Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userEmotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection
Robbert العاطفي Bertje غير حساس: الجمع بين المحولات وتؤثر على Lexica للكشف عن العاطفة الهولندية
151
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
في الخطوة الأولى نحو تحسين الكشف عن المشاعر الهولندية، نحاول الجمع بين نماذج المحولات الهولندية Bertje والرسم مع الأساليب القائمة على المعجم.نقترح دوران هندستين: واحدة يتم فيها حقن معلومات معجمية مباشرة في طراز المحول ونهج التعلم التلوي حيث يتم دمج التنبؤات من المحولات مع ميزات المعجم.يتم اختبار النماذج على 1000 تغريدة هولندية و 1000 تعليق من البرامج التلفزيونية التي تم تفاحها يدويا مع فئات العاطفة والأبعاد.نجد أن Robbert تفوق بوضوح Bertje، ولكن هذا يضيف مباشرة معلومات المعجم إلى المحولات لا يحسن الأداء.في نهج التعلم التلوي، أصبحت معلومات المعجم تأثير إيجابي على Bertje، ولكن ليس على Robbert.هذا يشير إلى أن المزيد من المعلومات العاطفية تحتوي بالفعل ضمن نموذج اللغة الأخير.
In a first step towards improving Dutch emotion detection, we try to combine the Dutch transformer models BERTje and RobBERT with lexicon-based methods. We propose two architectures: one in which lexicon information is directly injected into the transformer model and a meta-learning approach where predictions from transformers are combined with lexicon features. The models are tested on 1,000 Dutch tweets and 1,000 captions from TV-shows which have been manually annotated with emotion categories and dimensions. We find that RobBERT clearly outperforms BERTje, but that directly adding lexicon information to transformers does not improve performance. In the meta-learning approach, lexicon information does have a positive effect on BERTje, but not on RobBERT. This suggests that more emotional information is already contained within this latter language model.
References used
https://aclanthology.org/
rate research
Read More
Since their inception, transformer-based language models have led to impressive performance gains across multiple natural language processing tasks. For Arabic, the current state-of-the-art results on most datasets are achieved by the AraBERT languag
e model. Notwithstanding these recent advancements, sarcasm and sentiment detection persist to be challenging tasks in Arabic, given the language's rich morphology, linguistic disparity and dialectal variations. This paper proffers team SPPU-AASM's submission for the WANLP ArSarcasm shared-task 2021, which centers around the sarcasm and sentiment polarity detection of Arabic tweets. The study proposes a hybrid model, combining sentence representations from AraBERT with static word vectors trained on Arabic social media corpora. The proposed system achieves a F1-sarcastic score of 0.62 and a F-PN score of 0.715 for the sarcasm and sentiment detection tasks, respectively. Simulation results show that the proposed system outperforms multiple existing approaches for both the tasks, suggesting that the amalgamation of context-free and context-dependent text representations can help capture complementary facets of word meaning in Arabic. The system ranked second and tenth in the respective sub-tasks of sarcasm detection and sentiment identification.
The problem of detecting psychological stress in online posts, and more broadly, of detecting people in distress or in need of help, is a sensitive application for which the ability to interpret models is vital. Here, we present work exploring the us
e of a semantically related task, emotion detection, for equally competent but more explainable and human-like psychological stress detection as compared to a black-box model. In particular, we explore the use of multi-task learning as well as emotion-based language model fine-tuning. With our emotion-infused models, we see comparable results to state-of-the-art BERT. Our analysis of the words used for prediction show that our emotion-infused models mirror psychological components of stress.
Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis. Recent works have explored knowledge infusion supplementing the linguistic competence a
nd latent knowledge of large pre-trained language models with structured knowledge graphs (KGs), yet few works have applied such methods to the SD task. In this work, we first perform stance-relevant knowledge probing on Transformers-based pre-trained models in a zero-shot setting, showing these models' latent real-world knowledge about SD targets and their sensitivity to context. We then train and evaluate new knowledge-enriched stance detection models on two Twitter stance datasets, achieving state-of-the-art performance on both.
In Romanian language there are some resources for automatic text comprehension, but for Emotion Detection, not lexicon-based, there are none. To cover this gap, we extracted data from Twitter and created the first dataset containing tweets annotated
with five types of emotions: joy, fear, sadness, anger and neutral, with the intent of being used for opinion mining and analysis tasks. In this article we present some features of our novel dataset, and create a benchmark to achieve the first supervised machine learning model for automatic Emotion Detection in Romanian short texts. We investigate the performance of four classical machine learning models: Multinomial Naive Bayes, Logistic Regression, Support Vector Classification and Linear Support Vector Classification. We also investigate more modern approaches like fastText, which makes use of subword information. Lastly, we fine-tune the Romanian BERT for text classification and our experiments show that the BERT-based model has the best performance for the task of Emotion Detection from Romanian tweets. Keywords: Emotion Detection, Twitter, Romanian, Supervised Machine Learning
We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations. Our model is trained by minimizing the EMD (Earth Mover's Distance) loss
between the predicted VAD score distribution and the categorical emotion distributions sorted along VAD, and it can simultaneously classify the emotion categories and predict the VAD scores for a given sentence. We use pre-trained RoBERTa-Large and fine-tune on three different corpora with categorical labels and evaluate on EmoBank corpus with VAD scores. We show that our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification and shows significant positive correlations with the ground truth VAD scores. Also, further training with supervision of VAD labels leads to improved performance especially when dataset is small. We also present examples of predictions of appropriate emotion words that are not part of the original annotations.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
