United States

Sentiment analysis is a useful problem which could serve a variety of fields from business intelligence to social studies and even health studies. Using SemEval 2022 Task 10 formulation of this problem and taking sequence labeling as our approach, we propose a model which learns the task by fine-tuning a pretrained transformer, introducing as few parameters (~150k) as possible and making use of precomputed attention values in the transformer. Our model improves shared task baselines on all task datasets.

NAACL 2022

Making Use of Pre-Trained Model’s Attention Values in Structured Sentiment Analysis

attention

sentiment analysis

sequence labeling

pre-trained models

## 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

Multiword expressions (MWEs) or idiomaticity are common phenomenon in natural languages. Current pre-trained language models cannot effectively capture the meaning of these MWEs. The reason is that two normal words, after combining together, could have an abruptly different meaning than the compositionality of the meanings of each word, whereas pre-trained language models reply on words compositionality. We proposed an improved method of adding an LSTM layer to the BERT model in order to get better results on a text classification task (Subtask A). Our result is slightly better than the baseline. We also tried adding TextCNN to BERT and adding both LSTM and TextCNN to BERT. We find that adding only LSTM gives the best performance.

daminglu123 at SemEval-2022 Task 2: Using BERT and LSTM to Do Text Classification

We report the results of the SemEval 2022 Task
3, PreTENS, on evaluation the acceptability
of simple sentences containing constructions
whose two arguments are presupposed to be
or not to be in an ordered taxonomic relation.
The task featured two sub-tasks articulated as:
(i) binary prediction task and (ii) regression
task, predicting the acceptability in a continu-
ous scale. The sentences were artificially gen-
erated in three languages (English, Italian and
French). 21 systems, with 8 system papers
were submitted for the task, all based on var-
ious types of fine-tuned transformer systems,
often with ensemble methods and various data
augmentation techniques. The best systems
reached an F1-macro score of 94.49 (sub-task1)
and a Spearman correlation coefficient of 0.80
(sub-task2), with interesting variations in spe-
cific constructions and/or languages.

PreTENS — EvaluatingNeural Networks on Presuppositional Semantic Knowledge

I will give a presentaion about our work from four parts: motivation, methodology, experiment and conclusion. Our work describes the system used in the
SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition, achieving 3rd for track 1 on the leaderboard. We propose Dictionary-fused BERT, a flexible approach for entity dictionaries integration. The main ideas of our systems are: 1) integrating external knowledge (an entity dictionary) into pretrained models to obtain contextualized word and entity representations 2) designing a robust loss function leveraging a logit matrix 3) adding an auxiliary task, which is an on-top
binary classification to decide whether the token is a mention word or not, makes the main task easier to learn. It is worth noting that our system achieves an F1 of 0.914 in the postevaluation stage by updating the entity dictionary to the one of GemNet, which is higher than the score of 1st on the leaderboard of the evaluation stage.


PAI at SemEval-2022 Task 11: Name Entity Recognition with Contextualized Entity Representations and Robust Loss Functions


Sarcasm is a term that refers to the use of words to mock, irritate, or amuse someone. It is commonly used on social media. The metaphorical and creative nature of sarcasm presents a significant difficulty for sentiment analysis systems based on affective computing. The methodology and results of our team, UTNLP, in the SemEval-2022 shared task 6 on sarcasm detection are presented in this paper. We put different models, and data augmentation approaches to the test and report on which one works best. The tests begin with traditional machine learning models and progress to transformer-based and attention-based models. We employed data augmentation based on data mutation and data generation. Using RoBERTa and mutation-based data augmentation, our best approach achieved an F1-score of 0.38 in the competition’s evaluation phase. After the competition, we fixed our model’s flaws and achieved an
F1-score of 0.414.

UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm
Detection using generative-based and mutation-based data augmentation

This paper describes the system proposed by Sabancı University Natural Language Processing Group in the SemEval-2022 MultiCoNER task. We developed an unsupervised entity linking pipeline that detects potential entity mentions with the help of Wikipedia and also uses the corresponding Wikipedia context to help the classifier in finding the named entity type of that mention. The proposed pipeline significantly improved the performance, especially for complex entities in low-context settings.

SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity Linking

We examine the ability of contextualized representations of pretrained language model to distinguish whether sequences from instructional articles are plausible or implausible. Towards this end, we compare the BERT, RoBERTa, and DeBERTa models using simple classifiers based on the sentence representations of the [CLS] tokens and perform a detailed analysis by visualizing the representations of the \textsc{[CLS]} tokens of the models. In the experimental results of Subtask A: Multi-Class Classification, DeBERTa exhibits the best performance and produces a more distinguishable representation across different labels. Submitting an ensemble of 10 DeBERTa-based models, our final system achieves an accuracy of 61.4% and is ranked fifth out of models submitted by eight teams. 
Further in-depth results suggest that the abilities of pretrained language models for the plausibility detection task are more strongly affected by their model structures or attention designs than by their model sizes.

JBNU-CCLab at SemEval-2022 Task 7: DeBERTa for Identifying Plausible Clarifications in Instructional Texts

We present our system created for the SemEval 2022 Task 3: Presupposed Taxonomies - Evaluating Neural-network Semantics. This task is focused on correctly recognizing taxonomic word relations in English, French and Italian. We developed various data generation techniques that expand the originally provided train set and show that all methods increase the performance of models trained on these expanded datasets. Our final system outperformed the baseline system from the task organizers by achieving an average macro F1 score of 79.6 on all languages, compared to the baseline’s 67.4.

Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations

This presentation describes our contribution to SemEval 2022 Task 8 on Multilingual News Article Similarity. The aim was to test completely different approaches and distinguish the best performing. That is why we've considered systems based on Transformer-based encoders, NER-based, and NLI-based methods (and their combination with SVO dependency triplets representation). The results prove that Transformer models produce the best scores. However, there is space for research and approaches that give not yet comparable but more interpretable results.

Multilingual News Article Similarity via Exploration of News Texts to Vector Representations

In this paper, we focus on the structured sentiment analysis task that is released on SemEval-2022 Task 10. The task aims to extract the structured sentiment information (e.g., holder, target, expression and sentiment polarity) in a text. We propose a simple and unified model for both the monolingual and crosslingual structured sentiment analysis tasks. We translate this task into an event extraction task by regarding the expression as the trigger word and the other elements as the arguments of the event. Particularly, we first extract the expression by judging its start and end indices. Then, to consider the expression, we design a conditional layer normalization algorithm to extract the holder and target based on the extracted expression. Finally, we infer the sentiment polarity based on the extracted structured information.

A Simple and Unified Model for Monolingual and Crosslingual Structured Sentiment Analysis

We describe the approach we designed to solve SemEval-2022 Task 8: Multilingual News Article Similarity. 
We collect and use exclusively textual features (title, description and body) of articles. Our best model is a stacking of 14 Transformer-based Language models fine-tuned on single or multiple fields, using data in the original language or translated to English. It placed fourth on the original leaderboard, sixth on the complete official one and fourth on the English-subset official one. 
We observe the data collection as our principal source of error due to a relevant fraction of missing or wrong fields.

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daminglu123 at SemEval-2022 Task 2: Using BERT and LSTM to Do Text Classification

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