United States

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.

NAACL 2022

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

distilbert

textclassification

roberta

## 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

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

workshop paper

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.

drsphelps at SemEval-2022 Task 2: Learning idiom representations using BERTRAM

Previous work on multi-task learning in Natural Language Processing (NLP) often
incorporated carefully selected tasks as well as carefully tuning of
architectures to share information across tasks. Recently, it has shown that
for autoregressive language models, a multi-task second pre-training step on a
wide variety of NLP tasks leads to a set of parameters that more easily adapt
for other NLP tasks. In this paper, we examine whether a similar setup can be
used in autoencoder language models using a restricted set of semantically
oriented NLP tasks, namely all SemEval 2022 tasks that are annotated at the
word, sentence or paragraph level. We first evaluate a multi-task model trained
on all SemEval 2022 tasks that contain annotation on the word, sentence or
paragraph level (7 tasks, 11 sub-tasks), and then evaluate whether
re-finetuning the resulting model for each task specificially leads to further
improvements. Our results show that our mono-task baseline, our multi-task
model and our re-finetuned multi-task model each outperform the other models
for a subset of the tasks. Overall, huge gains can be observed by doing
multi-task learning: for three tasks we observe an error reduction of more than
40\%.


MaChAmp at SemEval-2022 Tasks 2, 3, 4, 6, 10, 11, and 12: Multi-task Multi-lingual Learning for a Pre-selected Set of Semantic Datasets

Large Language Models have been successful in capturing compositionality of the text representations. In spite of their great success, they fail to capture true meaning of idiomatic multi-word expressions (MWEs). In this paper, we focus on the detection of idiomatic expressions. We use a dataset consisting of the literal and idiomatic usage of MWEs in English and Portuguese. Thereafter, we perform a binary classification in two different settings: zero shot and one shot, to determine if a given sentence contains an idiom or not. N shot classification for this task is defined by N number of common idioms between the training and testing sets. In this paper, we train multiple Large Language Models in both the settings and achieve an F1 score (macro) of 0.73 for the zero shot setting and an F1 score (macro) of 0.85 for the one shot setting. An implementation of our work can be found at https://github.com/ashwinpathak20/Idiomaticity_Detection_Using_Few_Shot_Learning.

It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning

We present our submission to SemEval 2022 Task 5 on Multimedia Automatic Misogyny Identification. We address the two tasks: Task A consists of identifying whether a meme is misogynous. If so, Task B attempts to identify its kind among shaming, stereotyping, objectification, and violence. Our approach combines a BERT Transformer with CLIP for the textual and visual representations. Both textual
and visual encoders are fused in an early-fusion fashion through a Multimodal Bidirectional Transformer with unimodally pretrained components. Our official submissions obtain macro-averaged F1=0.727 in Task A (4th position out of 69 participants) and weighted F1=0.710 in Task B (4th position out of 42 participants).

UniBO at SemEval-2022 Task 5: A Multimodal bi-Transformer Approach to the Binary and Fine-grained Identification of Misogyny in Memes

We describe SemEval-2022 Task 7, a shared task on rating the plausibility of clarifications in instructional texts. The dataset for this task consists of manually clarified how-to guides for which we generated alternative clarifications and collected human plausibility judgements. The task of participating systems was to automatically determine the plausibility of a clarification in the respective context. In total, 21 participants took part in this task, with the best system achieving an accuracy of 68.9%. This report summarizes the results and findings from 8 teams and their system descriptions. Finally, we show in an additional evaluation that predictions by the top participating team make it possible to identify contexts with multiple plausible clarifications with an accuracy of 75.2%.

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Next from NAACL 2022

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

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