IIIT - MLNS at S em E val-2022 Task 8: S iamese Architecture for Modeling Multilingual News Similarity (2022)

Abstract: The task of multilingual news article similarity entails determining the degree of similarity of a given pair of news articles in a language-agnostic setting. This task aims to determine the extent to which the articles deal with the entities and events in question without much consideration of the subjective aspects of the discourse. Considering the superior representations being given by these models as validated on other tasks in NLP across an array of high and low-resource languages and this task not having any restricted set of languages to focus on, we adopted using the encoder representations from these models as our choice throughout our experiments. For modeling the similarity task by using the representations given by these models, a Siamese architecture was used as the underlying architecture. In experimentation, we investigated on several fronts including features passed to the encoder model, data augmentation and ensembling among our major experiments. We found data augmentation to be the most effective working strategy among our experiments.

## introduction news articles from the web covering the same event tend to differ on regional and political biases, style of writing, conciseness and preciseness of coverage and the intended audience of the news outlet. misleading and confusing articles might lead to unnecessary confusion, chaos and tensions potentially exacerbating or even creating non-existent issues. often, news articles covering the same event in different languages from varied regional sources are readily available. this can apply for happenings of local, regional as well as international significance. in such a scenario, it is pertinent to able to identify or cluster together news articles covering * these authors contributed equally to this work. the same story in different languages and different view points while also being able to distinguish between articles similar in style but covering different happenings. the significance of the problem of multilingual news article similarity @xcite lies towards the applicability in this area by scoring a pair of news articles based on their similarity in coverage of the event without focusing on the subjectivity of the content. in our approach, we model the task as a regression problem by making use of a siamese neural network @xcite as the base architecture. we perform experiments in feature engineering by making use of metadata such as title, description, keywords and tags in addition to the news text. we also experimented with artificially augmenting the data by document permutation. also, we tried with ensembling of two models -one trained using only textual features, the other using metadata information. while data augmentation strategy did give a decent improvement in performance, considering the length of the news articles and the coverage of a variety of entities or events throughout the article, globally modeling the text representation might not be the best way to model for this task. in the subsequent sections, we elaborate on our experiments performed and the results obtained and analyze the same. we have open-sourced our code foot_0 for ease of replicability and improvement over our techniques. ## task definition the task of multilingual news similarity consists of predicting the similarity score between two news articles on a scale of 1 to 4, with a higher score indicating a higher degree of similarity. the news articles may belong to either the same or different lang #train ar_ar 274 de_de 857 de_en 577 en_en 1800 es_es 570 fr_fr 72 pl_pl 349 tr_tr 465 statistics of the data created for the task are shown in ## system description figure 1 shows the architecture we adopted for modeling the task. in the siamese architecture @xcite , an encoder representation is taken for each of the news articles following which a linear layer is used for reducing the dimensionality of the representation. an aggregation of these representations is then performed for having a unified representation of the two articles before passing them through fully connected layers that finally output the similarity score from 1 to 4. the entire architecture was trained end-to-end by minimizing the mean squared error (mse) loss between the actual scores and model predictions. we experimented with various strategies in this generalized architecture which we elaborate in the remainder ## conclusion we presented our base architecture adopted for the task of multilingual news article similarity and explained the various experiments performed on the same. an analysis of the results gave insights on what strategies worked best on our underlying siamese architecture, of which we determined data augmentation to be the most effective one. finally, we provided some insights on the modeling efficiency of the architecture adopted along with directions for possible improvement. this problem being very relevant in the face of diverse, multilingual news content being continually generated from diverse sources, improvements achieved in this task would be of value in industry and social benefit.