United States

This paper describes the second-placed system on the leaderboard of SemEval-2022 Task 8: Multilingual News Article Similarity. We propose an entity-enriched Siamese Transformer which computes news article similarity based on different sub-dimensions, such as the shared narrative, entities, location and time of the event discussed in the news article. Our system exploits a Siamese network architecture using a Transformer encoder to learn document-level representations for the purpose of capturing the narrative together with the auxiliary entity-based features extracted from the news articles. The intuition behind using all these features together is to capture the similarity between news articles at different granularity levels and to assess the extent to which different news outlets write about “the same events”. Our experimental results and detailed ablation study demonstrate the effectiveness and the validity of our proposed method.

NAACL 2022

GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity

cross-lingual document similarity

multilingual document similarity

multilingual news article similarity

text similarity

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

workshop paper

Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased complexity of Structured Sentiment Analysis. To predict structured sentiment tuples in languages other than English we swap BART for a multilingual mT5 and introduce a novel Output Length Regularization to mitigate overfitting to common target sequence lengths, thereby improving the performance of the model by up to 70%. We evaluate our approach on seven datasets in five languages including a zero shot crosslingual setting.

Point Me to Your Opinion, SenPoi

Sarcasm is a form of figurative language where the intended meaning of a sentence differs from its literal meaning. This poses a serious challenge to several Natural Language Processing (NLP) applications such as Sentiment Analysis, Opinion Mining, and Author Profiling. In this paper, we present our participating system to the intended sarcasm detection task in English and Arabic languages. Our system\footnote{The source code of our system is available at \url{https://github.com/AbdelkaderMH/iSarcasmEval}} consists of three deep learning-based models leveraging two existing pre-trained language models for Arabic and English. We have participated in all sub-tasks. Our official submissions achieve the best performance on sub-task A for Arabic language and rank second in sub-task B. For sub-task C, our system is ranked 7th and 11th on Arabic and English datasets, respectively.

CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic

Building real-world complex Named Entity Recognition (NER) systems is a challenging task. This is due to the complexity and ambiguity of named entities that appear in various contexts such as short input sentences, emerging entities, and complex entities. Besides, real-world queries are mostly malformed, as they can be code-mixed or multilingual, among other scenarios. In this paper, we introduce our submitted system to the Multilingual Complex Named Entity Recognition (MultiCoNER) shared task. We approach the complex NER for multilingual and code-mixed queries, by relying on the contextualized representation provided by the multilingual Transformer XLM-RoBERTa. In addition to the CRF-based token classification layer, we incorporate a span classification loss to recognize named entities spans. Furthermore, we use a self-training mechanism to generate weakly-annotated data from a large unlabeled dataset. Our proposed system is ranked 6th and 8th in the multilingual and code-mixed MultiCoNER's tracks respectively.

UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and
Code-Mixed Complex Named Entity Recognition via Pseudo Labels using
Multilingual Transformer

This Presentation describes the authors’ submission to the SemEval-2022 task 4: Patronizing and Condescending Language (PCL) Detection. The aim of the task is the detection and classification of PCL in an annotated dataset. The authors of this paper worked on two different models with finetuned hyperparameters focusing on number of epochs, training batch size, evaluation batch size, gradient accumulation steps and learning rate. The authors submitted one RoBERTa model and one DistilBERT model. Both systems performed better than the random and RoBERTA baseline given by the task organizers. The RoBERTA model finetuned by the authors performed better in both subtasks than the DistilBERT model.

Felix&Julia at SemEval-2022 Task 4
Patronizing and Condescending Language Detection

We present Transformer based pretrained models, which are fine-tuned for Named
Entity Recognition (NER) task. Our team participated in SemEval-2022 Task 11
MultiCoNER: Multilingual Complex Named Entity Recognition task for Hindi and Bangla. Result comparison of six models (mBERT, IndicBERT, MuRIL (Base), MuRIL (Large), XLM-RoBERTa (Base) and XLM-RoBERTa (Large) ) has been performed. It is found that among these models MuRIL (Large) model performs better for both the Hindi and Bangla languages. Its F1-Scores for Hindi and Bangla are 0.69 and 0.59 respectively.

silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for
Hindi and Bangla languages

This presentation describes the system used in SemEval-2022 Task 6: Intended Sarcasm Detection in English and Arabic. Achieving 20th, 3rd places with 34 & 47 F1-Sarcastic score for task A, 16th place for task B with 0.0560 F1-macro score, and 10, 6th places for task C with 72% and 80% accuracy on the leaderboard. A voting classifier between either multiple different BERT-based models or machine learning models is proposed, as our final model. Multiple key points have been extensively examined to overcome the problem of the unbalance of the dataset as the type of models, suitable architecture, augmentation, loss function, etc. In addition to that, we present an analysis of our results in this work, highlighting its strengths and shortcomings.

reamtchka at SemEval-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasets

This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification. The challenge organizers provide an English meme dataset to develop and train systems for identifying and classifying misogynous memes. More precisely, the competition is separated into two sub-tasks: sub-task A asks for a binary decision as to whether a meme expresses misogyny, while sub-task B is to classify misogynous memes into the potentially overlapping sub-categories of stereotype, shaming, objectification, and violence. 
For our submission, we implement a new model fusion network and employ an ensemble learning approach for better performance. With this structure, we achieve a 0.755 macro-average F1-score (11th) in sub-task A and a 0.709 weighted-average F1-score (10th) in sub-task B.

Ensemble learning for identifying misogynous MEMEs

We investigate the capabilities of pre-trained models without any fine-tuning for a document-level multilingual news similarity task of SemEval-2022. We utilize title and news content with appropriate pre-processing techniques. Our system derives 14 different similarity features using a combination of pre-trained \texttt{MPNet} with well-known statistical methods (i.e., TF-IDF, Word Mover’s distance). We formulate the multilingual news similarity task as a regression task and approximate the overall similarity between two news articles using these features. Our best-performing system achieved a correlation score of 70.1% and was ranked 20th among the 34 participating teams. In this paper, in addition to a system description, we also provide further analysis of our results and an ablation study highlighting the strengths and limitations of our features. We make our code publicly available at https://github.com/cicl-iscl/multinewssimilarity

OversampledML at SemEval-2022 Task 8: When multilingual news similarity met Zero-shot approaches

We present our contribution to the SemEval 22 Shared Task 8: Multilingual news article similarity. The approach is lightweight and language-agnostic, it is based on the computation of several lexicographic and embedding-based features, and the use of a simple ML approach: random forests. In a notable departure from the task formulation, which is a ranking task, we tackled this task as a classification one. We present a detailed analysis of the behaviour of our system under different settings.

Team TMA at SemEval-2022 Task 8:
Lightweight and Language-Agnostic News Similarity Classifier

This paper describes our system for SemEval-2022 Task 2 Multilingual Idiomaticity Detection and Sentence Embedding sub-task B. We modify a standard BERT sentence transformer by adding embeddings for each idiom, which are created using BERTRAM and a small number of contexts. We show that this technique increases the quality of idiom representations and leads to better performance on the task. We also perform analysis on our final results and show that the quality of the produced idiom embeddings is highly sensitive to the quality of the input contexts.

GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity

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GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity

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