogcomp.org/page/publication_view/844.
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Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource
TLDR
It is shown that existing temporal extraction systems can be improved via this resource and that interesting statistics can be retrieved from this resource, which can potentially benefit other time-aware tasks.
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Temporal Common Sense Acquisition with Minimal Supervision
TLDR
This work proposes a novel sequence modeling approach that exploits explicit and implicit mentions of temporal common sense, extracted from a large corpus, to build TacoLM, a temporal commonsense language model.
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TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
TLDR
TORQUE is introduced, a new English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships, and results show that RoBERTa-large achieves an exact-match score of 51% on the test set of TORQUE, about 30% behind human performance.
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