Domain Adaptation for A rabic Crisis Response (2022)

Abstract: Deep learning algorithms can identify related tweets to reduce the information overload that prevents humanitarian organisations from using valuable Twitter posts. However, they rely heavily on human-labelled data, which are unavailable for emerging crises. Because each crisis has its own features, such as location, time and social media response, current models are known to suffer from generalising to unseen disaster events when pre-trained on past ones. Tweet classifiers for low-resource languages like Arabic has the additional issue of limited labelled data duplicates caused by the absence of good language resources. Thus, we propose a novel domain adaptation approach that employs distant supervision to automatically label tweets from emerging Arabic crisis events to be used to train a model along with available human-labelled data. We evaluate our work on data from seven 2018–2020 Arabic events from different crisis types (flood, explosion, virus and storm). Results show that our method outperforms self-training in identifying crisis-related tweets in real-time scenarios and can be seen as a robust Arabic tweet classifier.

## introduction arabic represents the world's fifth most spoken language and arabic language users are the fastestgrowing language group on the web @xcite . in february 2011, protestors in egypt used twitter as their main communication platform @xcite . this emphasises that twitter is an important and rich source of real-time and useful information during crises in arabic countries. people share their statuses and post information about injured or dead people and infrastructural damage @xcite . they also tweet to ask for help or to offer help to others. although humanitarian organisations could use these information to significantly improve crisis response with regard to reducing human and financial losses, they do not due to the information overload issue @xcite . to solve this problem, deep learning algorithms have been utilised to identify arabic tweets from unseen crises to support disaster management and enhance situational awareness in the middle east @xcite . however, they did not consider the domain-shift between source and target tweets posted during these events, which prevents the models from reaching a good generalisation level. as a result, semi-supervised approaches that automatically generate new labelled training data from an unlabelled corpus to reduce the gaps between the two domains are desirable. distant supervision has been applied to automatically generate new labelled training data for event extraction task @xcite . moreover, semi-supervised domain adaptation techniques have been successfully adopted to incorporate unlabelled target data to labelled source data to reduce the domain-shift between the two domains. our work here is motivated by the success of applying distant supervision and domain adaptation methods to high-resource english-language tweets presented in our previous works @xcite . however and unlike english, arabic is considered a low-resource language, with several notable issues highlighted in the crisis literature. first is the lack of labelled arabic tweets for crisis response @xcite . second, the lack of good supporting resources for arabic, such as external knowledge bases or language dictionaries @xcite . finally, arabic tweets are informal and regional in nature, and arabic regions have unique dialects which differ in syntax, phonology and morphology @xcite . in this paper, we propose an adaptive domain adaptation method from our previous work for english crisis response in @xcite to overcome all these challenges for arabic crisis response. our work, here, aims at minimising the domain shift between the target and the source arabic tweets. we use a distant supervisionbased framework to label the unlabelled target data (pseudo-labelling), whereby an initial keyword list is established using clusters from past events. the most related keywords are then selected using a statistical method. the selected keyword list is then expanded by employing distant supervision via an external source (almaany 1 ), and those tweets with a bigram of keywords are labelled as positive tweets, while tweets with none of the keywords are labelled as negative tweets. the generated labelled data is then mixed with the available source data to train a new target model. unlike self-training in @xcite , our method does not replicate the label noise that exists in the current dataset. in addition, crisis data that cannot be detected using existing keyword alert systems, as in @xcite , will be detected by our method because of the new crisis keywords derived from almaany. to the best of our knowledge, this is the first attempt to use distant supervision under the umbrella of domain adaptation techniques to classify unseen crisis-related arabic data from current events. the experimental results show that the proposed method can be seen as a robust approach to classifying unseen arabic tweets from an emerging event regardless of the crisis types used to create the keyword list. furthermore, it extends our framework's abilities from our prior work to automatically label data from low-resource languages with limited capabilities. ## proposed method the method consists of two stages as described in algorithm 1. ## experiments to determine the effectiveness of using pseudolabelled target data generated by our framework in domain adaptation settings, we compare two labelling with three adaptation methods. to automatically give labels to the unlabelled target data we apply distant supervision (ds) -using our distant supervision-based framework; and self-labelling (selfl; @xcite ) -using a pre-trained model on mls. to incorporate target labelled data, we use three adaptation methods: target model (tm) -building a model following the source architecture as described in the above section; finetuning (ft)modifying all the weights of the pre-trained model using the pseudo-labelled or self-labelled target data; and feature extraction (fx) -treating the pre-trained model as a feature extractor. here, we only train a linear classifier using pseudo-labelled or self-labelled data on the top of the extracted features. as a result, we compare 8 classifiers on 14 settings (keyword sets from the same or different crisis type of the target event -both from kawarith, as shown in we follow @xcite in cleaning arabic input tweets. we substitute hyperlinks with the arabic word " ", which means http address or url. similarly, we replace user mentions with " ", hashtags with " ", and numbers with " ". four types of letter normalizations are performed: (1) " ", the different forms of alef are normalized to " "; (2) " ", forms of elaf maqsora, to " ", (3) " ", a form of waw, to " "; and (4) ta marboutah " " to " ".we also eliminate stop words, special characters, punctuation, twitter-specific words such as "rt", elongation, emojis, non-arabic characters, diacritics and short vowels. we use convbilstm @xcite as the tweet classifier which contains two sub-models: the cnn model for feature setting keyword set target set s1 explosion cairo bombing s2 explosion beirut explosion s3 floods jordan floods s4 floods kuwait floods s5 floods hafer-albatin floods s6 floods covid'19 s7 explosion covid'19 s8 floods dragon storm s9 explosion dragon storm s10 floods cairo bombing s11 floods beurit explosion s12 explosion jordan floods s13 explosion kuwait floods s14 explosion hafer-albatin floods ## results and discussion results from the first column in ## conclusion we introduced a domain adaptation method to automatically label arabic tweets from emerging disasters. our goal is to overcome the issues of lowresource languages in applying solutions to domain shifts between source and target data. we use clusters instead of manually labelled tweets along with almaany to extend the initial keyword list. results showed that our method always improves the model performance (average of 3.7% absolute gain in f1 score) if the keyword sets share the crisis type of the target events.we also ran experiments to use keyword sets from different crisis types to the target incident. as a result, we found out that our framework can classify unseen tweets from a given disaster using a keyword set from different disasters and ds-tm always improves model performance (average of 5.5% absolute gain in f1 score). to this end, we can say that that ds-tm represents robust models to classify tweets from emerging events for languages with limited resources. it also expands our approach's ability to use corpora from other crisis types of the target data to create keyword sets that suit the situation of arabic tweets. we hope that leveraging automatically labelled data will accelerate the current research on classifying arabic tweets in crisis response. in the future, we want to extend our method to other low-resource languages like spanish. we also believe that tweets share features with ill-formed texts, which points to the potential of our method to identify specific events, behaviors or feelings expressed on other communication platforms.