United States

Political sentiment analysis has become an essential area of research in Natural Language Processing (NLP), driven by the rapid rise of social media as a key platform for political discourse. This study focuses on sentiment classification in Tamil political tweets, addressing the linguistic and cultural complexities inherent in low-resource languages. To overcome data scarcity challenges, we develop a system that integrates embeddings with advanced Machine Learning techniques, ensuring effective sentiment categorization. Our approach leverages deep learning-based models and transformer architectures to capture nuanced expressions, contributing to improved sentiment classification. This work enhances NLP methodologies for low-resource languages and provides valuable insights into Tamil political discussions, aiding policymakers and researchers in understanding public sentiment more accurately. Notably, our system secured Rank 5 in the NAACL shared task, demonstrating its effectiveness in real-world sentiment classification challenges.

NAACL 2025

Wictory@DravidianLangTech 2025: Political Sentiment Analysis of Tamil X(Twitter) Comments using LaBSE and SVM

tamil code mixed

political sentiment analysis

fine tuning

machine learning

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

workshop paper

Abusive language directed at women on social media, often characterized by crude slang, offensive terms, and profanity, is not just harmful communication but also acts as a tool for serious and widespread cyber violence. It is imperative that this pressing issue be addressed in order to establish safer online spaces and provide efficient methods for detecting and minimising this kind of abuse. However, the intentional masking of abusive language, especially in regional languages like Tamil and Malayalam, presents significant obstacles, making detection and prevention more difficult. The system created effectively identifies abusive sentences using supervised machine learning techniques based on RoBerta embeddings. The method aims to improve upon the current abusive language detection systems, which are essential for various online platforms, including social media and online gaming services. The proposed method currently ranked 8 in malayalam and 20 in tamil in terms of f1 score.

ANSR@DravidianLangTech 2025: Detection of Abusive Tamil and Malayalam Text Targeting Women on Social Media using RoBERTa and XGBoost

Social media platforms like X (twitter) have gained popularity for political debates and election campaigns in the last decade. This creates the need to moderate and understand the sentiments of the tweets in order to understand the state of digital campaigns. This paper focuses on political sentiment classification of Tamil X (Twitter) comments which proves to be challenging because of the presence of informal expressions, code-switching, and limited annotated datasets. This study focuses on categorizing them into seven classes: substantiated, sarcastic, opinionated, positive, negative, neutral, and none of the above. This paper proposes a solution to Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments - DravidianLangTech@NAACL 2025 shared task, the solution incorporates IndicBERTv2-MLM-Back-Translation model and TF-IDF vectors into a custom model. Further we explore the use of preprocessing techniques to enrich hashtags and emojis with their context. Our approach achieved Rank 1 with a macro F1 average of 0.38 in the shared task.

Synapse@DravidianLangTech 2025: Multiclass Political Sentiment Analysis in Tamil X (Twitter) Comments: Leveraging Feature Fusion of IndicBERTv2 and Lexical Representations

With the exponential growth of social media usage, the prevalence of abusive language targeting women has become a pressing issue, particularly in low-resource languages (LRLs) like Tamil and Malayalam. This study is part of the shared task at DravidianLangTech@NAACL 2025, which focuses on detecting abusive comments in Tamil social media content. The provided dataset consists of binary-labeled comments (Abusive or Non-Abusive), gathered from YouTube, reflecting explicit abuse, implicit bias, stereotypes, and coded language. We developed and evaluated multiple models for this task, including traditional machine learning algorithms (Logistic Regression, Support Vector Machine, Random Forest Classifier, and Multinomial Naive Bayes), deep learning models (CNN, BiLSTM, and CNN+BiLSTM), and transformer-based architectures (DistilBERT, Multilingual BERT, XLM-RoBERTa), and fine-tuned variants of these models. Our best-performing model, Multilingual BERT, achieved a weighted F1-score of 0.7203, ranking 19 in the competition.

cuetRaptors@DravidianLangTech 2025: Transformer-Based Approaches for Detecting Abusive Tamil Text Targeting Women on Social Media

Zero-shot in-context learning allows large language models (LLMs) to perform tasks using only provided instructions. However, pre-trained LLMs often face calibration issues in zero-shot scenarios, leading to challenges such as hallucinations and off-target translations that compromise output quality, particularly in machine translation (MT). This paper introduces a new method to improve zero-shot MT using fixed prefix pair bootstrapping. By initializing translations with an accurate bilingual prefix pair at the start of both source and target sentences, this approach effectively guides the model to generate precise target-language outputs. Extensive evaluations across four model architectures and multiple translation directions demonstrate significant and consistent improvements, showcasing the potential of this straightforward strategy to enhance zero-shot MT performance.

Enhanced Zero-Shot Machine Translation via Fixed Prefix Pair Bootstrapping

Wenzhou dialect is a Chinese dialect that is significantly distinct from Mandarin, the official language of China, and one of the most complex Chinese dialects. It is nearly incomprehensible to people from regions such as Northern China, creating substantial communication barriers. Therefore, the conversion between Wenzhou dialect and Mandarin is essential to facilitate communication between Wenzhou dialect speakers and people from other regions. However, as a low-resource language, Wenzhou dialect lacks publicly available datasets, and such conversion technologies have not been extensively researched. To address this issue, in this study, we create a parallel dataset containing Wenzhou dialect speech and corresponding Mandarin text and build benchmark models for Wenzhou dialect speech to Mandarin text conversion. In particular, we fine-tune two self-supervised learning-based pre-trained models, TeleSpeech-ASR1.0 and Wav2Vec2-XLS-R, with our training dataset and report their performance on our test dataset as baselines for future research. Our dataset and models are released.

