Spain

The poster presents our work on Semeval Task 4. Aside from creating competitive English models, we evaluate different approaches to using the system in the Czech language. We also show how models from subtask B can be used to rerank and improve outputs of models in subtask C. Inversely, we demonstrate that accuracy of subtask C models on subtask B correlates better with human judgement than BLEU score, which was used for the official evaluation.

COLING 2020

Multilingual commonsense

commonsense

multilingual

machine translation

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

poster

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, 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.

While Social media platforms can be valuable, they can also be exploited to promote negative views about an individual, race, or group with millions of people. There is thus an urgent need to develop mechanisms by which offensive language can be automatically detected in social media and managed in a timely manner.
We propose an ensemble approach that combines different levels of word embedding models and transfer learning from other sources of emotion-related tasks. The proposed system ranked 9th out of the 52 entries within the Arabic Offensive language identification subtask.


An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media

This paper describes our system submitted to task 4 of SemEval 2020: Commonsense Validation and Explanation (ComVE) which consists of three sub-tasks. The challenge is to directly validate whether the system can recognize natural language statements that make sense from those that do not and also require to generate reasonable explanation. Based on BERT architecture with the multi-task setting, we propose an effective and interpretable “Explain, Reason and Predict” (ERP) system to solve the three sub-tasks about commonsense: (a) Validation, and (c) Explanation, (b) Reasoning, following the order of the competition. Inspired by cognitive studies of common sense, our system ﬁrst generates a reason or understanding of the sentences and then choose which one statement makes sense, which is achieved by multi-task learning. The rational experiment validates our assumption and boosts performance. During the post-evaluation, our system has reached 92.9% accuracy in subtask A (rank 11), 89.7% accuracy in subtask B (rank 8), and a BLEU score of 12.9 in subtask C (rank 9).

CommonSense Explanation, Reasoning and Prediction with Multi-task Learning

Given the semantics of words captured by temporal word embeddings in different time periods, we investigate the use of unsupervised methods to detect when the target word has gained or lost senses.
To this end, we define a new algorithm based on Gaussian Mixture Models to cluster the target similarities computed over the two periods. We compare the proposed approach with a number of similarity-based thresholds. We found that, although the performance of the detection methods varies across the word embedding algorithms, the combination of Gaussian Mixture with Temporal Referencing resulted in our best system.

Gaussian Mixtures Cross Temporal Similarity Clustering

Task 12 of SemEval 2020 consisted of 3 subtasks, namely offensive language identification (Subtask A), categorization of offense type (Subtask B), and offense target identification (Subtask C). This paper presents the results our classifiers obtained for the English language in the 3 subtasks. The classifiers used by us were BERT and BiLSTM. On the test set, our BERT classifier obtained a macro F1 score of 0.90707 for subtask A, and 0.65279 for subtask B. The BiLSTM classifier obtained a macro F1 score of 0.57565 for subtask C. The paper also performs an analysis of the errors made by our classifiers. We conjecture that the presence of a few misleading instances in the dataset is affecting the performance of the classifiers. Our analysis also discusses the need for temporal context and world knowledge to determine the offensiveness of a few comments.

Comparison of BERT and BiLSTM in Detecting Offensive Language

We present a novel rule-based, language independent method for determining lexical entailment relations using semantic representations built from Wiktionary definitions. Combined with a simple WordNet-based method our system achieves top scores on the English and Italian datasets of the Semeval-2020 task “Predicting Multilingual and Cross-lingual (graded) Lexical Entailment” (Glavas et al., 2020). A detailed error analysis of our output uncovers future directions for improving both the semantic parsing method and the inference process on semantic graphs. reported in this document.


Lexical entailment with semantic graphs

We present our submitted systems for SemEval-2020 Task 8 titled "Memotion Analysis". For this task we used a multimodal architecture to determine the emotion expressed in a meme. Our architecture utilizes both textual and visual information present in a meme. To extract image features we experimented with pre-trained VGG-16 and Inception-V3 classifiers and to extract text features we used LSTM and BERT classifiers. Both FastText and GloVe embeddings were experimented with for the LSTM classifier. The best F1 scores our classifier obtained on the official analysis results are 0.3309, 0.4752, and 0.2897 for Task A, B, and C respectively. In our study, we found that combining both textual and visual information expressed in a meme improves the performance of the classifier as opposed to using standalone classifiers that use only text or visual data.

A Multimodal Approach to Detect Offensive, Sarcastic and Humorous Memes

In this paper, we describe an approach for modelling causal reasoning in natural language by detecting counterfactuals in text using multi-head self-attention weights. We use pre-trained transformer models to extract contextual embeddings and self-attention weights from the text. We show the use of convolutional layers to extract task-specific features from these self-attention weights. Further, we describe a fine-tuning approach with a common base model for knowledge sharing between the two closely related sub-tasks for counterfactual detection. We analyze and compare the performance of various transformer models in our experiments. Finally, we perform a qualitative analysis with the multi-head self-attention weights to interpret our models' dynamics.

Modelling Causal Reasoning in Language with Multi-Head Self-Attention Weights Based Counterfactual Detection

- Proposing a domain-tuned contextualized language model for
Offensive Language Detection task.
- Experimenting with different components and configurations (e.g.,
a multi-view SVM) stacked upon BERT models for specific sub-tasks.
- Achieving F1 scores of 91.7% in Sub-task A, 66.5% in Sub-task B,
and 63.2% in Sub-task C, ranking 5th, 6th, and 11th respectively.
- Performing error analysis to show common misclassification errors
made by our model and outlining research directions for future.

Domain-Tuned Contextualized Models for Offensive Language Detection

We describe the system submitted by the SWAGex team to the SemEval-2020 Commonsense Validation and Explanation Task. We used multiple methods on the pre-trained language model BERT (Devlin et al., 2018) for tasks that require the system to recognize sentences against commonsense and justify the reasoning behind this decision. Our best performing model is BERT trained on SWAG fine-tuned for the task. We investigate the ability to transfer commonsense knowledge from SWAG to SemEval-2020 by training a model for the Explanation task with Next Event Prediction data.

Commonsense Explanation as Next Event Prediction

This poster describes the NTUAAILS submission for SemEval 2020 Task 11 Detection of Propaganda Techniques in News Articles. Using deep learning techniques to detect and classify propaganda techniques, we can automatically provide the means to address propaganda and fake news.
In order to achieve this goal, two separate models were created using bidirectional LSTMs and pre-trained word embeddings like ELMo and GloVe. 
At first, the detection sub-task (Span identification) was addressed as Named Entity Recognition problem (NER). The entities consisted of two categories (token-level) propaganda and no-propaganda.
After the detection, the second sub-task's (Technique Classification) goal was to classify the given propaganda fragments in the 14 propaganda classes.
The models achieved rank 28 among 36 teams with F1 score of 0.335 and ranked 25 among 31 teams with 0.463 F1 scores for Span Identification and Technique Classification sub-tasks respectively. Our results indicate that the proposed deep learning models, although relatively simple in architecture and fast to train, achieve satisfactory results in the tasks on hand.

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Next from COLING 2020

An Ensemble of Different Word Embeddings and Emotion Transfer Learning for Arabic Offensive Language Identification in Social Media

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SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection

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COMPANY

RESOURCES

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