Spain

We apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking words by the degree of their semantic drift over time. We analyse the performance of two contextualising architectures (BERT and ELMo) and three change detection algorithms. We find that the most effective algorithms rely on the cosine similarity between averaged token embeddings and the pairwise distances between token embeddings. They outperform strong baselines by a large margin (in the post-evaluation phase, we have the best Subtask 2 submission for SemEval-2020 Task 1), but interestingly, the choice of a particular algorithm depends on the distribution of gold scores in the test set.

COLING 2020

Contextualised Embeddings for Lexical Semantic Change Detection

diachronic semantic shifts

semantic change detection

contextualized embeddings

elmo

bert

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 the approaches used by the Discovery Team to solve SemEval-2020 Task 1 -Unsupervised Lexical Semantic Change Detection. The proposed method is based on clusteringof BERT contextual embeddings, followed by a comparison of cluster distributions across time.The best results were obtained by an ensemble of this method and static Word2Vec embeddings.According to the official results, our approach proved the best for Latin in Subtask 2.

Static Embeddings better than Contextualised for Semantic Change Detection?

This poster describes an ensemble model designed for SemEval2020-Task 7. The task is based on the Humicroedit datasets that is comprised of news titles and one-word substitutions to make them humorous. We use BERT, FastText, Elmo, and Word2Vec to encode these titles then pass them to a Bidirectional GRU with attention. Finally, we use XGBoost on the concatenation of the results to predict the humor rating.

BiGRU Ensemble for Evaluating Humor in Edited News Headlines

We participate in both subtasks of the SemEval-2020 task Detection of Propaganda Techniques in News Articles: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our architectures are built using embeddings from BERT in combination with additional lexical features and extensive label post-processing. Our systems achieve a rank of 8 out of 35 teams in the SI subtask (F1-score: 43.86%) and 8 out of 31 teams in the TC subtask (F1-score: 57.37%).

An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection

We present an approach for sentiment analysis in code mixed
language on twitter defined in SemEval 2020 Task 9.Our team
(referred as LiangZhao ) employ different multilingual models with
weighted loss focused on complexity of code mixing in sentence, in
which the best model achieved f1 score of 0.806 and ranked 1 st of
subtask Sentimix Spanglish. The performance of method is analyzed

Cross lingual Models
with Focal Loss for Sentiment Analysis of Code Mixing Language

We present our submission to subtask A at SemEval 2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval2). For Danish, Turkish, Arabic and Greek, we develop an architecture based on transfer learning and relying on a two-channel BERT model, in which the English BERT and the multilingual one are combined after creating a machine-translated parallel corpus for each language in the task. For English, instead, we adopt a more standard, single-channel approach. 
We find that, in a multilingual scenario, with some languages having small training data, using parallel BERT models with machine translated data can give systems more stability, especially when dealing with noisy data. The fact that machine translation on social media data may not be perfect does not hurt the overall classification performance.

Using Multi-Channel BERT for Multilingual Offensive Language Detection

SemEval 2020 Task 12 focusses on the challenge of detecting offensive language present in social media. To tackle the subtask A (Offensive language identification) and the subtask C (Identifying the targets of offensive language) of SemEval 2020 Task 12, we developed ensembles of classifiers based on DistilBERT pre-trained language model. Our ensemble models achieved F1 scores of 0.906 for the subtask A, and 0.623 for the subtask C


Ensembles of Classifiers for Offensive Language Detection in Social Media

This work presents a system based on BERT for the offensive language detection in English and MBERT for the multilingual analysis. We proposed the system for the three subtasks of the task 12 of SemEval-2020: OffensEval 2020.

BERT for Multilingual Offensive Language Detection

This work presents a multimodal model that combines the Bert and VGG architectures for the analysis of the textual and visual information from memes respectively. We proposed the model for each of the three subtasks of the task 8 of SemEval-2020: Memotion Analysis. The objective is to identify whether an Internet meme presents one, several or none of the categories sarcasm, humor, offense and motivation. Moreover, the sentiment analysis of memes is included.

Study of Multimodal Techniques for Memes Analysis

Code mixing is a common phenomena in multilingual societies where people switch from one language to another for various reasons. Recent advances in public communication over different social media sites have led to an increase in the frequency of code-mixed use in written language. In this paper, we present the Generative Morphemes with Attention (GenMA) Model sentiment analysis system, which contributes to the SemEval 2020 Task 9 SentiMix. This system aims to predict the sentiments of the given English-Hindi code-mixed tweets without using word-level language tags, but instead inferring emotion content automatically using a morphological model. The system is based on a novel deep neural network (DNN) architecture, which has outperformed the baseline F1-score on the test data-set as well as the validation data-set.

Generative Morphemes with an Attention Model for Sentiment Analysis in Code-Mixed Text

This paper describes the participation of SINAI team at Task 12: OffensEval 2: Multilingual Offensive Language Identification in Social Media. In particular, the participation in Sub-task A in English which consists of identifying tweets as offensive or not offensive. We preprocess the dataset according to the language characteristics used on social media. Then, we select a small set from the training set provided by the organizers and fine-tune different Transformer-based models in order to test their effectiveness. Our team ranks 20th out of 85 participants in Subtask-A using the XLNet model.

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Static Embeddings better than Contextualised for Semantic Change Detection?

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Static Embeddings better than Contextualised for Semantic Change Detection?

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