Papers with low-resource settings
Recycling a Pre-trained BERT Encoder for Neural Machine Translation (D19-56)
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| Challenge: | In monolingual tasks, the number of unlearned model parameters is as huge as the number learned parameters in the BERT model. |
| Approach: | They propose to apply a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model to Transformer-based neural machine translation (NMT) based on the Transformer. |
| Outcome: | The proposed model is stable and efficient in low-resource settings. |
Measuring the Value of Linguistics: A Case Study from St. Lawrence Island Yupik (P19-2)
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| Challenge: | a recent study has called into question the utility of linguistics in the development of computational systems. |
| Approach: | a new research proposes to integrate linguistics into a neural morphological analyzer for a polysynthetic language . the researchers propose to use linguistic elements to improve performance in low-resource settings . |
| Outcome: | The proposed analysis shows that linguistics can improve performance in low-resource and high-resolution settings. |
Learning with Limited Text Data (2022.acl-tutorials)
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| Challenge: | Natural Language Processing (NLP) relies on labeled data to perform state-of-the-art performance . labeles are often required to label large amounts of textual data . this tutorial will provide an overview of labeleing in NLP . |
| Approach: | This tutorial will provide a systematic overview of methods for learning from limited labeled data. |
| Outcome: | This tutorial will provide a systematic and up-to-date overview of the proposed methods . it will highlight current challenges and future directions . |
PolyNorm: Few-Shot LLM-Based Text Normalization for Text-to-Speech (2025.emnlp-industry)
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| Challenge: | Text Normalization (TN) is a key preprocessing step in Text-to-Speech systems. |
| Approach: | They propose a prompt-based approach to TN using Large Language Models (LLMs) they propose scalable experimentation across languages to reduce the reliance on manual rules . |
| Outcome: | The proposed approach reduces the reliance on manual rules and enables broader linguistic applicability with minimal human intervention across eight languages. |
Simulated multiple reference training improves low-resource machine translation (2020.emnlp-main)
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| Challenge: | Existing valid translations for a given sentence are limited by a single reference translation, causing data sparsity in low-resource settings. |
| Approach: | They propose a method that approximates the full space of possible translations by sampling a paraphrase of the reference sentence from a MT model and training it to predict the paraphraser’s distribution over possible tokens. |
| Outcome: | The proposed method improves in low-resource settings and is complementary to back-translation. |
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (2023.tacl-1)
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| Challenge: | Existing methods for enhancing data efficiency in limited labeled data are limited. |
| Approach: | They propose to use data augmentation methods to increase the efficiency of limited data learning in NLP. |
| Outcome: | The proposed methods perform well on topics/news classification, inference tasks, paraphrasing tasks, and single-sentence tasks. |
Low-resource neural machine translation with morphological modeling (2024.findings-naacl)
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| Challenge: | Existing methods for character-based and sub-word tokenization are limited to the surface forms of the words. |
| Approach: | They propose a framework-solution for modeling complex morphology in low-resource settings using a transformer architecture and beam search-based decoder. |
| Outcome: | The proposed model improves translation performance on Kinyarwanda English translation using public-domain parallel text. |
Privacy-Preserving Federated Learning for Hate Speech Detection (2025.naacl-srw)
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| Challenge: | a federated learning system with differential privacy is tailored to low-resource languages . data with fewer than 20 sentences per client struggled due to excessive noise . |
| Approach: | They propose a federated learning system with differential privacy for hate speech detection . they fine-tuned pre-trained language models to find it to be the most effective . |
| Outcome: | The proposed learning system outperforms other models in low-resource languages . balanced datasets and augmenting hateful data with non-hateful examples proved critical . |
TALEN: Tool for Annotation of Low-resource ENtities (P18-4)
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| Challenge: | Named entity recognition (NER) is a task that requires a large amount of training data and annotators who do not speak the language are hard or impossible to find. |
| Approach: | They propose a web-based interface for named entity annotation in low-resource settings . TALEN includes in-place lexicon integration, TF-IDF token statistics, Internet search, and entity propagation . |
| Outcome: | The proposed interface performs better than a popular annotation tool and is more accurate and recall-rich than the current one. |
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages (2021.eacl-srw)
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| Challenge: | Neural dependency parsing has been a success for many domains and languages, but the bottleneck of massive labelled data limits its effectiveness for low resource languages. |
| Approach: | They propose to use morphological knowledge to improve dependency parsing for morphology rich languages in a low-resource setting to perform experiments. |
| Outcome: | The proposed method achieves an average gain of 2 points (UAS) and 3.6 points (LAS) on 10 MRLs in low-resource settings. |
CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translation (2022.acl-long)
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| Challenge: | a novel data-augmentation technique for neural machine translation is based on a letter substitution cipher . a bijective ciphered text is in effect invisible to modern NLP techniques because of its invariant distributional features . |
| Approach: | They propose a data-augmentation technique for neural machine translation based on ROT-k ciphertexts. |
| Outcome: | The proposed method outperforms existing methods on several datasets by a significant margin. |
Automated Screening of Antibacterial Nanoparticle Literature: Dataset Curation and Model Evaluation (2026.eacl-long)
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Alperen Ozturk, Şaziye Betül Özateş, Sophia Bahar Root, Angela Violi, Nicholas Kotov, J. Scott VanEpps, Emine Sumeyra Turali Emre
| Challenge: | Antimicrobial resistance is a growing global health threat, driving interest in nanoparticle-based alternatives to conventional antibiotics. |
| Approach: | They propose to use machine learning to classify scientific abstracts using inorganic nanoparticles with intrinsic antibacterial properties. |
| Outcome: | The proposed method distinguishes intrinsic antibacterial NPs from studies focusing on drug carriers or surface-bound applications. |
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference (2021.eacl-main)
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| Challenge: | Existing approaches to learning from examples are limited due to the vast number of languages, domains and tasks. |
| Approach: | They propose a semi-supervised training procedure that reformulates input examples as cloze-style phrases to help language models understand a given task. |
| Outcome: | The proposed approach outperforms supervised training and strong semi-supervised approaches in low-resource settings by a large margin. |
Distilling Text Style Transfer With Self-Explanation From LLMs (2024.naacl-srw)
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| Challenge: | Text Style Transfer (TST) aims to alter the style of text while preserving its core content. |
| Approach: | They propose a framework that leverages large language models alongside chain-of-thought prompting to facilitate TST. |
| Outcome: | The proposed framework surpasses supervised fine-tuning and knowledge distillation methods in low-resource settings. |
Revisiting Low-Resource Neural Machine Translation: A Case Study (P19-1)
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| Challenge: | Recent research has shown that neural machine translation models are highly data-inefficient and underperform phrase-based statistical machine translation (PBSMT) in low-resource settings. |
| Approach: | They propose to use auxiliary data to train low-resource neural machine translation systems without auxiliary monolingual or multilingual data. |
| Outcome: | The proposed methods outperform PBSMT and other statistical machine translation models in Korean–English with minimal data. |
LINSPECTOR WEB: A Multilingual Probing Suite for Word Representations (D19-3)
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| Challenge: | LINSPECTOR WEB is an open source multilingual inspector to analyze word embeddings. |
| Approach: | They propose to use LINSPECTOR WEB to analyze word embeddings in 28 languages. |
| Outcome: | The system performs 16 simple linguistic probing tasks for a diverse set of 28 languages. |
Effectively Aligning and Filtering Parallel Corpora under Sparse Data Conditions (2020.acl-srw)
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| Challenge: | Parallel corpora are key to developing good machine translation systems, but abundant parallel data is hard to come by for languages with a low number of speakers. |
| Approach: | They propose an unsupervised alignment method that can handle rich morphology by removing incorrect translations and segments containing extraneous data. |
| Outcome: | The proposed method maximizes the number of correctly translated segments in a corpus and minimises noise by removing incorrect translations and segments containing extraneous data. |
Using Interpretation Methods for Model Enhancement (2023.emnlp-main)
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| Challenge: | Existing frameworks for enhancing neural models with interpretation methods and gold rationales have not been fully explored. |
| Approach: | They propose a framework for utilizing interpretation methods and gold rationales to enhance neural models. |
| Outcome: | The proposed framework outperforms gradient-based methods in low-resource settings on a variety of tasks. |
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)
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| Challenge: | Existing approaches to extract relational facts from text are limited in their ability to learn from limited labeled data. |
| Approach: | They propose to use prompt-based methods with few-shot labeled data to evaluate performance . data augmentation technologies and self-training are also proposed to generate more labeles in-domain data. |
| Outcome: | The proposed methods perform well in low-resource settings with 8 relation extraction datasets. |
UrBLiMP: A Benchmark for Evaluating the Linguistic Competence of Large Language Models in Urdu (2026.findings-acl)
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| Challenge: | Evaluating how large language models capture grammatical structure of low-resource languages remains underexplored. |
| Approach: | They evaluate a set of 5,696 minimal pairs that contrast grammatical acceptability across ten core syntactic and morpho-syntactical phenomena in Urdu. |
| Outcome: | The proposed framework compares multilingual models with the proprietary model . the proposed framework achieves the highest average accuracy on regular phenomena . |
Combining Weakly Supervised ML Techniques for Low-Resource NLU (2021.naacl-industry)
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| Challenge: | Recent advances in transfer learning have improved the performance of virtual assistants . however, meager training data is often a key bottleneck in creating voice-enabled applications . |
| Approach: | They propose to use unsupervised and semi-supervised techniques to improve NLU accuracy . they incorporate anonymized, unlabeled and automatically transcribed user utterances into training . |
