Thailand

Sentiment analysis serves as a pivotal component in Natural Language Processing (NLP). Advancements in multilingual pre-trained models such as XLM-R and mT5 have contributed to the increasing interest in cross-lingual sentiment analysis. The recent emergence in Large Language Models (LLM) has significantly advanced general NLP tasks, however, the capability of such LLMs in cross-lingual sentiment analysis has not been fully studied. This work undertakes an empirical analysis to compare the cross-lingual transfer capability of public Small Multilingual Language Models (SMLM) like XLM-R, against English-centric LLMs such as Llama-3, in the context of sentiment analysis across English, Spanish, French and Chinese. Our findings reveal that among public models, SMLMs exhibit superior zero-shot cross-lingual performance relative to LLMs. However, in few-shot cross-lingual settings, public LLMs demonstrate an enhanced adaptive potential. In addition, we observe that proprietary GPT-3.5 and GPT-4 lead in zero-shot cross-lingual capability, but are outpaced by public models in few-shot scenarios.

ACL 2024

The Model Arena for Cross-lingual Sentiment Analysis: A Comparative Study in the Era of Large Language Models

sentiment analysis

few-shot

cross-lingual

zero-shot

fine-tuning

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

The objective of this paper is to predict (A)~whether a sentence in a written text expresses an emotion, (B) the mode(s) in which the emotion is expressed, (C) whether it is basic or complex, and (D) its emotional category.
One of our major contributions, in addition to a dataset and a model, is to integrate the fact that an emotion can be expressed in different modes: from a direct mode, essentially lexicalized, to a more indirect mode, where emotions will only be suggested, a mode that NLP approaches generally don't take into account. 

The scope is on written texts, i.e. it does not focus on conversational or multi-modal data. In this context, modes of expression are seen as a factor towards the automatic analysis of complexity in texts.

Experiments on French texts show acceptable results compared to the human annotators' agreement to predict the mode and category, and outperforming results compared to using a large language model with in-context learning (i.e. no fine-tuning) on all tasks.

Dataset and model can be downloaded on HuggingFace: https://huggingface.co/TextToKids .

Emotion Identification for French in Written Texts: Considering their Modes of Expression as a Step Towards Text Complexity Analysis

Measuring happiness as a determinant of well-being is increasingly recognized as crucial. 
While previous studies have utilized free-text descriptions to estimate happiness on a broad scale, limited research has focused on tracking individual fluctuations in happiness over time owing to the challenges associated with longitudinal data collection. 
This study addresses this issue by obtaining longitudinal data from two workplaces over two and six months respectively.
Subsequently, the data is used to construct a happiness estimation model and assess individual happiness levels.
Evaluation of the model performance using correlation coefficients shows variability in the correlation values among individuals.
Notably, the model performs satisfactorily in estimating 9 of the 11 users’ happiness scores, with a correlation coefficient of 0.4 or higher. 
To investigate the factors affecting the model performance, we examine the relationship between the model performance and variables such as sentence length, lexical diversity, and personality traits. Correlations are observed between these features and model performance.

Estimation of Happiness Changes through Longitudinal Analysis of Employees' Texts

The advent of deep learning models has made a considerable contribution to the achievement of Emotion Recognition in Conversation (ERC). However, this task still remains an important challenge due to the plurality and subjectivity of human emotions. Previous work on ERC provides predictive models using mostly graph-based conversation representations. In this work, we propose a way to model the conversational context that we incorporate into a metric learning training strategy, with a two-step process. This allows us to perform ERC in a flexible classification scenario and end up with a lightweight yet efficient model. Using metric learning through a Siamese Network architecture, we achieve 57.71 in macro F1 score for emotion classification in conversation on DailyDialog dataset, which outperforms the related work. This state-of-the-art result is promising in terms of the use of metric learning for emotion recognition, yet perfectible compared to the micro F1 score obtained.

SEC: Context-Aware Metric Learning for Efficient Emotion Recognition in Conversation

Depression is a highly prevalent condition recognized by the World Health Organization as a leading contributor to global disability. Many people suffering from depression express their thoughts and feelings using social media, which thus becomes a source of data for research in this domain. However, existing annotation schemes tailored to studying depression symptoms in social media data remain limited. Reliable and valid annotation guidelines are crucial for accurately measuring mental health conditions for those studies. This paper addresses this gap by presenting a novel depression annotation scheme and guidelines for detecting depression symptoms and their severity in social media text. Our approach leverages validated depression questionnaires and incorporates the expertise of psychologists and psychiatrists during scheme refinement. The resulting annotation scheme achieves high inter-rater agreement, demonstrating its potential for suitable depression assessment in social media contexts.

