News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events.This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it.This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models.We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful.We release all resources for future research on this topic.1

A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated.However, current machine reading comprehension benchmarks have practically no questions that test temporal phenomena, so systems trained on these benchmarks have no capacity to answer questions such as "what happened before/after [some event]?"We introduce TORQUE, a new English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships.Results show that RoBERTa-large achieves an exact-match score of 51% on the test set of TORQUE, about 30% behind human performance.1 1 https://allennlp.org/torque.htmlHeavy snow is causing disruption to transport across the UK, with heavy rainfall bringing flooding to the south-west of England.Rescuers searching for a woman trapped in a landslide at her home in Looe, Cornwall, said they had found a body.Q1: What events have already finished?A: searching trapped landslide said found Q2: What events have begun but has not finished?A: snow causing disruption rainfall bringing flooding Q3: What will happen in the future?A: No answers.Q4: What happened before a woman was trapped?A: landslide Q5: What had started before a woman was trapped?A: snow rainfall landslide Q6: What happened while a woman was trapped?A: searching Q7: What happened after a woman was trapped?A: searching said found Q8: What happened at about the same time as the snow?A: rainfall Q9: What happened after the snow started?A: causing disruption bringing flooding searching trapped landslide said found Q10: What happened before the snow started?A: No answers.warm

Existing temporal relation (TempRel) annotation schemes often have low interannotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition.This paper proposes a new multi-axis modeling to better capture the temporal structure of events.In addition, we identify that event end-points are a major source of confusion in annotation, so we also propose to annotate TempRels based on start-points only.A pilot expert annotation effort using the proposed scheme shows significant improvement in IAA from the conventional 60's to 80's (Cohen's Kappa).This better-defined annotation scheme further enables the use of crowdsourcing to alleviate the labor intensity for each annotator.We hope that this work can foster more interesting studies towards event understanding. 1

Understanding temporal and causal relations between events is a fundamental natural language understanding task.Because a cause must occur earlier than its effect, temporal and causal relations are closely related and one relation often dictates the value of the other.However, limited attention has been paid to studying these two relations jointly.This paper presents a joint inference framework for them using constrained conditional models (CCMs).Specifically, we formulate the joint problem as an integer linear programming (ILP) problem, enforcing constraints that are inherent in the nature of time and causality.We show that the joint inference framework results in statistically significant improvement in the extraction of both temporal and causal relations from text. 1

Qiang Ning, Hao Wu, Haoruo Peng, Dan Roth. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.