| Outcome: | The proposed methods improve NLU accuracy in low-resource settings by integrating unsupervised and SSL techniques. |
MA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition (2025.acl-srw)
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Shanshan Liu, Noriki Nishida, Rumana Ferdous Munne, Narumi Tokunaga, Yuki Yamagata, Kouji Kozaki, Yuji Matsumoto
| Challenge: | Existing concepts recognition methods that rely on explicit mention identification fail to capture complex concepts not explicitly stated in the text. |
| Approach: | They propose a framework that reformulates concept recognition as an indexing-recognition task. |
| Outcome: | The proposed framework reduces computational requirements and improves recognition efficiency in low-resource settings. |
HyperT5: Towards Compute-Efficient Korean Language Modeling (2023.acl-industry)
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| Challenge: | Pretraining and fine-tuning language models is a common practice in NLP, but deploying general-purpose language models without the abundant computation or data resources is proving difficult. |
| Approach: | They propose a sequence-to-sequence language model architecture that can be more practical and compute-efficient than the decoder-oriented approach. |
| Outcome: | The proposed language model outperforms competing models in Korean benchmarks and is more efficient in low-resource settings. |
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems (N19-1)
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| Challenge: | Existing methods to regularize neural machine translation are limited in low-resource settings. |
| Approach: | They propose a method that uses regressing word embeddings to regularize neural machine translation. |
| Outcome: | The proposed system improves on a strong baseline and a state-of-the-art system. |
Rosetta-PL: Propositional Logic as a Benchmark for Large Language Model Reasoning (2025.naacl-srw)
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Shaun Lee Baek, Shaun Esua-Mensah, Cyrus Tsui, Sejan Vigneswaralingam, Abdullah Alali, Michael Lu, Vasu Sharma, Kevin Zhu
| Challenge: | Large Language Models (LLMs) are primarily trained on high-resource natural languages, limiting their effectiveness in low-resourced settings and in tasks requiring deep logical reasoning. |
| Approach: | They propose to use a dataset of logical propositions from Lean into a custom logical language to evaluate LLMs' logical reasoning and generalization capabilities in a controlled environment. |
| Outcome: | The proposed model improves accuracy and accuracy beyond 20,000 training samples. |
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation (C18-1)
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| Challenge: | Recent studies have shown that multilingual NMT models can handle more than one translation direction with a single system. |
| Approach: | They propose a multilingual neural machine translation model that can handle more than one translation direction with a single system. |
| Outcome: | The proposed model performs well in low-resource settings against bilingual systems. |
Leveraging Moment Injection for Enhanced Semi-supervised Natural Language Inference with Large Language Models (2025.naacl-short)
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| Challenge: | Existing studies have used class-specific fine-tuned large language models to generate hypotheses and assign pseudo-labels but discarded many LLM-constructed samples to ensure the quality. |
| Approach: | They propose to leverage LLM-constructed samples by injecting the moments of labeled samples during training to properly adjust the level of noise. |
| Outcome: | The proposed method outperforms strong baselines on multiple NLI datasets in low-resource settings. |
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning (2024.naacl-short)
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| Challenge: | Relation classification (RC) models extract rich information from sentences with limited labeled instances. |
| Approach: | They propose to combine multiple sentence representations with contrastive learning to enhance information extraction by combining multiple sentence and entity tokens. |
| Outcome: | The proposed approach is able to extract discriminative information from multiple representations and contrastive learning. |
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing (2022.aacl-short)
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| Challenge: | Large Language Models excel at a low-resource level given limited data, but are unsuitable for runtime systems which require low latency. |
| Approach: | They propose a method to augment training data for a model 40x smaller (500M parameters) they use Alexa to generate synthetic data from Alexa 20B to augment the training set . |
| Outcome: | The proposed method improves low-resource SP on two datasets in low-source settings. |
Robust Cross-Lingual Hypernymy Detection Using Dependency Context (N18-1)
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| Challenge: | Existing approaches to cross-lingual hypernymy detection are sparse and can be trained on related languages with negligible loss of performance. |
| Approach: | They propose a family of unsupervised approaches for cross-lingual hypernymy detection which learns sparse, bilingual word embeddings based on dependency contexts. |
| Outcome: | The proposed approach significantly improves performance on this task, compared to approaches based only on lexical context. |
Enhancing Clinical BERT Embedding using a Biomedical Knowledge Base (2020.coling-main)
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| Challenge: | Domain knowledge is important for building Natural Language Processing (NLP) systems for low-resource settings, such as in the clinical domain. |
| Approach: | They propose a joint method for adding knowledge base information from the Unified Medical Language System (UMLS) into language model pre-training for some clinical domain corpus. |
| Outcome: | The proposed method outperforms existing models on three clinical domain tasks with no knowledge base information. |
Cross-lingual Few-Shot Learning on Unseen Languages (2022.aacl-main)
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| Challenge: | Large pre-trained language models have demonstrated the ability to obtain good performance on downstream tasks with limited examples in resource-rich languages. |
| Approach: | They propose to use a downstream sentiment analysis task to analyze the effectiveness of several few-shot learning strategies across 12 languages, including 8 unseen languages, to compare results. |
| Outcome: | The proposed model, XLM-R, gives the best performance on a task with few examples in resource-rich languages. |
Annotating FrameNet via Structure-Conditioned Language Generation (2024.acl-short)
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| Challenge: | despite the remarkable generative capabilities of language models, their effectiveness on explicit manipulation and generation of linguistic structures remains understudied. |
| Approach: | They propose a framework to generate frame-semantically annotated sentences following FrameNet . they use explicit semantic information to generate frames with high human acceptance . |
| Outcome: | The proposed framework produces frame-semantic annotations with high human acceptance . generating high-quality, semantically rich data is effective in low-resource settings, but not under higher resource settings. |
TONY: an open-source TOolkit for Nlp in psYchology (2026.acl-demo)
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Federico Ravenda, Sofia Irene Ravenda, Volodymyr Karpenko, Daniele Montagnani, Andrea Raballo, Antonietta Mira
| Challenge: | Existing NLP tools are fragmented, closed-source, or difficult to use . a single sentence can convey emotion, social dynamics, cognitive states, and implicit attitudes . |
| Approach: | They propose an open-source python TOolkit for NLP in clinical psychology. |
| Outcome: | The TOolkit bridges traditional psycholinguistic analysis and modern NLP . it integrates interpretable lexical features with state-of-the-art lightweight transformer models . the toolkit is released under an open-source license and is evaluated through multiple MH–related datasets. |
A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling (P18-1)
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| Challenge: | Existing studies have shown that multi-task learning can boost the performance of related tasks such as MT and abstractive text summarization. |
| Approach: | They propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling. |
| Outcome: | The proposed architecture achieves 4.3%-50.5% absolute gains compared to mono-lingual model . the proposed model is particularly effective in low-resource settings . |
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification (2021.eacl-main)
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| Challenge: | Semi-supervised learning and multilingual pretraining have been shown to be effective for task-specific labelled data shortages. |
| Approach: | They propose to combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. |
| Outcome: | The proposed method outperforms state-of-the-art models in low-resource settings across several languages and outperformed existing models in English. |
Unsupervised Detection of LLM-Generated Text in Korean Using Syntactic and Semantic Cues (2026.findings-eacl)
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| Challenge: | Prior work focused on English, leaving low-resource languages such as Korean underexplored. |
| Approach: | They propose an unsupervised framework that integrates syntactic token cohesiveness and semantic regeneration similarity to detect Korean text. |
| Outcome: | The proposed framework outperforms baselines in Korean and other low-resource languages without training. |
Cross-lingual Text Classification with Heterogeneous Graph Neural Network (2021.acl-short)
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| Challenge: | Existing methods for cross-lingual text classification only consider factors beyond semantic similarity, causing performance degradation between some language pairs. |
| Approach: | They propose a method to incorporate heterogeneous information within and across languages for cross-lingual text classification using graph convolutional networks. |
| Outcome: | The proposed method significantly outperforms state-of-the-art models on all tasks and achieves consistent performance gain over baselines in low-resource settings. |
Controlling Language Confusion in Multilingual LLMs (2025.acl-srw)
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| Challenge: | Large language models suffer from language confusion, a phenomenon in which responses are partially or entirely generated in unintended languages. |
| Approach: | They propose a supervised fine-tuning methodology which optimizes the likelihood of correct tokens without explicitly penalizing undesired outputs such as cross-lingual mixing. |
| Outcome: | The proposed model suppresses language-confused generation while maintaining strong language consistency even under high decoding temperatures while preserving general QA performance. |
Promoting Graph Awareness in Linearized Graph-to-Text Generation (2021.findings-acl)
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| Challenge: | Recent applications of pretrained transformers to linearizations of graph inputs yield stateof-the-art results on graph-to-text tasks. |
| Approach: | They propose to use pretrained transformers to encode local graph structures . they find they can improve the quality of models' implicit graph encodings . |
| Outcome: | The proposed models can encode local graph structures and reconstruct corrupted inputs. |
One More Modality: Does Abstract Meaning Representation Benefit Visual Question Answering? (2025.findings-emnlp)
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| Challenge: | incorporating explicit semantic information, in the form of Abstract Meaning Representation graphs, can enhance VQA models. |
| Approach: | They augment two vision-language models with sentence- and document-level AMRs . they find that in well-resourced settings, models are negatively impacted by AMR . |