Wenzhou Dialect Speech to Mandarin Text Conversion

Low-resource machine translation (MT) presents a diversity of community needs and application challenges that remain poorly understood. To complement surveys and focus groups, which tend to rely on small samples of respondents, we propose an observational study on actual usage patterns of a specialized MT service for the Tetun language, which is the lingua franca in Timor-Leste. Our analysis of 100,000 translation requests reveals patterns that challenge assumptions based on existing corpora. We find that users, many of them students on mobile devices, typically translate text from a high-resource language into Tetun across diverse domains including science, healthcare, and daily life. This contrasts sharply with available Tetun corpora, which are dominated by news articles covering government and social issues.Our results suggest that MT systems for institutionalized minority languages like Tetun should prioritize accuracy on domains relevant to educational contexts, in the high-resource to low-resource direction. More broadly, this study demonstrates how observational analysis can inform low-resource language technology development, by grounding research in practical community needs.

Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation service

Subword tokenization, where text is represented in an intermediate form between full words and characters, is ubiquitous in modern NLP due to its ability to represent any input sentence with a small vocabulary. However for Korean, where there are 11,172 base characters (*syllables*) in its alphabet, it is difficult to have a vocabulary large enough to succinctly encode text while fitting within parameter-budget constraints. This motivates us to explore an alternative representation for Korean which relies on the decompositional nature of Korean syllables: a syllable can be uniquely decomposed into a sequence of two or three subcharacters (*jamo*), of which there are only 68.Using jamo as the basis for subword tokenization (e.g., byte-pair encoding) leads to shorter tokenized sequences with fewer vocabulary parameters, exposes the model to sub-syllable-level morphological information, and increases the amount of augmentation gained from subword regularization. We evaluate jamo-level subword tokenization on several Korean translation tasks and find that jamo-level subword models consistently outperform syllable- and byte-level models in low-resource and restricted-vocabulary settings.

Jamo-Level Subword Tokenization in Low-Resource Korean Machine Translation

We introduce ModeLing, a novel benchmark of Linguistics Olympiad-style puzzles which tests few-shot reasoning in AI systems. Solving these puzzles necessitates inferring aspects of a language's grammatical structure from a small number of examples. Such puzzles provide a natural testbed for language models, as they require compositional generalization and few-shot inductive reasoning. Consisting solely of new puzzles written specifically for this work, ModeLing has no risk of appearing in the training data of existing AI systems: this ameliorates the risk of data leakage, a potential confounder for many prior evaluations of reasoning. Evaluating several large open source language models and GPT on our benchmark, we observe non-negligible accuracy, demonstrating few-shot emergent reasoning ability which cannot merely be attributed to shallow memorization. However, imperfect model performance suggests that ModeLing can be used to measure further progress in linguistic reasoning.

ModeLing: A Novel Dataset for Testing Linguistic Reasoning in Language Models

Domain adaptation in Neural Machine Translation (NMT) is commonly achieved through fine-tuning, but this approach becomes inefficient as the number of domains increases. Knowledge distillation (KD) provides a scalable alternative by training a compact model on distilled data from a larger model. However, we hypothesize that vanilla sequence-level KD primarily distills the decoder while neglecting encoder knowledge, leading to suboptimal knowledge transfer and limiting its effectiveness in low-resource settings, where both data and computational resources are constrained. To address this, we propose an improved sequence-level KD method that enhances encoder knowledge transfer through a cosine-based alignment loss. Our approach first trains a large model on a mixed-domain dataset and generates a Distilled Mixed Dataset (DMD). A small model is then trained on this dataset via sequence-level KD with encoder alignment. Experiments in a low-resource setting validate our hypothesis, demonstrating that our approach outperforms vanilla sequence-level KD, improves generalization to out-of-domain data, and facilitates efficient domain adaptation while reducing model size and computational cost.

Encoder-Aware Sequence-Level Knowledge Distillation for Low-Resource Neural Machine Translation

We introduce a new dataset for machine translation of Guinea-Bissau Creole (Kiriol), comprising around 40 thousand parallel sentences to English and Portuguese. This dataset is made up of predominantly religious data (from the Bible and texts from the Jehovah's Witnesses), but also a small amount of general domain data (from a dictionary). This mirrors the typical resource availability of many low resource languages, and we train a number of transformer-based models to investigate how to improve domain transfer from religious data to a more general domain. We find that adding even 300 sentences from the target domain when training substantially improves the translation performance, highlighting the importance and need for data collection for low-resource languages, even on a small-scale. We additionally find that Portuguese-to-Kiriol translation models perform better on average than other language pairs and directions, and investigate how this is related to the morphological complexity of the languages involved and the degree of lexical overlap between creoles and their lexifier languages. Overall, we hope our work will stimulate research into Kiriol, and into the translation of creole languages in general.

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ANSR@DravidianLangTech 2025: Detection of Abusive Tamil and Malayalam Text Targeting Women on Social Media using RoBERTa and XGBoost

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