Speech acts and Communicative Intentions for Urgency Detection (2022)

Abstract: Recognizing speech acts (SA) is crucial for capturing meaning beyond what is said, making communicative intentions particularly relevant to identify urgent messages. This paper attempts to measure for the first time the impact of SA on urgency detection during crises,006in tweets. We propose a new dataset annotated for both urgency and SA, and develop several deep learning architectures to inject SA into urgency detection while ensuring models generalisability. Our results show that taking speech acts into account in tweet analysis improves information type detection in an out-of-type configuration where models are evaluated in unseen event types during training. These results are encouraging and constitute a first step towards SA-aware disaster management in social media.

## introduction discovered by @xcite and extensively promoted by @xcite , speech acts (henceforth sa) have been the object of extensive discussion in the philosophical and the linguistic literature @xcite . according to the austinian initial view, sa are to achieve action rather than conveying information. when uttering i now pronounce you man and wife, the priest accomplishes the action of marrying rather than just stating a proposition. beyond these prototypical cases, the literature has quickly broaden the understanding of the notion of sa as a special type of linguistic object that encompasses questions, orders and assertions and transcends propositional content revealing communicative intentions on the part of the speaker @xcite @xcite . because recognizing speakers' intentions is crucial for capturing meaning beyond what is said @xcite , sa have given rise to an extensive body of work in the computational linguistics literature where various approaches have been proposed to detect them in both synchronous (e.g., meeting, phone) @xcite as well as asynchronous dialogues (e.g., emails, tweet threads) @xcite @xcite . sa have shown to be an important step in many down stream nlp applications such as strategic action prediction @xcite , dialogue summarization (goo and @xcite ) and conversational systems @xcite . in this paper, we attempt to measure for the first time the role of sa on urgency detection in tweets, focusing on natural disasters @xcite . sa are particularly relevant to identify urgent messages, i.e. those that raise situational awareness over a crisis (including human/material damages, security instructions, etc.), providing therefore actionable information that will help to set priorities for the human teams and decide appropriate rescue actions. by tweeting, speakers seek to achieve impact via enhancing a chain of reactions. they do not necessarily seek to merely express themselves. the greater the number of re-tweets and replies the greater the impact. therefore, tweets are not only public, but they are also interactive. they mostly aim to make interlocutors react (perlocutionary level) by different linguistic means (illocutionary level), in view of achieving a purpose (on perlocutionary / illocutionary, see @xcite ). we illustrate this in the following examples 1 where speaking subjects perform qualitatively very different language acts depending on the situation they find themselves in. in the tweet (1a), the writer publicly expresses an explicit commitment to provide help after the irma hurricane tragedy, using an explicit action verb ("to help") which is under the scope of an explicit attitude verb ("want"), thus aiming to obtain a reply on what to do to provide help. (1b) on the other hand ex-presses an intention to complain about the absence of assistance without using any explicit intent keywords and thus raise awareness and attention on the part of the people in charge of assistance. when annotating tweets posted during a crisis (like earthquakes, bombing, attacks) according to different taxonomies of sa, state of the art corpusbased studies observe a majority of statements, essentially supplemented by suggestions and comments -in contrast, the topics dealing with e.g. celebrities are essentially made up of comments @xcite @xcite . these results have however been obtained after manual annotations, the focus being rather on sa classification of topic oriented tweets. the next step now is to show to what extent these observations are still valid from a computational point of view. our contribution is threefold and consists in: 1. a new dataset of 6,669 tweets in french annotated for both urgency and sa for disaster events of various types that occurred in france; foot_1 2. a set of deep learning experiments to inject sa information into urgency detection using monotask and multitask architectures. we investigate the role of communicative intentions in three classification settings: relatedness (i.e., useful vs. non useful for emergency responders), urgency detection (i.e., non useful vs. urgent vs. non urgent), and information type following a predefined taxonomy of six actionable categories; this paper is organized as follows. we first provide related work on nlp-based approaches to crisis management as well as sa in social media. we then describe our data, the annotation procedure and the results of the annotation campaign. we detail the experiments we carried out on injecting sa in urgency detection in section 4 and discuss our results in section 5. we end the paper by some perspectives for future work. ## dataset since our focus is on crises that occur in metropolitan france and its overseas departments, we rely on the only available corpus of french tweets by @xcite foot_3 composed of about 12k tweets collected using dedicated keywords about ecological crises that occurred in france from 2016 to 2019 and posted 24h before, during (48h) and 72h after the crisis: 2 floods that occurred in aude and corsica regions, 10 storms-béryl, berguitta, fionn, eleanor, bruno, egon, ulrika, susanna, fakir and ana, and 2 hurricanes-irma and harvey, and 1 sudden crisis (marseille building collapse). it is important to note that in this dataset, some crises occurred in the same time period which implies that some messages that were scraped for some crises actually belonged to other (they were annotated as not useful in this case, as they are not related to the targeted crisis, see below). ## speech acts for urgency detection we propose several models to automatically classify a tweet according to its relatedness (binary classification-rel), urgency (three classes-urg) and information type categories (multiclass-inf) while injecting sa information into the learning process. our models have been compared to saagnostic baselines while analyzing the impact of sa on generalization to new disaster events which is important for this application, since disasters can vary widely with respect to both their specific properties as well as their types. although sa detection is an important preliminary step, this is however out of the scope of this paper. note that a baseline camembert model @xcite fine-tuned to predict the five sa tags achieves a macro f-score of 0.686 with a precision of 0.690 and recall of 0.701. improvement of these results is left for future work. ## conclusion this paper newly addresses the role of speech acts in urgency detection in tweets. in particular, we propose a dataset of french tweets about urgent situations and create models that utilize speech acts to classify the tweets. we also analyze the generalization of the models over new urgent events. our results are encouraging and demonstrate that sa improve urgency detection. this is more salient for out-of-type evaluation setting, where the sa-aware approach has shown to have a good generalisation power in fine-grained classification. this work could be very useful to government workers who need to respond to natural disasters and to decide how to deploy possibly limited resources. as future work, we plan to explore a finer-grained sa taxonomy on urgency detection.