| Outcome: | The proposed model improves in well-resourced and low-resource settings with AMR graphs . the model achieves 13.1% relative gain using sentence-level AMRs compared with the smaller model . |
K-hop neighbourhood regularization for few-shot learning on graphs: A case study of text classification (2023.eacl-main)
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| Challenge: | We show that few-sample word-document graphs can be used for improved learning in low-resource settings. |
| Approach: | They propose a method to utilize word-document graph properties for improved learning in low-resource settings by using a regularizer for heterogeneous graphs. |
| Outcome: | The proposed method outperforms a baseline TextGCN with 17% accuracy over eight languages while performing on par with the state-of-the-art models. |
Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages (2021.naacl-main)
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| Challenge: | Unsupervised translation systems have impressive performance on resource-rich language pairs . however, in more realistic settings, unsupervised systems perform poorly . |
| Approach: | They propose a model for 5 low-resource languages that leverages monolingual and auxiliary parallel data from other high-resourced languages. |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource languages . it also matches the current state- of-the art model for Nepali-English . |
Joint Entity Extraction and Assertion Detection for Clinical Text (P19-1)
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| Challenge: | Existing systems for in-formation extraction treat negative medical findings as a pipeline of two separate tasks. |
| Approach: | They propose a multi-task neural model to jointly extract entities and negations from medical reports. |
| Outcome: | The proposed model performs considerably better than existing systems on a 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset. |
AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages (2025.naacl-long)
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Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Abinew Ali Ayele, David Ifeoluwa Adelani, Ibrahim Said Ahmad, Saminu Mohammad Aliyu, Paul Röttger, Abigail Oppong, Andiswa Bukula, Chiamaka Ijeoma Chukwuneke, Ebrahim Chekol Jibril, Elyas Abdi Ismail, Esubalew Alemneh, Hagos Tesfahun Gebremichael, Lukman Jibril Aliyu, Meriem Beloucif, Oumaima Hourrane, Rooweither Mabuya, Salomey Osei, Samuel Rutunda, Tadesse Destaw Belay, Tadesse Kebede Guge, Tesfa Tegegne Asfaw, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Seid Muhie Yimam, Nedjma Ousidhoum
| Challenge: | Hate speech and abusive language are global phenomena that need sociocultural background knowledge to be understood, identified, and moderated. |
| Approach: | They propose to use a multilingual dataset to collect hate speech and abusive language in 15 African languages to help improve model performance. |
| Outcome: | The proposed datasets are based on tweets annotated by native speakers familiar with the regional culture and show that they perform well in low-resource settings. |
Optimizing Instruction Synthesis: Effective Exploration of Evolutionary Space with Tree Search (2024.findings-emnlp)
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| Challenge: | Extensive research has highlighted the quality of instruction data is essential for the success of this alignment. |
| Approach: | They propose a framework for iteratively improving existing instruction data by using Monte Carlo tree search to find suitable prompts that align the language model to effectively learn multiple skills. |
| Outcome: | The proposed framework improves the evaluation scores of seed instruction data, raising the average evaluation scores from 2.19 to 3.81. |
Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation (N18-1)
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| Challenge: | Existing metrics to evaluate multilingual topic quality are inadequate for multilingual document analysis. |
| Approach: | They propose a new intrinsic evaluation metric for multilingual topic models that correlates well with human judgments of multilingual coherence and performance in downstream applications. |
| Outcome: | The proposed model improves the performance of multilingual topic models in low-resource languages and with human judgments of multilinguistic topic coherence. |
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning (2020.coling-main)
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| Challenge: | Recent work has explored the suitability of pre-trained language models in low resource settings with less than 1,000 training data points. |
| Approach: | They propose to use pool-based active learning to speed up training while keeping the cost of labeling new data constant. |
| Outcome: | The proposed model can be fine-tuned to optimize for low-resource settings while keeping the cost of labeling constant. |
A Cautious Generalization Goes a Long Way: Learning Morphophonological Rules (2023.acl-long)
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| Challenge: | Explicit linguistic knowledge encoded by rule-based morphological analyzers is expensive and non-trivial . creating such resources is tedious and requires additional efforts to extract human-interpretable patterns from them. |
| Approach: | They propose a method for automatically learning morphophonological rules of Arabic from a corpus. |
| Outcome: | The proposed approach produces a set of generalizable rules from a dataset. |
Low-Resource Compositional Semantic Parsing with Concept Pretraining (2023.eacl-main)
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| Challenge: | Semantic parsing is a key role in voice assistants by mapping natural language to structured meaning representations. |
| Approach: | They propose an architecture to perform domain adaptation automatically with only a small amount of metadata about the new domain and without any new training data. |
| Outcome: | The proposed architecture outperforms existing models in low-resource settings. |
FELIX: Flexible Text Editing Through Tagging and Insertion (2020.findings-emnlp)
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| Challenge: | FELIX is efficient in low-resource settings and fast at inference time, while being capable of modeling flexible input-output transformations. |
| Approach: | They propose a flexible text-editing approach that decomposes a text-generating task into two sub-tasks: tagging and insertion. |
| Outcome: | The proposed model is efficient in low-resource settings and fast at inference time while being capable of modeling flexible input-output transformations. |
Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues (2023.acl-long)
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| Challenge: | Existing studies on USM neglect explicitly modeling the user’s task goals fulfillment using the task schema. |
| Approach: | They propose a schema-guided user satisfaction modeling framework that explicitly models the degree to which the user’s preferences regarding task attributes are fulfilled by the system. |
| Outcome: | The proposed framework outperforms existing methods on benchmark datasets and shows that it can interpret and scale well with unseen tasks and can work in low-resource settings. |
AfriVox: Probing Multilingual and Accent Robustness of Speech LLMs (2026.eacl-long)
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Busayo Awobade, Mardhiyah Sanni, Tassallah Abdullahi, Chibuzor Okocha, Kelechi Ezema, Devendra Deepak Kayande, Lukman Enegi Ismaila, Tobi Olatunji, Gloria Ashiya Katuka
| Challenge: | Recent advances in multimodal and speech-native large language models have delivered impressive speech recognition, translation, understanding, and question-answering capabilities for high-resource languages. |
| Approach: | They propose to benchmark African languages and African-accented French, Arabic, and 100+ African English accents across 20 African languages. |
| Outcome: | The proposed model outperforms traditional speech transcription and translation models in African languages and non-native French or English accents. |
Achilles-Bench: A Challenging Benchmark for Low-Resource Evaluation (2024.findings-acl)
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| Challenge: | Existing low-resource datasets that challenge neural networks cause over-estimated performance, despite promising yet saturated results in high-res settings. |
| Approach: | They propose a benchmark Achilles-Bench to better evaluate the learning ability of neural networks in low-resource settings. |
| Outcome: | The proposed benchmarks show that even pre-trained language models show performance drops on NLP tasks. |
MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning (2026.findings-eacl)
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Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Tasnim Mohiuddin, Md Mofijul Islam, Swakkhar Shatabda
| Challenge: | Existing benchmarks primarily focus on English or a narrow subset of high-resource languages, leaving significant gaps in assessing multilingual and cross-lingual mathematical reasoning. |
| Approach: | They propose a parallel multilingual benchmark for mathematical problem solving and reasoning that encompasses 2,890 parallel Bangla-English gold standard artifacts. |
| Outcome: | The proposed model encompasses 2,890 parallel Bangla-English gold standard artifacts, totaling 30K aligned question–answer pairs across thirteen languages, representing high-, medium-, and low-resource linguistic settings. |
Evaluating the Diversity, Equity, and Inclusion of NLP Technology: A Case Study for Indian Languages (2023.findings-eacl)
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| Challenge: | In order for NLP technology to be widely applicable, fair, and useful, it needs to serve a diverse set of speakers across the world’s languages, be equitable, not unduly biased towards any particular language, and be inclusive of all users. |
| Approach: | They propose to use Gini coefficient to assess NLP across all three dimensions to assess diversity, equity, and inclusion across all languages. |
| Outcome: | The proposed evaluation paradigm assesses NLP technologies across all three dimensions and identifies the need for regional-specific choices in model building and dataset creation. |
LTRS: Improving Word Sense Disambiguation via Learning to Rank Senses (2025.coling-main)
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| Challenge: | Conventional training strategies only consider predefined senses for target words and learn each of them from relatively limited instances, neglecting the influence of similar ones. |
| Approach: | They propose a method to rank senses to improve the task of word Sense Disambiguation (WSD) by ranking an expanded list of sense definitions. |
| Outcome: | The proposed method achieves a SOTA F1 score of 79.6% in Chinese WSD and shows faster convergence than previous methods. |
A Query-Parallel Machine Reading Comprehension Framework for Low-resource NER (2023.findings-emnlp)
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| Challenge: | Named entity recognition (NER) is a fundamental task in natural language processing. |
| Approach: | They propose a query-parallel MRC-based approach to named entity recognition . the model is trained with parameter-efficient tuning technique, making it more data-efficient . |
| Outcome: | The proposed model performs competitively against strong baseline methods in resource-rich settings and achieves state-of-the-art results in low-resource settings. |
Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects (2022.findings-acl)
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| Challenge: | Pre-trained morphosyntactic tagging models outperform existing systems in Modern Standard Arabic and all the Arabic dialects studied. |
| Approach: | They present results on morphosyntactic tagging across different varieties of Arabic using pre-trained transformer language models. |
| Outcome: | The proposed models outperform existing systems in Modern Standard Arabic, 2.8% in Gulf, 1.6% in Egyptian, and 8.3% in Levantine. |
FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction (2022.findings-emnlp)
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| Challenge: | grammatical error correction (GEC) is a complex task that requires high-quality data from native speakers. |
| Approach: | They propose a human-annotated corpus to detect, identify and correct grammatical errors in Chinese examinations. |