Monitoring Depression Severity and Symptoms in User-Generated Content: An Annotation Scheme and Guidelines

To be included into chatbot systems, Large language models (LLMs) must be aligned with human conversational conventions. However, being trained mainly on web-scraped data gives existing LLMs a voice closer to informational text than actual human speech. In this paper, we examine the effect of decoding methods on the alignment between LLM-generated and human conversations, including Beam Search, Top K Sampling, and Nucleus Sampling. We present new measures of alignment in substance, style, and psychometric orientation, and experiment with two conversation datasets. Our results provide subtle insights: better alignment is attributed to fewer beams in Beam Search and lower values of P in Nucleus Sampling. We also find that task-oriented and open-ended datasets perform differently in terms of alignment, indicating the significance of taking into account the context of the interaction.

Impact of Decoding Methods on Human Alignment of Conversational LLMs

While large language models (LLMs) show promise for various tasks, their performance in compound aspect-based sentiment analysis (ABSA) tasks lags behind fine-tuned models. However, the potential of LLMs fine-tuned for ABSA remains unexplored. This paper examines the capabilities of open-source LLMs fine-tuned for ABSA, focusing on LLaMA-based models. We evaluate the performance across four tasks and eight English datasets, finding that the fine-tuned Orca 2 model surpasses state-of-the-art results in all tasks. However, all models struggle in zero-shot and few-shot scenarios compared to fully fine-tuned ones. Additionally, we conduct error analysis to identify challenges faced by fine-tuned models.

LLaMA-Based Models for Aspect-Based Sentiment Analysis

Cross-lingual emotion detection allows us to analyze global trends, public opinion, and so- cial phenomena at scale. We participated in the Explainability of Cross-lingual Emotion De- tection (EXALT) shared task, achieving an F1- score of 0.6046 on the evaluation set for the emotion detection sub-task. Our system outper- formed the baseline by more than 0.16 F1-score absolute, and ranked second amongst compet- ing systems. We conducted experiments using fine-tuning, zero-shot learning, and few-shot learning for Large Language Model (LLM)- based models as well as embedding-based BiL- STM and KNN for non-LLM-based techniques. Additionally, we introduced two novel meth- ods: the Multi-Iteration Agentic Workflow and the Multi-Binary-Classifier Agentic Workflow. We found that LLM-based approaches provided good performance on multilingual emotion de- tection. Furthermore, ensembles combining all our experimented models yielded higher F1- scores than any single approach alone.

TEII: Think, Explain, Interact and Iterate with Large Language Models toSolve Cross-lingual Emotion Detection"

Cross-lingual emotion detection faces challenges such as imbalanced label distribution, data scarcity, cultural and linguistic differences, figurative language, and the opaqueness of pre-trained language models. This paper presents our approach to the EXALT shared task at WASSA 2024, focusing on emotion transferability across languages and trigger word identification. We employ data augmentation techniques, including back-translation and synonym replacement, to address data scarcity and imbalance issues in the emotion detection sub-task. For the emotion trigger identification sub-task, we utilize token and label mapping to capture emotional information at the subword level. Our system achieves competitive performance, ranking 13th, 1st, and 2nd in the Emotion Detection, Binary Trigger Word Detection, and Numerical Trigger Word Detection tasks.

Enhancing Cross-Lingual Emotion Detection with Data Augmentation and
Token-Label Mapping

This study describes the model design of the NYCU-NLP system for the EXALT shared task at the WASSA 2024 workshop. We instruction-tune several large language models and then assemble various model combinations as our main system architecture for cross-lingual emotion and trigger detection in tweets. Experimental results showed that our best performing submission is an assembly of the Starling (7B) and Llama 3 (8B) models. Our submission was ranked sixth of 17 participating systems for the emotion detection subtask, and fifth of 7 systems for the binary trigger detection subtask.

NYCU-NLP at EXALT 2024: Assembling Large Language Models for
Cross-Lingual Emotion and Trigger Detection

This paper describes our task 1 submission for the WASSA 2024 shared task on Explainability for Cross-lingual Emotion in Tweets. Our task is to predict the correct emotion label (Anger, Sadness, Fear, Joy, Love, and Neutral) for a dataset of English, Dutch, French, Spanish, and Russian tweets, while training exclusively on English emotion labeled data, to reveal what kind of emotion detection information is trans- ferable cross-language (Maladry et al., 2024). To that end, we used an ensemble of mod- els with a GPT-4 decider. Our ensemble con- sisted of a few-shot GPT-4 prompt system and a TwHIN-BERT system fine-tuned on the EX- ALT and additional English data. We ranked 8th place under the name WU_TLAXE with an F1 Macro score of 0.573 on the test set. We also experimented with an English-only TwHIN-BERT model by translating the other languages into English for inference, which proved to be worse than the other models.

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Emotion Identification for French in Written Texts: Considering their Modes of Expression as a Step Towards Text Complexity Analysis

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Emotion Identification for French in Written Texts: Considering their Modes of Expression as a Step Towards Text Complexity Analysis

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Analyzing Zero-shot Cross-lingual Transfer in Supervised NLP Tasks

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