United States

Task 10 in SemEval 2022 is a composite task which entails analysis of opinion tuples, and recognition and demarcation of their nature. In this paper, we will elaborate on how such a methodology is implemented, how it is undertaken for a Structured Sentiment Analysis, and the results obtained thereof. To achieve this objective, we have adopted a bi-layered BiLSTM approach. In our research, a variation on the norm has been effected towards enhancement of accuracy, by basing the categorization meted out to an individual member as a by-product of its adjacent members, using specialized algorithms to ensure the veracity of the output, which has been modelled to be the holistically most accurate label for the entire sequence.Such a strategy is superior in terms of its parsing accuracy and requires less time. This manner of action has yielded an SF1 of 0.33 in the highest-performing configuration.

NAACL 2022

SSN_MLRG1 at SemEval-2022 Task 10: Structured Sentiment Analysis using 2-layer BiLSTM

sequence labelling

bert

## 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

workshop paper

Idioms are lexically-complex phrases whose meaning cannot be derived by compositionally interpreting their components. Although the automatic identification and understanding of idioms is essential for a wide range of Natural Language Understanding tasks, they are still largely under-investigated.
This motivated the organization of the SemEval-2022 Task 2, which is divided into two multilingual subtasks: one about idiomaticity detection, and the other about sentence embeddings. In this work, we focus on the first subtask and propose a Transformer-based dual-encoder architecture to compute the semantic similarity between a potentially-idiomatic expression and its context and, based on this, predict idiomaticity. Then, we show how and to what extent Named Entity Recognition can be exploited to reduce the degree of confusion of idiom identification systems and, therefore, improve performance.
Our model achieves 92.1 F1 in the one-shot setting and shows strong robustness towards unseen idioms achieving 77.4 F1 in the zero-shot setting. We release our code at https://github.com/Babelscape/ner4id.

NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection

We present our strategy to address the SemEval-2022 Task 3 PreTENS
The goal of the task is to identify if a sentence is deemed acceptable or not, depending
on the taxonomic relationship that holds between a noun pair contained in the
sentence.
For sub-task 1—binary classification—we pro- pose an effective way to enhance the
robustness and the generalizability of language models for better classification on this
downstream task. We design a two-stage fine-tuning pro- procedure on the ELECTRA
language model using data augmentation techniques. Rigorous experiments are
carried out using multi-task learning and data-enriched fine-tuning. Experimental
results demonstrate that our proposed model, UU-Tax, is indeed able to generalize well
for our downstream task.
For sub-task 2— regression—we propose a simple classifier that trains on features
obtained from Universal Sentence Encoder (USE). In addition to describing the
submitted systems, we discuss other experiments that employ pre-trained language
models and data augmentation techniques. For both sub-tasks, we perform error
analysis to further understand the behavior of the proposed models. We achieved a
global F1Binary score of 91.25% in sub-task 1 and a rho score of 0.221 in sub-task 2

UU-Tax at SemEval-2022 Task 3: Improving the generalizability of
language models for taxonomy classification through data augmentation

The subtle and typically unconscious use of patronizing and condescending language (PCL) in large-audience media outlets undesirably feeds stereotypes and strengthens power-knowledge relationships, perpetuating discrimination towards vulnerable communities. Due to its subjective and subtle nature, PCL detection is an open and challenging problem, both for computational methods and human annotators. In this paper we describe the systems submitted by the DH-FBK team to SemEval-2022 Task 4, aiming at detecting PCL towards vulnerable communities in English media texts. Motivated by the subjectivity of human interpretation, we propose to leverage annotators' uncertainty and disagreement to better capture the shades of PCL in a multi-task, multi-view learning framework. Our approach achieves competitive results, largely outperforming baselines and ranking on the top-left side of the leaderboard on both PCL identification and classification. Noticeably, our approach does not rely on any external data or model ensemble, making it a viable and attractive solution for real-world use.

DH-FBK at SemEval-2022 Task 4: Leveraging Annotators' Disagreement and Multiple Data Views for Patronizing Language Detection

This paper describes an approach to detect idiomaticity only from the contextualized representation of a MWE over mutltilingual pretrained language models.
Our experiments find that larger models are usually more effective in idiomaticity detection.
However, using a higher layer of the model may not guarantee a better performance.
In multilingual scenario, the convergence of different languages are not consistent and rich-resource languages have big advantages over other languages.

HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models

In this paper we propose four deep learning models for the task of detecting and classifying Patronizing and Condescending Language (PCL) using a corpus of over 13,000 annotated paragraphs in English. The task, hosted at SemEval-2022, consists of two different subtasks. The Subtask 1 is a binary classification problem. Namely, given a paragraph, a system must predict whether or not it contains any form of PCL. The Subtask 2 is a multi-label classification task. Given a paragraph, a system must identify which PCL categories express the condescension. A paragraph might contain one or more categories of PCL. To face with the first subtask we propose a multi-channel Convolutional Neural Network (CNN) and an Hybrid LSTM. Using the multi-channel CNN we explore the impact of parallel word emebeddings and convolutional layers involving different kernel sizes. With Hybrid LSTM we focus on extracting features in advance, thanks to a convolutional layer followed by two bidirectional LSTM layers. For the second subtask a Transformer BERT-based model (i.e. DistilBERT) and an XLNet-based model are proposed. The multi-channel CNN model is able to reach an F1 score of 0.2928, the Hybrid LSTM model
is able to reach an F1 score of 0.2815, the DistilBERT-based one an average F1 of 0.2165 and the XLNet an average F1 of 0.2296. 
In this paper, in addition to system descriptions, we also provide further analysis of the results, highlighting strengths and limitations.


McRock at SemEval-2022: PCL Detection using Multi-Channel CNN, Hybrid LSTM, DistilBERT and XLNet

In this presentation, we design the model that has the ability of semantic understanding through the MLM task with contrastive learning, which is randomly initialized with mDeBERTa model. After that, we report the performance of our model in this shared task, and obtain the best performance in English and Russian tasks, which proves that our method is effective.


LingJing at SemEval-2022 Task 1: Multi-task Self-supervised Pre-training for Multilingual Reverse Dictionary

This paper describes the system developed by the USTC-NELSLIP team for SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition (MultiCoNER). We propose a gazetteer-adapted integration network (GAIN) to improve the performance of language models for recognizing complex named entities. The method first adapts the representations of gazetteer networks to those of language models by minimizing the KL divergence between them. After adaptation, these two networks are then integrated for backend supervised named entity recognition (NER) training. The proposed method is applied to several state-of-the-art Transformer-based NER models with a gazetteer built from Wikidata, and shows great generalization ability across them. The final predictions are derived from an ensemble of these trained models. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on three tracks (Chinese, Code-mixed and Bangla) and 2nd on the other ten tracks in this task.

USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition

In this presentation, we describe our system to the Semeval-22 task3 PreTENS. Based on the pre-training DeBERTa-v3, we carry out a simple and effective classification method in sub-task 1 and apply the method of knowledge transferring to sub-task 2. The proposed systems have won first place on both two sub-tasks. The experimental results show that our proposed method has better performance than other methods. In addition, we also conducted a number of comparative experiments to further explore the difficulties of the PreTENS task.

Applying DeBERTa to Lexical-level Presupposed Relation Taxonomy with Knowledge Transfer

The act of appearing kind or helpful via the use of but having a feeling of superiority condescending and patronizing language can have have serious mental health implications to those that experience it. Thus, detecting this condescending and patronizing language online can be useful for online moderation systems. Thus, in this manuscript, we describe the system developed by Team UTSA SemEval-2022 Task 4, Detecting Patronizing and Condescending Language. Our approach explores the use of several deep learning architectures including RoBERTa, convolutions neural networks, and Bidirectional Long Short-Term Memory Networks. Furthermore, we explore simple and effective methods to create ensembles of neural network models. Overall, we experimented with several ensemble models and found that the a simple combination of five RoBERTa models achieved an F-score of .6441 on the development dataset and .5745 on the final test dataset. Finally, we also performed a comprehensive error analysis to better understand the limitations of the model and provide ideas for further research.

UTSA NLP at SemEval-2022 Task 4: An Exploration of Simple Ensembles of Transformers, Convolutional, and Recurrent Neural Networks

Detecting MEME images to be misogynous or not is an application useful on curbing
online hateful information against women. In the SemEval-2022 Multimedia Automatic Misogyny Identification (MAMI) challenge, we designed a system using two simple but effective principles. First, we leverage on recently emerging Transformer models pretrained (mostly in a self-supervised learning way) on massive data sets to obtain very effective visual (V) and language (L) features. In particular, we used the CLIP (Radford et al., 2021) model provided by OpenAI to obtain coherent V and L features and then simply used a logistic regression model to make binary predictions. Second, we emphasized more on data rather than tweaking models by following the data-centric AI principle. These principles were proven to be useful and our final macro-F1 is 0.778 for the MAMI task A and ranked the third place among participant teams.

RIT Boston at SemEval-2022 Task 5: Multimedia Misogyny Detection By
Using Coherent Visual and Language Features from CLIP Model and
Data-centric AI Principle

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Next from NAACL 2022

NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection

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