| Outcome: | The proposed model outperforms other models in low-resource settings, but there is a significant gap between the models and humans that encourages future models to bridge it. |
Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction? (2023.acl-long)
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| Challenge: | Existing approaches to biomedical relation extraction (RE) are limited due to the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels. |
| Approach: | They propose a method which converts biomedical relation extraction (RE) as natural language inference formulation through indirect supervision. |
| Outcome: | Extensive experiments on three widely-used biomedical RE benchmarks show that indirect supervision improves biomedically relation extraction even when a domain gap exists. |
Self-supervised Rewiring of Pre-trained Speech Encoders:
Towards Faster Fine-tuning with Less Labels in Speech Processing (2022.findings-emnlp)
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| Challenge: | Pre-trained speech encoders have facilitated great success across various speech processing tasks, but fine-tuning them for downstream tasks requires large training data to converge or to achieve state-of-the-art. |
| Approach: | They propose to rewire pre-trained speech encoders to improve their representation space without task-specific labels by neutrally synthesising audio inputs and frame masking. |
| Outcome: | The proposed model shows consistent improvement in isotropy in the representation space on 6 speech processing tasks. |
INDIC QA BENCHMARK: A Multilingual Benchmark to Evaluate Question Answering capability of LLMs for Indic Languages (2025.findings-naacl)
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Abhishek Kumar Singh, Vishwajeet Kumar, Rudra Murthy, Jaydeep Sen, Ashish Mittal, Ganesh Ramakrishnan
| Challenge: | Large Language Models perform well on unseen tasks in English, but their abilities in non-English languages are less explored due to limited benchmarks and training data. |
| Approach: | They propose to release a large dataset for context-grounded question answering in 11 major Indian languages. |
| Outcome: | The Indic-QA Benchmark compared large datasets of large LLMs on extractive and abstractive tasks in 11 major Indian languages. |
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications (P19-1)
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| Challenge: | Existing approaches to generalize deep neural networks are datahungry and generalize poorly from small datasets. |
| Approach: | They propose an agreement score to evaluate routing processes at instance-level and an adaptive optimizer to enhance routing. |
| Outcome: | The proposed approach improves on two NLP tasks and in low-resource settings with few training instances. |
OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages (2022.acl-long)
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| Challenge: | a new study examines the performance of pretraining for sign language recognition in low-resource settings. |
| Approach: | They propose using pose extracted through pretrained models as the standard modality of data to reduce training time and enable efficient inference. |
| Outcome: | The proposed model reduces training time and allows efficient inference in sign languages. |
How Much Pretraining Does Structured Data Need? (2026.eacl-long)
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| Challenge: | Large language models are increasingly adopted for handling structured data, despite pretraining on unstructured text. |
| Approach: | They propose to re-initialize subsets of layers with random weights before fine-tuning on structured datasets. |
| Outcome: | The proposed models are compared to unstructured datasets and show that they perform well over structured data. |
Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing (2021.findings-emnlp)
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Akshat Shrivastava, Pierce Chuang, Arun Babu, Shrey Desai, Abhinav Arora, Alexander Zotov, Ahmed Aly
| Challenge: | a novel approach to map utterances to semantic frames is based on non-autoregressive parsers that shift the decoding task from text generation to span prediction. |
| Approach: | They propose a non-autoregressive, task-oriented parser which shifts the decoding task from text generation to span prediction and produces endpoints as opposed to text. |
| Outcome: | The proposed model bridges the quality gap between non-autoregressive and autoregressive parsers, achieving 87 EM on TOPv2 and shows a 70% reduction in latency and 83% reduction in memory at beam size 5 compared to prior non-regressives. |
Prompt-based Text Entailment for Low-Resource Named Entity Recognition (2022.coling-1)
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| Challenge: | Pre-trained Language Models (PLMs) have been applied in NLP tasks but require labeled data for downstream tasks. |
| Approach: | They propose a method for low-resource named entity recognition that uses prompts to get entailment scores for each candidate and inject tagging labels into prompts. |
| Outcome: | The proposed method achieves competitive performance on the CoNLL03 dataset, and better than fine-tuned counterparts on the MIT Movie and Few-NERD datasets in low-resource settings. |
Investigating the Transferability of Code Repair for Low-Resource Programming Languages (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have shown remarkable performance on code generation tasks. |
| Approach: | They investigate the benefits of distilling the ability to repair code for both high and low resource languages to determine if the techniques are also applicable in low resource settings. |
| Outcome: | The proposed techniques are effective in high- and low-resource languages, but weak in low-level languages. |
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER (2022.acl-long)
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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. |
| Approach: | They propose a demonstration-based learning method which lets the input be prefaced by task demonstrations for in-context learning. |
| Outcome: | The proposed method improves on in-domain learning and domain adaptation in low-resource settings. |
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (2021.naacl-main)
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| Challenge: | a growing body of work is focused on improving performance in low-resource settings . a goal of this study is to explain how these methods differ in their requirements . |
| Approach: | They propose to analyze data-lean scenarios across different dimensions of data availability to understand which approaches are effective in a specific low-resource setting. |
| Outcome: | The proposed methods enable learning when training data is sparse. |
SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations (2026.findings-eacl)
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| Challenge: | Standard NLP benchmarks often miss subtle, culturally-specific cues in social media . incorporating structured cultural knowledge into the retrieval process improves accuracy by up to 31% . |
| Approach: | They propose a retrieval-augmented framework that integrates a culture-specific slang knowledge graph into large language models via one-shot prompting. |
| Outcome: | The proposed framework outperforms traditional and unstructured retrieval methods in slang-based models by 31% and 28%. |
Beyond Full Fine-tuning: Harnessing the Power of LoRA for Multi-Task Instruction Tuning (2024.lrec-main)
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Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning algorithm for large-scale language models. |
| Approach: | They conduct a systematic study of Low-Rank Adaptation (LoRA) on diverse tasks and rich resources with different learning capacities. |
| Outcome: | The proposed algorithm can achieve remarkable performance in high-resource and multi-task scenarios, even comparable to full fine-tuning. |
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)
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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang
| Challenge: | Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios. |
| Approach: | They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers . |
| Outcome: | The proposed model outperforms baselines and class transfer models in low-resource scenarios. |
Low-resource Taxonomy Enrichment with Pretrained Language Models (2021.emnlp-main)
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| Challenge: | Taxonomies represent hierarchical relationships between terms or entities. |
| Approach: | They propose a framework for taxonomy enrichment in low-resource settings with pretrained language models as knowledge bases to compensate for the shortage of information. |
| Outcome: | The proposed framework predicts whether inputted term pairs have hierarchical relationships and leverages implicit knowledge from the LM to generate queries efficiently. |
CycleOIE: A Low-Resource Training Framework For Open Information Extraction (2025.coling-main)
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Zhihong Jin, Chunhong Zhang, Zheng Hu, Jibin Yu, Ruiqi Ma, Qingyun Chen, Xiaohao Liao, Yanxing Zhang
| Challenge: | Open Information Extraction (OpenIE) models rely heavily on large amounts of annotated data. |
| Approach: | They propose a training framework that maximizes data efficiency through a cycle-consistency mechanism. |
| Outcome: | The proposed approach improves the quality of training data by curating low-quality datasets annotated by a large language model. |
Boundary-Aware LLM Augmentation for Low-Resource Event Argument Extraction (2026.eacl-long)
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| Challenge: | Event argument extraction (EAE) is a crucial task in information extraction but its performance heavily depends on expensive annotated data. |
| Approach: | They investigate argument replacement, adjunction rewriting, their combination, and annotation generation using four LLM-based augmentation strategies. |
| Outcome: | The proposed methods improve performance over boundary-agnostic methods and provide detailed analysis of quality from multiple perspectives. |
Modular and Parameter-Efficient Multimodal Fusion with Prompting (2022.findings-acl)
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| Challenge: | Recent research has made impressive progress in large-scale multimodal pre-training. |
| Approach: | They propose to use prompt vectors to align multimodal modalities by pretraining text inputs with prompts or embedding vectors. |
| Outcome: | The proposed method achieves comparable performance to several other multimodal fusion methods in low-resource settings. |
Recent Advances in Online Hate Speech Moderation: Multimodality and the Role of Large Models (2024.findings-emnlp)
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| Challenge: | HS is any communication demeaning a person or a group based on social or ethnic characteristics that undermines social harmony and individual safety . the recent Israel-Hamas conflict has escalated both anti-Muslim and anti-Semitic sentiments worldwide . |
| Approach: | They examine the role of large language models and large multimodal models in HS moderation . they examine how text, images, and audio interact to spread hate speech . |
| Outcome: | The findings highlight the need for solutions in low-resource settings and highlight the gaps in existing methods. |
Tracing Multilingual Knowledge Acquisition Dynamics in Domain Adaptation: A Case Study of Biomedical Adaptation (2026.eacl-long)
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| Challenge: | Multilingual domain adaptation (ML-DA) enables large language models to acquire domain knowledge across languages. |
| Approach: | They propose an adaptive evaluation method that constructs multiple-choice QA datasets from the same bilingual domain corpus used for training. |
| Outcome: | The proposed method constructs multiple-choice QA datasets from the same bilingual domain corpus used for training, thereby enabling direct analysis of multilingual knowledge acquisition. |
Joint Multilingual Supervision for Cross-lingual Entity Linking (D18-1)
