United States

Misogynistic memes are rampant on social media, and often convey their messages 
using multimodal signals (e.g., images paired with derogatory text or captions). However, to date very few multimodal systems have been leveraged for the detection of misogynistic memes. Recently, researchers have turned to contrastive learning solutions for a variety of problems. Most notably, OpenAI’s CLIP model has served as an innovative solution for a variety of multimodal tasks. In this work, we experiment with contrastive learning to address the detection of misogynistic memes within the context of SemEval2022 Task 5. Although our model does not achieve top results, these experiments provide important exploratory findings for 
this task. We conduct a detailed error analysis, revealing promising clues and offering a foundation for follow-up work.

NAACL 2022

Exploring Contrastive Learning for Multimodal Detection of
Misogynistic Memes

multimodal system

clip

contrastive learning

computer vision

natural language processing

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

workshop paper

## 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

This presentation introduces a system to solve the SemEval 2022 Task 8: Multilingual News Article Similarity. The task focuses on the consistency of events reported in two news articles. The system consists of a pre-trained model to extract multilingual news features, following fully-connected networks to measure the similarity. In addition, data augmentation and Ten Folds Voting are used to enhance the model.
Our final submitted model is an ensemble of three base models, with a Pearson value of 0.784 on the test dataset.

ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data
Augmentation and Voting for Multilingual News Similarity


The presentation describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language (PCL) Detection. For sub-task 1, where the objective is to classify a text as PCL or non-PCL, we use a T5 Model fine-tuned on the dataset. For sub-task 2, which is a multi-label classification problem, we use a RoBERTa model fine-tuned on the dataset. Given that the key challenge in this task is classification on an imbalanced dataset, our models rely on an augmented dataset that we generate using paraphrasing. We found that these two models yield the best results out of all the other approaches we tried.

Tesla at SemEval-2022 Task 4: Patronizing and Condescending Language
Detection using Transformer-based Models with Data Augmentation

Structured Sentiment Analysis (SSA) deals with extracting opinion tuples in a text, where each tuple (h, e, t, p) consists of h, the holder, who expresses a sentiment polarity p towards a target t through a sentiment expression e. While prior works explore graph-based or sequence labeling-based approaches for the task, we in this paper present a novel unified generative method to solve SSA, a SemEval-2022 shared task. We leverage a BART-based encoder-decoder architecture and suitably modify it to generate, given a sentence, a sequence of opinion tuples. Each generated tuple consists of seven integers respectively representing the indices corresponding to the start and end positions of the holder, target, and expression spans, followed by the sentiment polarity class associated between the target and the sentiment expression. We perform rigorous experiments for both Monolingual and Cross-lingual subtasks, and achieve competitive Sentiment F1 scores on the leaderboard in both settings

ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative Approach

We propose a pair of deep learning systems;
To efficiently learn representations from dictionary definitions (a.k.a. the reverse dictionary subtask) using a transformer encoder with a multitask approach and a contrastive loss.
To generate dictionary definitions given representations (a.k.a. the definition modeling subtask) using a transformer decoder by modifying cross attention.
Our systems achieved competitive performance on the RevDict subtask, and first place on the DefMod subtask for the English language.

Using Transformers to Learn Dictionaries and Representations


Patronizing and condescending language (PCL) can find its way into many mediums of public discourse. Presence of PCL in text can produce negative effects in the society. The challenge presented by the task emerges from the subtleties of PCL and various data dependent constraints. Hence, developing techniques to detect PCL in text, before it is propagated is vital. The aim of this paper is twofold, a) to present systems that can be used to classify a text as containing PCL or not, and b) to present systems that assign the different categories of PCL present in text. The proposed systems are primarily rooted in transformer-based pre- trained language models. Among the models submitted for Subtask 1, the best F1-Score of 0.5436 was achieved by a deep learning based ensemble model. This system secured the rank 29 in the official task ranking. For Subtask 2, the best macro-average F1-Score of 0.339 was achieved by an ensemble model combining transformer-based neural architecture with gradient boosting label-balanced classifiers. This system secured the rank 21 in the official task ranking. Among subsequently carried out experiments a variation in architecture of a system for Subtask 2 achieved a macro-average F1-Score of 0.3527.

Binary and Multi-label Classification of PCL using Fine-tuned Transformer-based Models

This paper describes INF-UFRGS submission for SemEval-2022 Task 5 Multimodal Automatic Misogyny Identification (MAMI). Unprecedented levels of harassment came with the ever-growing internet usage as a mean of worldwide communication. The goal of the task is to improve the quality of existing methods for misogyny identification, many of which require dedicated personnel, hence the need for automation. We experimented with five existing models, including ViLBERT and Visual BERT - both uni and multimodally pretrained - and MMBT. The datasets consist of memes with captions in English. The results show that all models achieved Macro-F1 scores above 0.64. ViLBERT was the best performer with a score of 0.698.

INF-UFRGS at SemEval-2022 Task 5: analyzing the performance of multimodal models

In this presentation, we are going to talk about the work we've done in task3, PreTENS, which is aimed at evaluating the acceptability of simple sentences containing constructions whose two arguments are presupposed to be or not to be in an ordered taxonomic relation.

Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M Model

In the SubTaskA, we use InfoXLM as text encoder and exponential moving average (EMA) method and the adversarial attack strategy.

In the SubTaskB, we add an cross-attention module, contrastive objective and employ a momentum contrast.

Additionally, we use the alignment and uniformity properties to measure the quality of sentence embeddings.

ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations

Kyle (1985) proposes two types of rumors: in- formed rumors that are based on some private information and uninformed rumors that are not based on any information (i.e. bluffing). Also, prior studies find that when people have credible source of information, they are likely to use a more confident textual tone in their spreading of rumors. Motivated by these theoretical findings, we propose a double-channel structure to determine the ex-ante veracity of rumors on social media. Our ultimate goal is to classify each rumor into true, false, or unverifiable category. We first assign each text into either certain (informed rumor) or uncertain (uninformed rumor) category. Then, we apply lie detection algorithm to informed rumors and thread-reply agreement detection algorithm to uninformed rumors. Using the dataset of SemEval 2019 Task 7, which requires ex-ante threefold classification (true, false, or unverifiable) of social media rumors, our model yields a macro-F1 score of 0.4027, outperforming all the baseline models and the second-place winner (Gorrell et al., 2019). Furthermore, we empirically validate that the double-channel structure outperforms single-channel structures which use either lie detection or agreement de- tection algorithm to all posts.

Detecting Rumor Veracity with Only Textual Information by Double-Channel Structure

The last few years have witnessed an exponential rise in the propagation of offensive text on social media. Identification of this text with high precision is crucial for the well-being of society. Most of the existing approaches tend to give high toxicity scores to innocuous statements (e.g., “I am a gay man”). These false positives result from over-generalization on the training data where specific terms in the statement may have been used in a pejorative sense (e.g., “gay”). Emphasis on such words alone can lead to discrimination against the classes these systems are designed to protect. In this paper, we address the problem of offensive language detection on Twitter, while also detecting the type and the target of the offence. We propose a novel approach called SyLSTM, which integrates syntactic features in the form of the dependency parse tree of a sentence and semantic features in the form of word embeddings into a deep learning architecture using a Graph Convolutional Network. Results show that the proposed approach significantly outperforms the state-of-the-art BERT model with orders of magnitude fewer number of parameters.

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ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News Similarity

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