iSarcasm: A Dataset of Intended Sarcasm

We consider the distinction between intended and perceived sarcasm in the context of textual sarcasm detection. The former occurs when an utterance is sarcastic from the perspective of its author, while the latter occurs when the utterance is interpreted as sarcastic by the audience. We show the limitations of previous labelling methods in capturing intended sarcasm and introduce the iSarcasm dataset of tweets labeled for sarcasm directly by their authors. Examining the state-of-the-art sarcasm detection models on our dataset showed low performance compared to previously studied datasets, which indicates that these datasets might be biased or obvious and sarcasm could be a phenomenon under-studied computationally thus far. By providing the iSarcasm dataset, we aim to encourage future NLP research to develop methods for detecting sarcasm in text as intended by the authors of the text, not as labeled under assumptions that we demonstrate to be sub-optimal.Comment: 9 page

Computational sarcasm detection and understanding in online communication

The presence of sarcasm in online communication has motivated an increasing number of computational investigations of sarcasm across the scientific community. In this thesis, we build upon these investigations. Pointing out their limitations, we bring four contributions that span two research directions: sarcasm detection and sarcasm understanding. Sarcasm detection is the task of building computational models optimised for recognising sarcasm in a given text. These models are often built in a supervised learning paradigm, relying on datasets of texts labelled for sarcasm. We bring two contributions in this direction. First, we question the effectiveness of previous methods used to label texts for sarcasm. We argue that the labels they produce might not coincide with the sarcastic intention of the authors of the texts that they are labelling. In response, we suggest a new method, and we use it to build iSarcasm, a novel dataset of sarcastic and non-sarcastic tweets. We show that previous models achieve considerably lower performance on iSarcasm than on previous datasets, while human annotators achieve a considerably higher performance, compared to models, pointing out the need for more effective models. Therefore, as a second contribution, we organise a competition that invites the community to create such models. Sarcasm understanding is the task of explicating the phenomena that are subsumed under the umbrella of sarcasm through computational investigation. We bring two contributions in this direction. First, we conduct an alaysis into the socio-demographic ecology of sarcastic exchanges between human interlocutors. We find that the effectiveness of such exchanges is influenced by the socio-demographic similarity between the interlocutors, with factors such as English language nativeness, age, and gender, being particualry influential. We suggest that future social analysis tools should account for these factors. Second, we challenge the motivation of a recent endeavour of the community; mainly, that of augmenting dialogue systems with the ability to generate sarcastic responses. Through a series of social experiments, we provide guidelines for dialogue systems concerning the appropriateness of generating sarcastic responses, and the formulation of such responses. Through our work, we aim to encourage the community to consider computational investigations of sarcasm interdisciplinarily, at the intersection of natural language processing and computational social science

THE ROMANIAN EXTERNAL TRADE IN LIVE ANIMALS AND ANIMAL PRODUCTS

In terms of foreign trade, in Romania there were some major changes over the past 20 years. In this paper we have focused on the Romanian external trade. The products which have been taken into account were live animals and animal products. Thus, we have made an analyse on the Romanian imports and exports at the global level and at the European level. Focused on the animal products, on the global level, there were registered major differences during the first seven years in the analysed period. Breaking by branches, we have pointed out huge distinctions between imports and exports, where the balance of trade was completely negative. Meanwhile, to have a good view on the international trade there were made links, based on some indexes between imports, exports, GDP and investments

Exploring Author Context for Detecting Intended vs Perceived Sarcasm

We investigate the impact of using author context on textual sarcasm detection. We define author context as the embedded representation of their historical posts on Twitter and suggest neural models that extract these representations. We experiment with two tweet datasets, one labelled manually for sarcasm, and the other via tag-based distant supervision. We achieve state-of-the-art performance on the second dataset, but not on the one labelled manually, indicating a difference between intended sarcasm, captured by distant supervision, and perceived sarcasm, captured by manual labelling.Comment: 6 pages, 1 figure, ACL 202

Towards global flood mapping onboard low cost satellites with machine learning

Spaceborne Earth observation is a key technology for flood response, offering valuable information to decision makers on the ground. Very large constellations of small, nano satellites— ’CubeSats’ are a promising solution to reduce revisit time in disaster areas from days to hours. However, data transmission to ground receivers is limited by constraints on power and bandwidth of CubeSats. Onboard processing offers a solution to decrease the amount of data to transmit by reducing large sensor images to smaller data products. The ESA’s recent PhiSat-1 mission aims to facilitate the demonstration of this concept, providing the hardware capability to perform onboard processing by including a power-constrained machine learning accelerator and the software to run custom applications. This work demonstrates a flood segmentation algorithm that produces flood masks to be transmitted instead of the raw images, while running efficiently on the accelerator aboard the PhiSat-1. Our models are trained on WorldFloods: a newly compiled dataset of 119 globally verified flooding events from disaster response organizations, which we make available in a common format. We test the system on independent locations, demonstrating that it produces fast and accurate segmentation masks on the hardware accelerator, acting as a proof of concept for this approach