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| Challenge: | Entity Linking (XEL) systems ground entity mentions written in any language to Wikipedia . XEL is challenging for most languages due to limited availability of resources as supervision . |
| Approach: | They develop a cross-lingual XEL approach that combines supervision from multiple languages jointly. |
| Outcome: | The proposed approach significantly improves on the current state-of-the-art in 8 languages. |
How Lexical is Bilingual Lexicon Induction? (2024.findings-naacl)
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| Challenge: | lexical variation and low-resource settings make it difficult to learn in low-level settings. |
| Approach: | They propose to incorporate additional lexical information into the retrieve-and-rank approach to improve lexicon induction. |
| Outcome: | The proposed approach improves on XLING by an average of 2% across all language pairs. |
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation (2025.findings-emnlp)
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| Challenge: | generative large language models are increasingly used for data augmentation tasks . text samples are mostly selected randomly and a comprehensive overview of other sample selection strategies is lacking. |
| Approach: | They compare random sample selection strategies and random sample sampling strategies to evaluate their effects in a low-resource setting. |
| Outcome: | The proposed model performance improvements are compared with other sample selection strategies. |
uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data Regimes (2025.naacl-long)
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| Challenge: | Recent work on distilling Whisper’s knowledge into small models using pseudo-labels shows promising performance while reducing the size by up to 50%. |
| Approach: | They propose a framework that distills Whisper’s knowledge into small models using pseudo-labels and reduces the size by up to 50%. |
| Outcome: | The proposed model outperforms the teacher model by 5-7 WER points and is 25-50% more efficient when scaling the data. |
Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling (2025.acl-long)
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| Challenge: | Existing topic modeling models struggle in low-resource settings where data is limited . et al., 2003: domain adaptation for low-source topic modeling is challenging in low resources . |
| Approach: | They propose a domain adaptation framework that disentangles domaininvariant and domain-specific components to improve topic adaptation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on low-resource datasets on diverse datasets. |
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings (2023.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) have ignited a surge in demand for effective fine-tuning techniques . data labeling is notoriously time-consuming and expensive, hindering the development of sizable labeled datasets . |
| Approach: | They propose to use active learning to reduce labeling costs by minimizing label complexity . they find PEFT adapter modules have significant potential in low-resource settings . |
| Outcome: | The proposed model outperforms FFT in low-resource settings and shows that it yields more stable representations of early and middle layers than FFT. |
Scented-EAE: Stage-Customized Entity Type Embedding for Event Argument Extraction (2024.findings-acl)
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| Challenge: | Existing methods for incorporating entities into EAE rely on prompts or NER . weak semantic associations due to missing role-entity correspondence cues . one-sided semantic understanding relying solely on argument role semantics a problem . |
| Approach: | They propose an EAE model with stage-customized entity type embedding to explore the role of entity types. |
| Outcome: | The proposed model achieves state-of-the-art performance on mainstream benchmarks and robustness in low-resource settings. |
CodePrompt: Task-Agnostic Prefix Tuning for Program and Language Generation (2023.findings-acl)
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| Challenge: | Prompt-tuning methods have been used to solve inefficient parameter update and storage issues in Natural Language Generation tasks. |
| Approach: | They propose a task-agnostic prompt tuning method that reflects the traits of PLM for program language. |
| Outcome: | The proposed method is effective in three PLG tasks, not only in the full-data setting but also in the low-resource setting and cross-domain setting. |
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference (2022.findings-emnlp)
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| Challenge: | Semi-supervised learning (SSL) is a popular technique for reducing the reliance on human annotations for NLI tasks. |
| Approach: | They propose a way to incorporate unlabeled data into semi-supervised learning (SSL) using a conditional language model, they propose to generate hypotheses for unlabed sentences . |
| Outcome: | The proposed framework significantly improves the performance of four NLI datasets in low-resource settings. |
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels (D19-1)
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| Challenge: | Existing approaches to improve supervised labeling with noisy training data do not take the input features into account or they need to learn the noise modeling from scratch. |
| Approach: | They propose to cluster training data using input features and compute different confusion matrices for each cluster. |
| Outcome: | The proposed model improves on low-resource named entity recognition settings in several languages, compared with other models which do not take the input features into account or need to learn noise modeling from scratch. |
Lexicon Learning for Few Shot Sequence Modeling (2021.acl-long)
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| Challenge: | Past work has shown that many failures of systematic generalization arise from neural models’ inability to disentangle lexical phenomena from syntactic ones. |
| Approach: | They propose a lexical translation mechanism that generalizes existing copy mechanisms to incorporate learned, decontextualized, token-level translation rules. |
| Outcome: | The proposed model improves generalization on a diverse set of sequence modeling tasks drawn from cognitive science, formal semantics, and machine translation. |
BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languages (2023.emnlp-main)
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| Challenge: | Current research on automatic readability assessment (ARA) has focused on improving the performance of models in high-resource languages such as English. |
| Approach: | They propose a hierarchical cross-lingual modeling approach that takes advantage of a language’s placement in the family tree to increase the amount of available training data. |
| Outcome: | The proposed model improves the performance of models in high-resource languages such as English and Hiligaynon, minasbate, Karay-a, and Rinconada. |
Self-Training using Rules of Grammar for Few-Shot NLU (2021.findings-emnlp)
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| Challenge: | Existing methods for learning natural language understanding are limited in low-resource settings. |
| Approach: | They propose to use rules of grammar to construct and expand rules of grammatical structure of data without human involvement. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in three benchmark datasets. |
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
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Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
| Approach: | They propose a method to optimize prompts for in-context learning by a generator and a discriminator. |
| Outcome: | The proposed method improves state-of-the-art prompt optimization techniques on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. |
XtremeCLIP: Extremely Parameter-efficient Tuning for Low-resource Vision Language Understanding (2023.findings-acl)
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| Challenge: | Existing approaches to fine-tune visual-language understanding (VLU) require tasks-specific designs and sufficient training data. |
| Approach: | They propose a simple yet efficient paradigm for low-resource Visual Language Understanding (VLU) they reformulate a series of VLU tasks as an open-book affinity-matching problem. |
| Outcome: | The proposed framework outperforms baselines in low-resource settings. |
Unsupervised Statistical Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation (NMT) systems can be trained from monolingual corpora without supervision. |
| Approach: | They propose a phrase-based approach that trains from monolingual corpora . their method is based on phrase-driven Statistical Machine Translation (SMT) they propose to train NMT systems without supervision from monolinguistic corpors . |
| Outcome: | The proposed approach improves on the existing supervised systems by combining a phrase table with an n-gram language model and fine-tuning hyperparameters through an unsupervised MERT variant. |
Code-Switching Curriculum Learning for Multilingual Transfer in LLMs (2025.findings-acl)
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| Challenge: | Large language models (LLMs) exhibit near human-level performance in various tasks, but performance drops after a handful of high-resource languages due to the imbalance in pre-training data. |
| Approach: | They propose a code-switching curriculum learning model to enhance cross-lingual transfer for LLMs by progressively training models with a curriculum consisting of token-level code-changing, sentence-level codeswitching, and monolingual corpora. |
| Outcome: | The proposed model improves language transfer to Korean, with significant gains in Japanese and Indonesian . the proposed model mitigates spurious correlations between language resources and safety alignment . |
DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification (2022.coling-1)
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| Challenge: | Existing methods to improve the robustness of text classification models are token-, sentence-, and hiddenlevel augmentation. |
| Approach: | They propose an interpolation-based data augmentation approach called DoubleMix to improve the robustness of text classification models by learning the “shifted” features in hidden space. |
| Outcome: | The proposed approach outperforms several popular methods on six text classification benchmark datasets and visual analysis shows that the model features are highly interpretable. |
Guiding Attention for Self-Supervised Learning with Transformers (2020.findings-emnlp)
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| Challenge: | Recent studies show that self-attention patterns in trained models contain a majority of non-linguistic regularities. |
| Approach: | They propose a technique to allow efficient self-supervised learning with bi-directional Transformers by using an auxiliary loss function to guide attention heads to conform to such patterns. |
| Outcome: | The proposed method achieves state-of-the-art in low-resource settings and is agnostic to pre-training objectives. |
Order-Agnostic Data Augmentation for Few-Shot Named Entity Recognition (2024.acl-long)
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| Challenge: | Existing DA methods for named entity recognition (NER) are costly and labor-intensive to acquire, necessitating innovative approaches to data scarcity. |
| Approach: | They propose an order-agnostic data augmentation solution that exploits the order-based property in the training phase of sequence-to-sequence NER methods for data augmented. |
| Outcome: | The proposed method significantly enhances the few-shot capabilities of pre-trained language models in low-resource settings. |
Answering Ambiguous Questions via Iterative Prompting (2023.acl-long)
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| Challenge: | Empirical studies show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. |
| Approach: | They propose an answering model with a prompting model to address imperfections in open-domain question answering . Empirical studies show AmbigPrompt achieves state-of-the-art or competitive results . |
| Outcome: | The proposed framework improves on two commonly-used open benchmarks and achieves state-of-the-art or competitive results while using less memory and having a lower inference latency. |
Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond (2025.naacl-long)
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Mardhiyah Sanni, Tassallah Abdullahi, Devendra Deepak Kayande, Emmanuel Ayodele, Naome A Etori, Michael Samwel Mollel, Moshood O. Yekini, Chibuzor Okocha, Lukman Enegi Ismaila, Folafunmi Omofoye, Boluwatife A. Adewale, Tobi Olatunji
| Challenge: | Afrispeech-Dialog is a benchmark dataset of 50 simulated medical and non-medical African-accented English conversations . a 10%+ performance degradation is found in ASR systems on long-form, accented speech . |
| Approach: | They propose to use a dataset to evaluate automatic speech recognition systems on African-accented conversations. |
| Outcome: | The proposed dataset compares state-of-the-art speech recognition systems on accented conversations with native accents and shows a 10%+ performance degradation. |
Pre-training with Meta Learning for Chinese Word Segmentation (2021.naacl-main)
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| Challenge: | Recent studies show that pre-trained models are beneficial to Chinese Word Segmentation (CWS). However, these models lack task-specific prior segmentation knowledge. |
| Approach: | They propose a pre-trained Chinese word segmentation model MetaSeg which incorporates meta learning into a multi-criteria pre-training task. |
| Outcome: | Empirical results show that MetaSeg can achieve new state-of-the-art performance on twelve widely-used CWS datasets and significantly improve model performance in low-resource settings. |
Dirichlet-Smoothed Word Embeddings for Low-Resource Settings (2020.lrec-1)
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| Challenge: | Existing count-based word embeddings are superseded by machine-learning methods like word2vec and GloVe, but in many settings there is not much text data available. |
| Approach: | They propose to use positive pointwise mutual information (PPMI) weighted co-occurrence matrices to compute word embeddings from a corpus using large amounts of text data. |
| Outcome: | The proposed method outperforms word2vec and the state-of-the-art for low-resource settings and obtains competitive results for Maltese and Luxembourgish. |
Generate-and-Retrieve: Use Your Predictions to Improve Retrieval for Semantic Parsing (2022.coling-1)
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Yury Zemlyanskiy, Michiel de Jong, Joshua Ainslie, Panupong Pasupat, Peter Shaw, Linlu Qiu, Sumit Sanghai, Fei Sha
| Challenge: | Existing retrieval techniques for semantic parsing use similarity of query and exemplar inputs . Existing work suggests that appending training samples to training samples improves performance . |
| Approach: | They propose a retrieval procedure that retrieves exemplars for which outputs are similar . existing retrieval techniques are based on similarity of query and exemplar inputs . |
| Outcome: | Existing retrieval techniques rely on similarity of query and exemplar inputs . they retrieve exemplars with similar outputs and generate a final prediction . |
Linguistically-Motivated Yorùbá-English Machine Translation (2022.coling-1)
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| Challenge: | Several phenomena where asymmetry arises have been identified as challenging problems for machine translation. |
| Approach: | They perform a fine-grained analysis of how an SMT system compares with two NMT systems when translating bare nouns into English. |
| Outcome: | The proposed model outperforms the SMT and BiLSTM models for 4 categories and the BiLST outperformed the SLT models for 3 categories. |
Rewarding What Matters: Step-by-Step Reinforcement Learning for Task-Oriented Dialogue (2024.findings-emnlp)
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| Challenge: | Existing RL methods focus on generation tasks while neglecting dialogue state tracking (DST) for understanding. |
| Approach: | They propose a method that integrates RL into both understanding and generation tasks by introducing step-by-step rewards throughout the token generation. |
| Outcome: | The proposed approach achieves state-of-the-art results on three widely used datasets. |
A Survey of Multilingual Reasoning in Language Models (2025.findings-emnlp)
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| Challenge: | This survey provides the first in-depth review of multilingual reasoning in Language Models. |
| Approach: | This survey provides the first in-depth review of multilingual reasoning in LMs. |
| Outcome: | The present study provides the first in-depth review of multilingual reasoning in LMs. |
Inverse-Q*: Token Level Reinforcement Learning for Aligning Large Language Models Without Preference Data (2024.findings-emnlp)
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) relies on complex methodologies like Proximal Policy Optimization (PPO) that require extensive hyper-parameter tuning and pose challenges in sample efficiency and stability. |
| Approach: | They propose an innovative framework that leverages direct preference optimization techniques but extends them by estimating the conditionally optimal policy directly from the model’s responses. |
| Outcome: | The proposed framework matches and exceeds the effectiveness of Proximal Policy Optimization (PPO) in terms of convergence speed and alignment of model responses with human preferences. |
TCProF:Time-Complexity Prediction SSL Framework (2025.naacl-long)
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| Challenge: | determining the precise time complexity of a code is theoretically undecidable . determining time complexity is a challenging task in programming efficiency analysis . |
| Approach: | They propose a time-complexity prediction SSL framework that uses code snippets labeled with their time complexity classes to predict code time. |
| Outcome: | The proposed approach improves performance by 60% over self-training methods. |
Enhancing Code-Switching for Cross-lingual SLU: A Unified View of Semantic and Grammatical Coherence (2023.emnlp-main)
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| Challenge: | Existing models rely on annotated training data, limiting their scalability to low-resource languages. |
| Approach: | They propose a method termed SoGo for zero-shot cross-lingual SLU that uses keywords as substitution options to extract keywords and a token-level alignment strategy to ensure grammatical coherence. |
| Outcome: | The proposed method improves zero-shot cross-lingual SLU across nine languages on MultiATIS++. |
Scaling Laws for BERT in Low-Resource Settings (2023.findings-acl)
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| Challenge: | Large language models require huge training corpora, which is unobtainable for most NLP practitioners. |
| Approach: | They propose power-law formulas that relate model size, corpora size and computation power to find the optimal settings in advance given a fixed budget. |
| Outcome: | The proposed models perform better on MLM and NLU tasks on four languages of different linguistic characteristics. |
Coordination Generation via Synchronized Text-Infilling (2022.coling-1)
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| Challenge: | Generating synthetic data from pre-trained language models has enhanced performance across several NLP tasks. |
| Approach: | They propose a method for generating sentences with a coordinate structure in which the boundaries of its conjuncts are explicitly specified. |
| Outcome: | The proposed method produces promising coordination instances that provide gains for the task in low-resource settings. |
PICLe: Pseudo-annotations for In-Context Learning in Low-Resource Named Entity Detection (2025.naacl-long)
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| Challenge: | In-context learning is sensitive to the choice of demonstrations and can be used for tasks with few examples. |
| Approach: | They propose a framework for in-context learning with noisy, pseudo-annotated demonstrations . they annotate large quantities of demonstrations in a zero-shot first pass . |
| Outcome: | The proposed framework outperforms ICL on biomedical NED datasets with zero human-annotation. |
Tagged Back-translation Revisited: Why Does It Really Work? (2020.acl-main)
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| Challenge: | In this paper, we show that neural machine translation systems trained on large back-translated data overfit some of the characteristics of machine-transcribed texts. |
| Approach: | They propose to add a tag to back-translations to help distinguish back-translated data from original parallel training data. |
| Outcome: | The proposed tag helps the system distinguish back-translated data from original parallel training data and is as effective as a tag in high-resource training. |
Enhancing Parameter-efficient Fine-tuning with Simple Calibration Based on Stable Rank (2024.lrec-main)
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| Challenge: | Existing methods for lightweight fine-tuning are ineffective in low-resource settings but fail in high-resourced settings, leading to unreliable outcomes. |
| Approach: | They propose a calibration strategy that takes into account the inherent variance of generalization ability in model components and potential changes during the fine-tuning process. |
| Outcome: | The proposed calibration improves GLUE score by 3.1 points over the previous calibration method. |
Data Augmentation for Low-Resource Keyphrase Generation (2023.findings-acl)
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| Challenge: | Existing works on keyphrase generation rely on large-scale annotated datasets, which are not easy to acquire. |
| Approach: | They propose to use full text to improve keyphrase generation in resource-constrained domains by using the full text of the articles to augment their methods. |
| Outcome: | The proposed methods improve both present and absent keyphrase generation on three datasets and show that they are cost-effective. |
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation (2022.emnlp-main)
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| Challenge: | Recent work on aspect sentiment quad prediction (ASQP) uses a template to extract aspect quadruplets from review sentences. |
| Approach: | They propose to use a pre-trained language model to select proper orders from a template order perspective to improve aspect sentiment quad prediction. |
| Outcome: | The proposed method outperforms state-of-the-art methods significantly in low-resource settings. |
Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval (2025.findings-acl)
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| Challenge: | Existing methods for multilingual and cross-lingual retrieval are lacking in low-resource, morphologically rich languages such as Amharic. |
| Approach: | They propose to train Amharic-specific dense retrieval models based on pre-trained Amharican BERT and RoBERTa backbones. |
| Outcome: | The proposed model achieves 17.6% improvement in MRR@10 and 9.86% gain in Recall@10 over the strongest multilingual baseline, Arctic Embed 2.0. |
Bi-level Finetuning with Task-dependent Similarity Structure for Low-resource Training (2023.findings-acl)
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| Challenge: | Existing approaches to fine tune a large language model in low-resource settings are limited in their expressiveness or rely on task-independent knowledge. |
| Approach: | They propose a framework where all parameters are finetuned with task-dependent information from the training data only. |
| Outcome: | The proposed framework outperforms baseline models on several classification datasets in low-resource scenarios. |
UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor (2022.coling-1)
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| Challenge: | Multi-Document Summarization (MDS) uses the extract-then-abstract paradigm, which extracts a relatively short meta-document and then feeds it into the deep neural networks to generate an abstract. |
| Approach: | They propose to use pre-trained language models to calculate document and keyword’s perplexity to boost other metrics for evaluating a document’s salience. |
| Outcome: | The proposed method can be applied as a plug-in to boost other metrics for evaluating a document’s salience, thus improving the subsequent abstract generation. |
Reward Modeling for Scientific Writing Evaluation (2026.acl-long)
