Papers with topic classification
The Strength of the Weakest Supervision: Topic Classification Using Class Labels (N19-3)
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| Challenge: | a topic classifier can understand only class labels when training for tasks that require a large amount of labeled documents. |
| Approach: | They propose an algorithm that can initialize a topic classifier using only class labels . they propose a method that combines word embedding and naive Bayes classification . |
| Outcome: | The proposed approach saves significant initial labeling effort by providing a "warm start" the proposed approach can be fine-tuned with more labeled documents to reach a certain performance level. |
An Overview of the Active Gene Annotation Corpus and the BioNLP OST 2019 AGAC Track Tasks (D19-57)
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| Challenge: | Biomedical natural language processing (BioNLP) has long been recognized as effective method to accelerate drug-related knowledge discovery. |
| Approach: | They developed an active gene annotation corpus (AGAC) to support drug repurposing. |
| Outcome: | The active gene annotation corpus (AGAC) was developed to support knowledge discovery for drug repurposing. |
SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects (2024.eacl-long)
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David Adelani, Hannah Liu, Xiaoyu Shen, Nikita Vassilyev, Jesujoba Alabi, Yanke Mao, Haonan Gao, En-Shiun Lee
| Challenge: | despite progress in building multilingual language models evaluation is limited to a few languages with available datasets . despite this, we create a large-scale open-sourced benchmark dataset for topic classification in 205 languages and dialects to address the lack of evaluation dataset for Natural Language Understanding (NLU). |
| Approach: | They create a large-scale open-sourced benchmark dataset for topic classification in 205 languages and dialects to address the lack of evaluation dataset for Natural Language Understanding (NLU). |
| Outcome: | The proposed dataset addresses the lack of evaluation dataset for Natural Language Understanding (NLU) for many languages, it is the first publicly available evaluation dataset. |
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots (2021.naacl-industry)
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| Challenge: | E-commerce has grown rapidly over the last several years, and chatbots for intelligent customer service are simultaneously drawing attention. |
| Approach: | They propose a framework to obtain proper answer to customers’ emotional questions using emotion classification model and text matching. |
| Outcome: | The proposed framework is very promising on real online systems. |
Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages (2025.acl-srw)
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| Challenge: | Low-resource languages (LRLs) face significant challenges in natural language processing due to limited data. |
| Approach: | They evaluate adapter-based methods for adapting mLMs to low-resource languages . they use unstructured text and structured knowledge from ConceptNet to evaluate adapters . |
| Outcome: | The proposed methods outperform large language models and LLaMA-3 and deepSeek-R1 models on low training data. |
CTM - A Model for Large-Scale Multi-View Tweet Topic Classification (2022.naacl-industry)
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| Challenge: | Existing methods to classify social media posts into topics have been used to class up documents into topics. |
| Approach: | They propose a neural model that automatically associates social media posts with topics to solve these challenges. |
| Outcome: | The proposed model outperforms existing methods in the context of Twitter where the topic space is 10 times larger with potentially multiple topic associations per Tweet. |
LTV: Labeled Topic Vector (C18-2)
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| Challenge: | Using nnDDC, we generate labeled topic classifications based on the Dewey Decimal Classification (DDC) Unlike related approaches, we use classifiers to define the dimensions of CISS, which are directly labeles by the underlying target class. |
| Approach: | They propose a website and API that generates labeled topic classifications based on the Dewey Decimal Classification (DDC) they propose nnDDC, a largely language-independent natural network-based classifier for DDC, which is language-dependent . |
| Outcome: | The proposed model is language-independent and performs well in 40 languages. |
GrEmLIn: A Repository of Green Baseline Embeddings for 87 Low-Resource Languages Injected with Multilingual Graph Knowledge (2025.findings-naacl)
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| Challenge: | Contextualized word embeddings are available for many languages, but their coverage is limited for low resourced languages. |
| Approach: | They propose a method that integrates multilingual graph knowledge into the embeddings to make them green. |
| Outcome: | The proposed method outperforms state-of-the-art embeddings on lexical similarity task while being parameter-free at inference time. |
