Temporal Expression Extraction (TEE) is essential for understanding time in natural language. It has applications in Natural Language Processing (NLP) tasks such as question answering, information retrieval, and causal inference. To date, work in this area has mostly focused on English as there is a scarcity of labeled data for other languages. We propose XLTime, a novel framework for multilingual TEE. XLTime works on top of pre-trained language models and leverages multi-task learning to prompt cross-language knowledge transfer both from English and within the non-English languages. XLTime alleviates problems caused by a shortage of data in the target language. We apply XLTime with different language models and show that it outperforms the previous automatic SOTA methods on French, Spanish, Portuguese, and Basque, by large margins. XLTime also closes the gap considerably on the handcrafted HeidelTime method.

Social media has increasingly played a key role in emergency response: first responders can use public posts to better react to ongoing crisis events and deploy the necessary resources where they are most needed. Timeline extraction and abstractive summarization are critical technical tasks to leverage large numbers of social media posts about events. Unfortunately, there are few datasets for benchmarking technical approaches for those tasks. This paper presents , the largest dataset of local crisis event timelines available to date. contains 1,000 crisis event timelines across four domains: wildfires, local fires, traffic, and storms. We built using a semi-automated cluster-then-refine approach to collect data from the public Twitter stream. Our initial experiments indicate a significant gap between the performance of strong baselines compared to the human performance on both tasks.Our dataset, code, and models are publicly available (https://github.com/CrisisLTLSum/CrisisTimelines).

Social media is an essential tool to share information about crisis events, such as natural disasters. Event Detection aims at extracting information in the form of an event, but considers each event in isolation, without combining information across sentences or events. Many posts in Crisis NLP contain repetitive or complementary information which needs to be aggregated (e.g., the number of trapped people and their location) for disaster response. Although previous approaches in Crisis NLP aggregate information across posts, they only use shallow representations of the content (e.g., keywords), which cannot adequately represent the semantics of a crisis event and its sub-events. In this work, we propose a novel framework to extract critical sub-events from a large-scale crisis event by combining important information across relevant tweets. Our framework first converts all the tweets from a crisis event into a temporally-ordered set of graphs. Then it extracts sub-graphs that represent semantic relationships connecting verbs and nouns in 3 to 6 node sub-graphs. It does this by learning edge weights via Dynamic Graph Convolutional Networks (DGCNs) and extracting smaller, relevant sub-graphs. Our experiments show that our extracted structures (1) are semantically meaningful sub-events and (2) contain information important for the large crisis-event. Furthermore, we show that our approach significantly outperforms event detection baselines, highlighting the importance of aggregating information across tweets for our task.

The task of news article image captioning aims to generate descriptive and informative captions for news article images. Unlike conventional image captions that simply describe the content of the image in general terms, news image captions follow journalistic guidelines and rely heavily on named entities to describe the image content, often drawing context from the whole article they are associated with. In this work, we propose a new approach to this task, motivated by caption guidelines that journalists follow. Our approach, Journalistic Guidelines Aware News Image Captioning (JoGANIC), leverages the structure of captions to improve the generation quality and guide our representation design. Experimental results, including detailed ablation studies, on two large-scale publicly available datasets show that JoGANIC substantially outperforms state-of-the-art methods both on caption generation and named entity related metrics.

Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection. However, they often only leverage the edges (dependencies) between words, and discard the dependency labels (e.g., nominal-subject), treating the underlying graph edges as homogeneous. In this work, we propose a novel framework for incorporating both dependencies and their labels using a recently proposed technique called Graph Transformer Network (GTN). We integrate GTN to leverage dependency relations on two existing homogeneous-graph-based models and demonstrate an improvement in the F1 score on the ACE dataset.

The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.