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| Challenge: | Existing models for scientific writing evaluation are primarily optimized for general-purpose benchmarks with fixed scoring rubrics and evaluation criteria. |
| Approach: | They propose to train scientific writing evaluation models that leverage domain knowledge . they use a two-stage evaluation framework that optimizes evaluation preferences and refines reasoning capabilities . |
| Outcome: | The proposed model generalizes effectively across tasks and to previously unseen settings. |
Dealing with Abbreviations in the Slovenian Biographical Lexicon (2022.emnlp-main)
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| Challenge: | Abbreviations are a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors. |
| Approach: | They propose a method for identifying abbreviations in a Slovenian biographical lexicon . they use a newly developed dataset to evaluate the method against common ad-hoc solutions . |
| Outcome: | The proposed method performs better than ad-hoc solutions on a Slovenian biographical lexicon. |
Explaining Pre-Trained Language Models with Attribution Scores: An Analysis in Low-Resource Settings (2024.lrec-main)
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| Challenge: | Currently, prompt-based models are gaining popularity due to their easier adaptability in low-resource settings. |
| Approach: | They analyze attribution scores extracted from prompt-based models w.r.t. plausibility and faithfulness and compare them with attribution score extracted from fine-tuned models and large language models. |
| Outcome: | The proposed model outperforms attention and Integrated Gradients in plausibility and faithfulness, while fine-tuning models are harder to explain in low-resource settings. |
Simple and Effective Unsupervised Speech Translation (2023.acl-long)
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Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
| Challenge: | Existing methods to train speech models without labeled data are limited for most languages. |
| Approach: | They propose a pipeline approach to build speech translation systems without labeled data by leveraging recent advances in unsupervised speech recognition, machine translation and speech synthesis. |
| Outcome: | The proposed approach outperforms the state-of-the-art in unsupervised speech recognition by 3.2 BLEU on the Libri-Trans benchmark and the best supervised end-to-end models from only two years ago by an average of 5.0 BLUE over five X-En directions. |
Making LLMs Better Many-to-Many Speech-to-Text Translators with Curriculum Learning (2025.acl-long)
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Yexing Du, Youcheng Pan, Ziyang Ma, Bo Yang, Yifan Yang, Keqi Deng, Xie Chen, Yang Xiang, Ming Liu, Bing Qin
| Challenge: | Existing studies on English-centric translation tasks have focused on multimodal large language models, but the exploration of many-to-many translation is limited by the scarcity of parallel data. |
| Approach: | They propose a three-stage curriculum learning strategy that leverages the machine translation capabilities of large language models and adapts them to S2TT tasks. |
| Outcome: | The proposed strategy achieves state-of-the-art average performance in 1514 language pairs, requiring fewer than 10 hours of speech data per language to achieve competitive results. |
SHONGLAP: A Large Bengali Open-Domain Dialogue Corpus (2022.lrec-1)
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| Challenge: | Existing open-domain dialogue systems suffer from data scarcity due to unavailability of high-quality datasets for low-resource languages like Bengali. |
| Approach: | They propose to prepare large-scale open-domain dialogue datasets from podcasts and talk-shows and label them based on weak-supervision techniques. |
| Outcome: | The proposed corpus improves performance of large language models in case of downstream classification tasks during fine-tuning. |
SSA-COMET: Do LLMs Outperform Learned Metrics in Evaluating MT for Under-Resourced African Languages? (2025.emnlp-main)
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Senyu Li, Jiayi Wang, Felermino D. M. A. Ali, Colin Cherry, Daniel Deutsch, Eleftheria Briakou, Rui Sousa-Silva, Henrique Lopes Cardoso, Pontus Stenetorp, David Ifeoluwa Adelani
| Challenge: | Existing metrics for machine translation quality for under-resourced African languages suffer from limited language coverage and poor performance in low-resource settings. |
| Approach: | They propose a large-scale human-annotated machine translation evaluation dataset . they use a reference-based and reference-free evaluation model to compare MT quality . |
| Outcome: | The proposed models outperform AfriCOMET and the strongest LLM on low-resource languages. |
FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations (2021.emnlp-main)
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| Challenge: | Recent work on word embeddings and pre-trained language models has shown the large impact of language representations on natural language processing (NLP) models across tasks and domains. |
| Approach: | They propose feature-based adversarial meta-embeddings with an attention function that is guided by word-specific properties, such as shape and frequency, to handle subword-based embeddings. |
| Outcome: | The proposed model improves performance in downstream tasks even with word embeddings from transformers. |
To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning (2021.emnlp-main)
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| Challenge: | Existing methods to select transfer sources are limited by text and task similarity, which limits their application in transfer settings where both the task and the text domain change. |
| Approach: | They propose a model similarity measure that represents text and task similarity jointly to automatically determine which and how many sources to exploit. |
| Outcome: | The proposed approach improves performance by 24 F1 points for predicting promising sources across domains and tasks with similar models. |
Cheetah: Natural Language Generation for 517 African Languages (2024.acl-long)
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| Challenge: | Low-resource African languages pose unique challenges for natural language processing (NLG) We demonstrate the effectiveness of Cheetah through comprehensive evaluations across six generation downstream tasks. |
| Approach: | They develop a multilingual NLG language model for African languages called Cheetah . they demonstrate that Cheethah outperforms other models in six tasks . |
| Outcome: | The proposed model outperforms other models in five of six generation tasks. |
Predicting Embedding Reliability in Low-Resource Settings Using Corpus Similarity Measures (2022.lrec-1)
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| Challenge: | a paper aims to evaluate embedding similarity, stability and reliability in low-resource settings . it uses corpus similarity measures before training to predict properties of embeddables . |
| Approach: | They use corpus similarity measures before training to predict properties of embeddings . they then apply the same measures to low-resource settings by modelling reliability . authors hope to use this method to evaluate low-source languages with limited corpus size . |
| Outcome: | The paper shows that it is possible to predict downstream embedding similarity using upstream corpus similarity measures . the main finding is that the measures remain robust on small amounts of training data . |
Context Filtering with Reward Modeling in Question Answering (2025.coling-main)
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| Challenge: | Question Answering (QA) tasks require a mix of relevant and irrelevant information in these contexts to perform well. |
| Approach: | They propose a context filtering approach that removes non-essential details, summarizing crucial content through Reward Modeling. |
| Outcome: | The proposed approach outperforms baseline models in 6.8-folds. |
DS2-ABSA: Dual-Stream Data Synthesis with Label Refinement for Few-Shot Aspect-Based Sentiment Analysis (2025.acl-long)
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| Challenge: | Existing methods for annotating data are time-consuming and labor-intensive . Existing low-resource solutions comprise data augmentation and in-context learning . |
| Approach: | They propose a dual-stream data synthesis framework for few-shot ABSA . it leverages key-point-driven and instance-driven LLMs to generate diverse data . |
| Outcome: | Extensive experiments show that DS2-ABSA outperforms existing methods . previous studies have shown that the proposed approach generates diverse data . |
Impact of Task Adapting on Transformer Models for Targeted Sentiment Analysis in Croatian Headlines (2024.lrec-main)
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| Challenge: | BERT models are often taken off-the-shelf and fine-tuned on a downstream task. |
| Approach: | They propose an extra stage of self-supervised task-adaptive pre-training to perform a task on a number of Croatian-supporting Transformer models. |
| Outcome: | The proposed approach improves performance across multilingual models but not in Croatian-dominant models. |
Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
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| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
| Approach: | They propose to use weakly supervised learning to train models with noisy labels from weak sources instead of collecting expensive human annotations. |
| Outcome: | The proposed methods outperform weakly supervised methods on various NLP datasets and tasks on the test sets. |
Noisy Self-Training with Synthetic Queries for Dense Retrieval (2023.findings-emnlp)
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| Challenge: | Existing neural retrieval models require training on a sufficient number of human-labelled query-passage pairs to work well. |
| Approach: | They propose a noisy self-training framework with synthetic queries to improve retrieval methods. |
| Outcome: | The proposed method outperforms baselines on general-domain and out-of-domain retrieval benchmarks on low-resource settings and is data efficient and data efficient. |
ZeroNER: Fueling Zero-Shot Named Entity Recognition via Entity Type Descriptions (2025.findings-acl)
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Alessio Cocchieri, Marcos Martínez Galindo, Giacomo Frisoni, Gianluca Moro, Claudio Sartori, Giuseppe Tagliavini
| Challenge: | Existing zero-shot learning methods rely on entity type names for generalization . current solutions require large datasets and prioritize a handful of commonly occurring types . |
| Approach: | They propose a description-driven framework that enhances hard zero-shot NER in low-resource settings. |
| Outcome: | The proposed framework outperforms existing models by up to 16% in the F1 score . it also surpasses baseline models that use type names alone . |
Self-training with Few-shot Rationalization (2021.emnlp-main)
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| Challenge: | Recent work focused on training largescale and complex neural network models, but they are opaque in terms of their decision-making process. |
| Approach: | They propose a multi-task teacher-student framework for self-training pre-trained language models with limited task-specific labels and annotated rationales. |
| Outcome: | The proposed model improves performance in low-resource settings by making it aware of its rationalized predictions. |
Advancing Arabic Diacritization: Improved Datasets, Benchmarking, and State-of-the-Art Models (2025.emnlp-main)
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| Challenge: | Arabic diacritics are typically omitted in written Arabic, leading to ambiguity . authors propose a methodology to analyze and refine a large diacritized corpus . |
| Approach: | They propose a methodology to analyze and refine a large diacritized corpus to improve training quality. |
| Outcome: | The proposed model achieves state-of-the-art results with 3.12% and 2.70% WER on WikiNews-2014 and Wikinews-2024. |
SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes (2025.findings-emnlp)
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Iva Bojic, Qi Chwen Ong, Stephanie Hilary Xinyi Ma, Lin Ai, Zheng Liu, Ziwei Gong, Julia Hirschberg, Andy Hau Yan Ho, Andy W. H. Khong