Zero-Shot Text Classification via Self-Supervised Tuning (2023.findings-acl)
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| Challenge: | Existing solutions to zero-shot text classification use pre-trained language models or large-scale annotated data. |
| Approach: | They propose a self-supervised learning paradigm to solve zero-shot text classification tasks by tuning the language models with unlabeled data. |
| Outcome: | The proposed model outperforms the state-of-the-art models on 7 out of 10 tasks and is less sensitive to prompt design. |
HUE: Pretrained Model and Dataset for Understanding Hanja Documents of Ancient Korea (2022.findings-naacl)
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| Challenge: | Historical records in Korea before the 20th century were primarily written in Hanja, an extinct language based on Chinese characters. |
| Approach: | They present a dataset that includes tasks for attribution, topic classification, named entity recognition and summary retrieval for classical Hanja documents. |
| Outcome: | The proposed models improve on the Annals of the Joseon Dynasty and Diaries of the Royal Secretariats datasets. |
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages (2020.emnlp-main)
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| Challenge: | Recent studies show that results from high-resource languages cannot be easily transferred to realistic, low-resourced scenarios. |
| Approach: | They analyse performance of multilingual transformer models using available resources for Hausa, isiXhosa and NER and topic classification. |
| Outcome: | The proposed models can achieve with as little as 10 or 100 labeled sentences the same performance as baselines with much more supervised training data. |
Language Model-Driven Data Pruning Enables Efficient Active Learning (2026.findings-eacl)
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| Challenge: | Existing data pruning methods for active learning are expensive and time-consuming. |
| Approach: | They propose a plug-and-play data pruning strategy that leverages language models to prune the unlabeled pool. |
| Outcome: | The proposed pruning strategy outperforms existing pruning methods on translation, sentiment analysis, topic classification, and summarization tasks on diverse datasets. |
Sentence-level Privacy for Document Embeddings (2022.acl-long)
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| Challenge: | Existing studies on the definitions of good privacy for natural language use argue that different applications and models require different definitions. |
| Approach: | They propose a technique that uses a combination of statistics and language modeling to produce high (768) dimensional, general -SentDP document embeddings that guarantee a single sentence can be substituted with any other sentence. |
| Outcome: | The proposed method outperforms baseline methods with weaker guarantees like word-level Metric DP and outperformed baseline methods. |
Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling (2021.naacl-main)
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| Challenge: | Topic models can augment or replace bag-of-words inputs with pre-trained transformer-based word prediction models. |
| Approach: | They propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual topic modeling. |
| Outcome: | The proposed methods improve both monolingual and zero-shot polylingual topic modeling. |
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport (2023.findings-acl)
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| Challenge: | Recent Neural Topic Models (NTMs) have limited applications in the real world due to the challenge of incorporating human knowledge. |
| Approach: | They propose a semi-supervised neural topic modeling method, vONTSS, which uses von Mises-Fisher variational autoencoders and optimal transport. |
| Outcome: | The proposed method outperforms existing semi-supervised topic modeling methods on multiple aspects. |
Twitter Topic Classification (2022.coling-1)
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Dimosthenis Antypas, Asahi Ushio, Jose Camacho-Collados, Vitor Silva, Leonardo Neves, Francesco Barbieri
| Challenge: | Existing methods to identify topics from posts are difficult to interpret and can differ from corpus to corpus. |
| Approach: | They propose a task based on tweet topic classification and release two datasets that can be used to train and test models. |
| Outcome: | The proposed task is based on two datasets from recent time periods and provides training and testing data. |
Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects (2026.acl-long)
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| Challenge: | Research on cross-dialectal transfer from a standard to a non-standard dialect variety has typically focused on text data. |
| Approach: | They compare standard-to-dialect transfer in three settings: text models, speech models, and cascaded systems where speech first gets automatically transcribed and then further processed by a text model. |
| Outcome: | The proposed model performs best on German dialect data while the text-only model perform best on the standard data. |
