Spain

This work presents our contribution to the Offensive Language Classification Task (EnglishSubTask A) of Semeval 2020. We propose different Bert models trained on several offensive language classification and profanity datasets, and combine their output predictions in an ensemble model. We experimented with different ensemble approaches, such as SVMs, Gradient boosting,AdaBoosting and Logistic Regression. We further propose an under-sampling approach of thecurrent SOLID dataset, which removed the most uncertain partitions of the dataset, increasing therecall of the dataset. Our best model, an average ensemble of four different Bert models, achieved11th place out of 82 participants with a macro F1 score of 0.91344 in the English SubTask A.

COLING 2020

Ensemble of offensive language classifiers

offenseval

semeval 2020

offensive language classification

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.

poster

This paper describes our approach to the task of identifying offensive languages in a multilingual setting. We investigate two data augmentation strategies: using additional semi-supervised labels with different thresholds and cross-lingual transfer with data selection. Leveraging the semi-supervised dataset resulted in performance improvements compared to the baseline trained solely with the manually-annotated dataset. We propose a new metric, Translation Embedding Distance, to measure the transferability of instances for cross-lingual data selection. We also introduce various preprocessing steps tailored for social media text along with methods to fine-tune the pre-trained multilingual BERT (mBERT) for offensive language identification. Our multilingual systems achieved competitive results in Greek, Danish, and Turkish at OffensEval 2020.

Improving Offensive Language Detection with Cross-lingual Transfer

Sentiment analysis for code-mixed social media text continues to be an under-explored area. This work adds two common approaches: fine-tuning large transformer models and sample efficient methods like ULMFiT \cite{ulmfit}. Prior work demonstrates the efficacy of classical ML methods for polarity detection. Fine-tuned general-purpose language representation models, such as those of the BERT family are benchmarked along with classical machine learning and ensemble methods. We show that NB-SVM beats RoBERTa by 6.2F1. The best performing model is a majority-vote ensemble which achieves an F1 of 0.707.

Fine-tuned Language Models for Hinglish Sentiment Detection

This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive LanguageDetection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research

A jointly-trained BERT multilingual model for offensive language detection


We participated in both the span identification and technique classification subtasks for SemEval-2020 Task 11. We experimented with several BERT-based models along with handcrafted features. Our models perform well above the baselines for both tasks, and we contribute ablation studies and discussion of our results to dissect the effectiveness of different features and techniques.

Features, Finetuning, and Data Augmentation in Neural for Propaganda Detection in News Articles

This poster describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6% and a micro-averaged F-score of 58.2% for those tasks accordingly

A Borrowed Approach to Sequence Tagging and Text Classification

In this paper, we present the results that the team IIITG-ADBU obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval 2020). This task required the detection of sentiment in code-mixed Hindi-English tweets. Broadly, we performed two sets of experiments for this task. The first experiment was performed using the multilingual BERT classifier and the second set of experiments was performed using SVM classifiers. The character-based SVM classifier obtained the best F1 score of 0.678 in the test set with a rank of 21 among 62 participants. The performance of the multilingual BERT classifier was quite comparable with the SVM classifier on the development set. However, on the test set it obtained an F1 score of 0.342.

SVM for Sentiment Analysis of English-Hindi Code-Mixed Text

We introduced the masked reasoner system that participated in 
SemEval-2020 Task 4: Commonsense Validation and Explanation. The system participated in the subtask B. We propose a novel method to fine-tune RoBERTa by masking the most important word in the statement. We believe that the confidence of the system in recovering that word is positively correlated to the score the masked language model assigns to the current statement-explanation pair. We evaluate the importance of each word using InferSent and do the masked fine-tuning on RoBERTa. Then we use the fine-tuned model to predict the most plausible explanation. Our system is fast in training and achieved 73.5% accuracy.

Fine-Tuning RoBERTa for Commonsense Reasoning

This paper describes the UCD-Net system entered for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We propose a novel method based on distance between temporally referenced lexical nodes in a semantic network constructed from a combination of the time specific corpora. We argue for the value of semantic networks as objects for transparent exploratory analysis and visualisation of lexical semantic change, and present an application of an
interactive semantic network exploration tool for interpreting specific aspects of subtle changes inconceptual meaning.

Temporal Referencing
with Semantic Network Distances

In this paper, we explore strategies to detect and evaluate counterfactual sentences. We describe our system for SemEval-2020 Task 5: Modeling Causal Reasoning in Language: Detecting Counterfactuals. We use a BERT base model for the classification task and build a hybrid BERT Multi-Layer Perceptron system to handle the sequence identification task. Our experiments show that while introducing syntactic and semantic features does little in improving the system in the classification task, using these types of features as cascaded linear inputs to fine-tune the sequence-delimiting ability of the model ensures it outperforms other similar-purpose complex systems like BiLSTM-CRF in the second task. Our system achieves an F1 score of 85.00% in Task 1 and 83.90% in Task 2.

Cascaded BERT Language Model for Counterfactual Statement Analysis

We attempt to demonstrate how to measure the relationships between a historical text and two other works of fiction written six centuries later when they have a similar set of characters with a social network framework. In order to achieve this, we annotated named-entities in the three texts in a semi-automatic way and made the annotation available for future research. We show that the surviving characters with a conserved pronunciation from the Nibelungenlied and from the Völsunga saga got more prominent roles than they had in Decem Libri Historium.

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