Spain

We consider detection of the span of antecedents and consequents in argumentative prose a structural, grammatical task. Our system comprises a set of stacked Bi-LSTMs trained on two complementary linguistic annotations. We explore the effectiveness of grammatical features (POS and clause type) through ablation. The reported experiments suggest that a multi-task learning approach using this external, grammatical knowledge is useful for detecting the extent of antecedents and consequents and performs nearly as well without the use of word embeddings.

COLING 2020

Multi-task Stacked Bi-LSTMs

grammatical features

stacked bilstms

ablation

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CAP Virtual Congress 2021

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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.

We present our systems to solve Task 3 of Semeval-2020, which aims to predict the effect that context has on human perception of similarity of words. The task consists of two subtasks in English, Croatian, Finnish and Slovenian: (1) predicting the change of similarity, and (2) predicting the human scores of similarity, both of them for a pair of words within two different contexts. We tackled the problem by developing two systems, the first one uses a centroid approach and word vectors. The second one uses the ELMo language model, which is trained for each pair of words with the given context. Our approach achieved the highest score in subtask 2 for the English language.

Predicting Contextual Word Similarity Using a Centroid based Approach and Word Embeddings

This poster describes our approach to "DeftEval: Extracting Definitions from Free Text in Textbooks" competition held as a part of SemEval 2020. The task was devoted to finding and labeling definitions in texts.

Multi-task learning for Definition Extraction

We propose hybrid models (HybridE and HybridW) for meme analysis (SemEval 2020 Task 8), which involves sentiment classification (Subtask A), humor classification (Subtask B), and scale of semantic classes (Subtask C). The hybrid model consists of BLSTM and CNN for text and image processing respectively. HybridE provides equal weight to BLSTM and CNN performance, while HybridW provides weightage based on the performance of BLSTM and CNN on a validation set. The performances (macro F1) of our hybrid model on Subtask A are 0.329 (HybridE),0.328 (HybridW), on Subtask B are 0.507 (HybridE), 0.512 (HybridW), and on Subtask C are0.309 (HybridE), 0.311 (HybridW).

You’re not the only one
cursed with knowledge –Multi branch model memotion analysis

In this paper, we assess the ability of BERT and its derivative models (RoBERTa, DistilBERT, and ALBERT) for short-edits-based humor grading. We test these models for humor grading and classification tasks on the Humicroedit and the FunLines dataset. We perform extensive experiments with these models to test their language modeling and generalization abilities via zero-shot inference and cross dataset inference-based approaches. Further, we also inspect the role of self-attention layers in humor-grading by performing a qualitative analysis over the self-attention weights from the final layer of the trained BERT model. Our experiments show that all the pre-trained BERT derivative models show significant generalization capabilities for humor-grading related tasks.

Assessing the Ability of BERT and Derivative Models to Perform Short-Edits based Humor Grading

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.

Ensemble of offensive language classifiers

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

Multi-task Stacked Bi-LSTMs

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