Explaining NLP Models via Minimal Contrastive Editing (MiCE) (2021.findings-acl)
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| Challenge: | Cognitive science and philosophy research has shown that human explanations are contrastive . a contrast case plays a key role in modulating what explanations can be given . |
| Approach: | They propose a method for producing contrastive explanations of model predictions . they edit models' outputs to change model outputs, and then edit them to the contrast case . |
| Outcome: | a new method produces contrastive explanations of model predictions in the form of edits . the edits are minimal and fluent, consistent with human contrastive edits. |
Evaluating Pixel Language Models on Non-Standardized Languages (2025.coling-main)
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| Challenge: | pixel-based models can be used to transfer learning from standard languages to dialects . pretrained language models achieve strong results for languages seen during training, but their performance declines with out-of-domain dialects. |
| Approach: | They compare pixel-based models to token-based ones to evaluate dialects . standard german is tokenized in a more meaningful way, whereas the Bern dialect is tokenize in pixel form . |
| Outcome: | The proposed models outperform token-based models in part-of-speech tagging, dependency parsing and intent detection for zero-shot dialect evaluation by up to 26 percentage points in some scenarios, though not in Standard German. |
In-Context Learning with Iterative Demonstration Selection (2024.findings-emnlp)
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| Challenge: | Existing literature has highlighted the importance of selecting examples that are diverse or semantically similar to the test sample . Existing studies have shown that the optimal selection dimension, i.e., diversity or similarity, is task-specific. |
| Approach: | They propose to use zero-shot chain-of-thought reasoning to iteratively select examples that are diverse but still strongly correlated with the test sample as ICL demonstrations. |
| Outcome: | The proposed method outperforms existing demonstration selection methods on reasoning, question answering, and topic classification tasks. |
Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter (2025.findings-acl)
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| Challenge: | Prior work on cross-lingual transfer often focuses on a small set of languages from a few language families and/or a single task. |
| Approach: | They analyze cross-lingual transfer for 263 languages from a wide variety of language families . they include three popular NLP tasks: POS tagging, dependency parsing, topic classification . |
| Outcome: | The proposed approach is based on linguistic similarity measures for 263 languages . the results show that the effect of linguistic similarities on transfer performance depends on a range of factors . |
ltzGLUE: Luxembourgish General Language Understanding Evaluation (2026.findings-acl)
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Alistair Plum, Felicia Körner, Anne-Marie Lutgen, Laura Bernardy, Fred Philippy, Emilia Milano, Nils Rehlinger, Cedric Lothritz, Tharindu Ranasinghe, Barbara Plank, Christoph Purschke
| Challenge: | ltzGLUE is the first official NLU benchmark for Luxembourgish (LTZ) based on the popular GLUE benchmark for English. |
| Approach: | They propose a new natural language understanding (NLU) benchmark for Luxembourgish based on the popular GLUE benchmark for English. |
| Outcome: | The proposed model performs well across many languages and is based on the GLUE benchmark for English. |
Prototypical Verbalizer for Prompt-based Few-shot Tuning (2022.acl-long)
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| Challenge: | Prompt-based tuning for pre-trained language models has shown its effectiveness in few-shot learning. |
| Approach: | They propose a prototypical verbalizer which learns prototype vectors as verbalizes by contrastive learning. |
| Outcome: | The proposed verbalizer outperforms existing verbalizing methods on topic classification and entity typing tasks. |
Enhancing Few-Shot Topic Classification with Verbalizers. a Study on Automatic Verbalizer and Ensemble Methods (2024.lrec-main)
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Quang Anh Nguyen, Nadi Tomeh, Mustapha Lebbah, Thierry Charnois, Hanene Azzag, Santiago Cordoba Muñoz
| Challenge: | Pretrained language models are increasingly being used for many tasks. |
| Approach: | They propose to use verbalizers to help interpret masked word distributions into output predictions. |
| Outcome: | The proposed approach outperforms models trained with individual templates while using significantly less resources. |
Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering (2021.acl-long)
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| Challenge: | Currently, language-equipped vision systems such as VizWiz, TapTapSee, BeMyEyes, and CamFind are actively being deployed across a broad spectrum of users. |