| Challenge: | SMARTMiner extracts specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching notes. |
| Approach: | They propose a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching notes. |
| Outcome: | The framework extracts behavior change goal spans and categorizes their SMARTness. |
ArkRepoBench: A Repository-Level Code Completion Benchmark for HarmonyOS Development (2026.findings-acl)
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Yanlin Wang, Bowen Zhang, Yanli Wang, Daya Guo, Terry Yue Zhuo, Jiachi Chen, Mingwei Liu, Xingong Zhang, Zibin Zheng
| Challenge: | Despite the maturity of LLM-based code assistance for mainstream languages, the capabilities of ArkTS are largely unexplored. |
| Approach: | They propose to benchmark repository-level code completion for ArkTS using 7,519 samples from 20 official HarmonyOS repositories. |
| Outcome: | The proposed benchmark covers multiple difficulty levels and categorizes completion instances into Single-File, Cross-Filled Independent, and Cross-Filed Dependent settings based on dependency analysis. |
ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information (2024.findings-emnlp)
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| Challenge: | Existing models for detecting harmful content lack diversity and quality of datasets. |
| Approach: | They propose a framework for synthesizing toxic information from social media datasets . their framework generates a wide variety of synthetic, yet remarkably realistic, examples of toxic information . |
| Outcome: | The proposed framework can generate a wide variety of synthetic, yet remarkably realistic, examples of toxic information. |
Improving Language and Modality Transfer in Translation by Character-level Modeling (2025.acl-long)
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| Challenge: | Current translation systems cover only 5% of the world's languages . expanding to the long-tail of low-resource languages requires data-efficient methods that rely on cross-lingual and cross-modal knowledge transfer. |
| Approach: | They propose a character-based approach to improve adaptability to new languages and modalities by using a teacher-student approach and parallel translation data to obtain a SONAR character-level encoder. |
| Outcome: | The proposed model outperforms subword-based models in speech-to-text translation on the FLEURS benchmark on 33 languages and achieves state-of-the-art generalizability to unseen languages. |
AnyTOD: A Programmable Task-Oriented Dialog System (2023.emnlp-main)
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Jeffrey Zhao, Yuan Cao, Raghav Gupta, Harrison Lee, Abhinav Rastogi, Mingqiu Wang, Hagen Soltau, Izhak Shafran, Yonghui Wu
| Challenge: | a neuro-symbolic approach allows zero-shot adaptation to unseen tasks and domains . a neural LM keeps track of events that occur during a conversation and a symbolic program implements dialog policy is executed to recommend actions. |
| Approach: | They propose an end-to-end, zero-shot task-oriented dialog system . it is designed to adapt to unseen tasks or domains without prior training . |
| Outcome: | The proposed system can be programmed to adapt to unseen tasks without training . it reduces data collection and training requirements for enabling new TOD 1 16189 tasks . |
A Graph-Theoretical Framework for Analyzing the Behavior of Causal Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in natural language processing have popularized causal language models . but their internal behavior remains poorly understood due to the high cost and reliance on large-scale benchmarks . |
| Approach: | They propose a graph-theoretical framework for analyzing causal language models . they construct graphs from model outputs and use metrics to capture linguistic features . |
| Outcome: | The proposed framework provides a macroscopic view of the overall behavior of a language model. |
VerifyMatch: A Semi-Supervised Learning Paradigm for Natural Language Inference with Confidence-Aware MixUp (2024.emnlp-main)
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| Challenge: | Natural language inference (NLI) is a key task for evaluating a model's ability to perform natural language understanding and reasoning. |
| Approach: | They propose to construct pseudo-generated samples using class-specific fine-tuned large language models (LLMs) . they retain all pseudo-labeled samples, but use MixUp to ensure unlabele . |
| Outcome: | The proposed approach achieves competitive accuracy compared to strong baselines for NLI datasets in low-resource settings. |
ProMALex: Progressive Modular Adapters for Multi-Jurisdictional Legal Language Modeling (2025.acl-long)
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| Challenge: | Existing approaches to training language models for each jurisdiction fail to leverage common legal principles beneficial for low-resource settings or risk negative interference from conflicting jurisdictional interpretations. |
| Approach: | They propose a parameter-efficient framework that derives hierarchical relationships across jurisdictions and progressively inserts adapter modules across model layers based on jurisdictional similarity. |
| Outcome: | The proposed framework outperforms fully shared and jurisdiction-specific models on two legal language modeling benchmarks. |
Parameter-Efficient Transfer Learning for End-to-end Speech Translation (2024.lrec-main)
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| Challenge: | Existing approaches to improve end-to-end speech translation are limited by the availability of labeled data. |
| Approach: | They propose a method which utilizes two lightweight adaptation techniques to modulate Attention and the Feed-Forward Network while preserving the capabilities of pre-trained models. |
| Outcome: | The proposed method outperforms baseline models and significantly improves performance in low-resource settings. |
Radical Allomorphy: Phonological Surface Forms without Phonology (2025.findings-emnlp)
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| Challenge: | Recent work typically frames morphophonology as generating surface forms from abstract underlying representations (URs) this theory-laden assumption is expensive to annotate, especially in low-resource settings. |
| Approach: | a new approach frames morphophonology as generating surface forms from abstract underlying representations by applying phonological rules or constraints. |
| Outcome: | The proposed model removes the need to posit or label URs and lets the model exploit the surface evidence directly. |
BNLP: A Text Annotation Platform for Quality Control of LLM-Generated Annotations (2026.findings-acl)
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| Challenge: | Existing annotation tools lack support for Large Language Models (LLMs) or use LLMs as one-off preannotation engines, compromising data reliability. |
| Approach: | They propose a text annotation platform that embeds LLM-assisted labeling into a quality-aware collaborative workflow. |
| Outcome: | Experiments show that BNLP reduces annotation time by 74.3% and improves annotation quality by 11.6% over purely manual annotation in LLM-assisted settings. |
Document-Level Event-Argument Data Augmentation for Challenging Role Types (2025.acl-long)
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| Challenge: | Existing methods for Event Argument Extraction (EAE) are not well-suited to a variety of real-world situations, including long documents and challenging role types. |
| Approach: | They propose two novel methods for generating document-level EAE samples using zero in-domain training data and validate their generalizability. |
| Outcome: | The proposed methods show significant performance increases in low-resource settings. |
Enhancing Domain-Specific Encoder Models with LLM-Generated Data: How to Leverage Ontologies, and How to Do Without Them (2025.findings-emnlp)
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| Challenge: | a new method for continual pretraining transformer encoder models is proposed for specialized domains with limited training data. |
| Approach: | They propose to use LLM-generated data to enrich domain-specific ontologies and pretrain transformer encoder models as an ontology-informed embedding model for concept definitions. |
| Outcome: | The proposed method improves on standard MLM pretraining on invasion biology domains. |
JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew (2026.findings-acl)
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| Challenge: | Existing efforts to personalize for individual decision-makers focus on user preferences rather than reasoning. |
| Approach: | They propose a synthetic-organic supervision pipeline that transforms raw judicial decisions into instruction-tuning data. |
| Outcome: | The proposed pipeline outperforms state-of-the-art methods across three tasks and settings. |
AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering (2025.findings-emnlp)
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| Challenge: | Existing Med-MLLMs fail when deployed in low-resource settings where abundant labeled data is unavailable. |
| Approach: | They propose a training-free agentic framework that performs medical knowledge augmentation via LLM agents. |
| Outcome: | The proposed framework performs medical knowledge augmentation via LLM agents. |
GEMS: Generation-Based Event Argument Extraction via Multi-perspective Prompts and Ontology Steering (2025.findings-acl)
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| Challenge: | Existing methods for event argument extraction rely on a single prompt . existing methods ignore complex structural and dynamic interdependencies between event arguments . |
| Approach: | They propose a multi-prompt learning framework that generates event arguments via multi-perspective prompts and ontology steering. |
| Outcome: | The proposed framework captures interrelationships between arguments and ontology steering . it uses multiple unfilled prompts for each sentence to generate event arguments . |
LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition (2025.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) tasks are performed using only a few demonstrations. |
| Approach: | They propose a method that leverages training labels through token-level statistics to improve ICL performance. |
| Outcome: | The proposed method outperforms existing methods on five NER datasets and is robust in low-resource settings. |
Scaling Laws or Threshold Effects: Exploring the Optimal Vocabulary Size for Balancing Performance and Efficiency in Low-Resource Languages (2026.findings-acl)
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| Challenge: | vocab expansion scaling laws are well-established for high-resource languages, but they remain unverified in low-resourced settings. |
| Approach: | They propose to scale trilingual vocabulary for languages with 140 to 195,000 tokens . they find that BBPE follows a "decline-then-rise" pattern, whereas BPE improves monotonically . |
| Outcome: | The proposed configuration reduces pre-training duration by over 71% across 1.5B to 8B models while improving downstream performance. |
Decomposing Unitization and Typing for Efficient and Consistent Span-Bound Concept Annotation (2026.findings-acl)
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| Challenge: | Substantial resources are typically spent on unitizing, the task of identifying precise span boundaries for entity mentions. |
| Approach: | They propose a method that focuses manual efforts on typed position annotations instead of full concept annotation. |
| Outcome: | The proposed procedure reduces the cost of concept annotations by focusing on typed positions instead of full concept annotation. |
GanitLLM: Difficulty-Aware Bengali Mathematical Reasoning through Curriculum-GRPO (2026.findings-acl)
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| Challenge: | Existing LLMs either reason in English and translate, or simply fail on multi-step Bengali math. |
| Approach: | They propose a Bengali mathematical reasoning model called GanitLLM with a difficulty-aware Bengali math corpus and a curriculum-based GRPO pipeline. |
| Outcome: | The proposed model improves on Bn-MGSM and Bn MSVAMP by +8 and +7 accuracy points while increasing the percentage of Bengali reasoning tokens from 14% to over 88% and reducing solution length from 943 to 193 words. |