| Approach: | They propose to identify collective outliers in active learning methods that are hard and often impossible for models to learn . they also propose to use visual inputs to identify these outlier examples as examples assigned low model confidence and prediction variability during training. |
| Outcome: | The proposed methods outperform random selection on visual question answering tasks. |
ResFormer: All-Time Reservoir Memory for Long Sequence Classification (2025.emnlp-main)
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| Challenge: | Existing models with quadratic time and memory complexity restrict input length . however, analyzing extensive sequential contexts is challenging . |
| Approach: | They propose a neural network architecture that captures contextual dependencies in linear time and a nonlinear readout to model short-term dependencies within sentences. |
| Outcome: | The proposed model outperforms baseline models on EmoryNLP datasets and on IEMOCAP and MultiWOZ datasets. |
Label Semantic Aware Pre-training for Few-shot Text Classification (2022.acl-long)
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| Challenge: | Existing models for text classification use label semantics but few studies have attempted to give models access to informative representations of labels. |
| Approach: | They propose to use label semantics to train generative models by performing secondary pre-training on labeled sentences from a variety of domains. |
| Outcome: | The proposed approach improves generalization and data efficiency of text classification systems while maintaining comparable performance to state-of-the-art models. |
Give your Text Representation Models some Love: the Case for Basque (2020.lrec-1)
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Rodrigo Agerri, Iñaki San Vicente, Jon Ander Campos, Ander Barrena, Xabier Saralegi, Aitor Soroa, Eneko Agirre
| Challenge: | Word embeddings and pre-trained language models are expensive to train and are often used by small companies and research groups to build their own. |
| Approach: | They propose to use word embeddings and pre-trained language models to build rich representations of text and improve NLP tasks. |
| Outcome: | The proposed models perform better than publicly available versions in downstream NLP tasks for Basque. |
A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot (2023.emnlp-main)
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| Challenge: | Existing annotated training datasets hinder development of supervised learning models for multimedia content . lack of annotating benchmarks hinders development of models with satisfactory performance . a recent study shows that large language models have zero-shot performance in multimedia understanding . |
| Approach: | They propose to verbalize long videos to generate their descriptions in natural language . they then perform video-understanding tasks on the generated story as opposed to the original video . |
| Outcome: | The proposed method achieves better results than baselines for video understanding. |
Hierarchical Multi-Label Classification of Scientific Documents (2022.emnlp-main)
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| Challenge: | Automated topic classification is a useful tool for managing scientific documents in a digital collection. |
| Approach: | They propose a hierarchical multi-label text classification dataset with keyword labeling as an auxiliary task. |
| Outcome: | The proposed model achieves a Macro-F1 score of 34.57% and is publicly available. |
Estimating Confidence of Predictions of Individual Classifiers and TheirEnsembles for the Genre Classification Task (2022.lrec-1)
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| Challenge: | Genre identification is a kind of non-topic text classification. genre is defined as a functional space. |
| Approach: | They propose to use SOTA to identify genres in non-topic texts . genres are functional and cannot be expressed just by some keywords . |
| Outcome: | The proposed models show that they perform better than their individual models in large datasets. |
2kenize: Tying Subword Sequences for Chinese Script Conversion (2020.acl-main)
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| Challenge: | Traditional Chinese character conversion is a common step in Chinese NLP but current methods do not take into account that a simplified Chinese character can correspond to multiple traditional characters. |
| Approach: | They propose a model that can disambiguate between mappings and convert between the two scripts by using subword segmentation and two language models. |
| Outcome: | The proposed model outperforms previous Chinese Character conversion approaches by 6 points in accuracy. |
Polyglot Prompt: Multilingual Multitask Prompt Training (2022.emnlp-main)
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| Challenge: | a monolithic framework for multilingual learning can be used without any task/language-specific module. |
| Approach: | They propose a framework to exploit prompting methods for learning a unified semantic space for different languages and tasks with multilingual prompt engineering. |
| Outcome: | The proposed framework can learn tasks from different languages in a monolithic framework without any task/language-specific module. |
Good or Bad News? Exploring GPT-4 for Sentiment Analysis for Faroese on a Public News Corpora (2024.lrec-main)
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| Challenge: | Existing studies on sentiment analysis in low-resource languages have focused on major languages and emotionally laden text genres like social media and reviews. |
| Approach: | They propose to use GPT-4 for sentiment analysis on Faroese news texts using a multi-class approach with 225 sentences analysed in 170 articles. |
| Outcome: | The proposed model performs remarkably well on 225 sentences and 170 articles compared to human annotators . |
Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Models (2024.findings-emnlp)
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| Challenge: | Prompt-based learning is susceptible to intrinsic bias present in pre-trained language models (LMs), leading to sub-optimal performance in prompt-based zero/few-shot settings. |
| Approach: | They propose a null-input prompting method to calibrate intrinsic bias encoded in pre-trained language models (LMs) they leverage a diverse set of auto-selected null meaning inputs generated from GPT-4 to probe intrinsic bias. |
| Outcome: | The proposed method significantly improves zero/few-shot learning performance of LMs for both in-context learning and prompt-based fine-tuning (on average 9% and 2%, respectively). |
Labeling Results of Topic Models: Word Sense Disambiguation as Key Method for Automatic Topic Labeling with GermaNet (2024.lrec-main)
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| Challenge: | Existing methods for topic modeling are not suitable for document classification, but they can be used to generate training data from large corpus. |
| Approach: | They propose to use topic modeling and automatic topic labeling to understand large corpora of text. |
| Outcome: | The proposed method is more accurate than existing methods. |
Inference and Verbalization Functions During In-Context Learning (2024.findings-emnlp)
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| Challenge: | Previous work has found that, in some settings, ICL performance is minimally affected by using demonstrations with irrelevant label words. |
| Approach: | They hypothesize that large language models (LMs) perform in-context learning from a handful of demonstrations via two sequential processes: an inference function that solves the task and a verbalization function that maps the inferred answer to the label space. |
| Outcome: | The proposed model can be localized in specific layers across open-source models, including GEMMA-7B, MISTRAL-7B-V0.3, GEIMA-2-27B, and LLAMA-3.1-70B. |
SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation (2024.emnlp-main)
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| Challenge: | Prior approaches to synthesis use few-shot prompting, which relies on the LLM’s parametric knowledge to generate usable examples. |
| Approach: | They propose to use a dataset to generate examples of each label from the LLM. |
| Outcome: | The proposed model significantly improves lexical and semantic diversity, similarity to human-written text, and distillation performance, when compared to 32-shot prompting and four prior approaches. |
Multilingual Topic Classification in X: Dataset and Analysis (2024.emnlp-main)
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| Challenge: | Social media platforms such as X (Twitter), Snapchat and Instagram provide an environment for content creation and information sharing. |
| Approach: | They propose a multilingual dataset featuring tweet topic classification in four languages . they leverage X-Topic to perform cross-linguistic and multilingual analysis . |
| Outcome: | The proposed dataset includes topics in four languages and is useful for cross-linguistic analysis and the development of robust multilingual models. |
Tokenization and Representation Biases in Multilingual Models on Dialectal NLP Tasks (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) pre-trained on massive text data in many languages are preferred solution for various Natural Language processing tasks. |
| Approach: | They compare tokenization parity and information parity as representational biases in pre-trained models . they find TP is better predictor of performance on tasks reliant on syntactic and morphological cues . |
| Outcome: | The proposed model improves on dialect classification, topic classification, and extractive question answering tasks. |
Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance (2025.emnlp-main)
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Xueqing Peng, Triantafillos Papadopoulos, Efstathia Soufleri, Polydoros Giannouris, Ruoyu Xiang, Yan Wang, Lingfei Qian, Jimin Huang, Qianqian Xie, Sophia Ananiadou
| Challenge: | Greek is the dominant language of the world's merchant navy and is a key language for international trade. |
| Approach: | They propose to develop a Greek financial evaluation benchmark and a financial LLM fine-tuned on Greek-specific financial data to bridge this gap. |
| Outcome: | The proposed benchmarks surpass GPT-4 by 8.33%, GPT- 4o by 26.83%, and Deepseek-V3 by 67.74%. |