Papers with data sparsity
Engage the Public: Poll Question Generation for Social Media Posts (2021.acl-long)
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| Challenge: | a novel application to generate poll questions for social media posts offers an easy way to hear the public's voice . for the silent majority, they tend to read others' messages instead of voicing their opinions with words . |
| Approach: | They propose to encode user comments and discover latent topics therein as contexts to generate poll questions for social media posts. |
| Outcome: | The proposed model outperforms popular models without exploiting topics from comments . human evaluations show it can generate high-quality polls useful to draw user engagements . |
Investigating data partitioning strategies for crosslinguistic low-resource ASR evaluation (2023.eacl-main)
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| Challenge: | Automatic speech recognition data sets include a single pre-defined test set consisting of one or more speakers whose speech never appears in the training set. |
| Approach: | They propose to use hold-speaker(s)-out partitioning to partition data for five languages . utterance duration and intensity are more predictive factors of variability . |
| Outcome: | The proposed method can produce results that do not reflect model performance on unseen data or speakers. |
Understanding Compositional Data Augmentation in Typologically Diverse Morphological Inflection (2023.emnlp-main)
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| Challenge: | a data augmentation technique that generates synthetic examples by randomly substituting stem characters in existing training examples is still poorly understood. |
| Approach: | They propose a data augmentation strategy that generates synthetic examples by randomly substituting stem characters in existing training examples. |
| Outcome: | The proposed method generates synthetic examples by randomly substituting stem characters in existing training examples. |
Neural Machine Translation for Agglutinative Languages via Data Rejuvenation (2025.acl-srw)
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| Challenge: | Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora. |
| Approach: | They propose to combine fine-grained inactive sample identification with target-side rejuvenation to improve translation quality from agglutinative languages. |
| Outcome: | The proposed framework improves on four low-resource agglutinative language tasks. |
Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation (2025.coling-main)
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| Challenge: | Existing contrastive learning-based methods struggle with data sparsity in real-world recommendations . Graph collaborative filtering incorporates contrastive training as an auxiliary task to improve performance . |
| Approach: | They propose a perturbation-driven dual auxiliary contrastive learning task for collaborative filtering . structure perturbation and weight perturbation are used to construct two graphs . |
| Outcome: | The proposed model outperforms benchmark models on multiple public datasets. |
Importance-Aware Data Augmentation for Document-Level Neural Machine Translation (2024.eacl-long)
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| Challenge: | Document-level neural machine translation (DocNMT) models can be difficult and expensive to train due to data sparsity. |
| Approach: | They propose an Importance-Aware Data Augmentation algorithm that augments training data based on token importance information estimated by the norm of hidden states and training gradients. |
| Outcome: | The proposed algorithm outperforms strong DocNMT baselines and several data augmentation approaches on three widely-used benchmarks. |
Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval (2024.emnlp-industry)
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Zhirui Kuai, Zuxu Chen, Huimu Wang, Mingming Li, Dadong Miao, Wang Binbin, Xusong Chen, Li Kuang, Yuxing Han, Jiaxing Wang, Guoyu Tang, Lin Liu, Songlin Wang, Jingwei Zhuo
| Challenge: | Generative retrieval (GR) is a transformative paradigm in search and recommender systems . however, data sparsity and long-tailed distribution hinder the full utilization of GR . |
| Approach: | They propose a method to reduce the "Hourglass" phenomenon in RQ-SID where codebook tokens become overly concentrated. |
| Outcome: | The proposed methods improve retrieval efficiency and generalization capabilities. |
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality (N18-1)
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| Challenge: | Efficient word representations play an important role in solving various problems related to NLP, data mining, text mining etc. |
| Approach: | They propose to leverage bilingual word embeddings learned through a parallel corpus to minimize the effect of data sparsity. |
| Outcome: | The proposed model is tested against state-of-the-art methods in two experimental setups. |
MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling (2022.findings-emnlp)
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| Challenge: | Existing studies focus on generating implicit user profiles from the user’s dialogue history, thus it suffers from data sparsity and performance degradation. |
| Approach: | They propose a self-supervised learning framework MCP for capturing better representations from users’ dialogue history for personalized chatbots. |
| Outcome: | The proposed model improves on two real-world datasets. |
Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain (2023.eacl-main)
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| Challenge: | a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain is proposed . connecting a symptom to an underlying cause is a crucial building block for natural language understanding across domains. |
| Approach: | They propose a method to aggregate salient predictions from a set of cloze task style templates and use a Wikipedia corpus to augment the model. |
| Outcome: | The proposed approach outperforms a traditional pattern-based approach by exploiting the compositionality assumption in a cloze task style setting. |
CVAE-based Re-anchoring for Implicit Discourse Relation Classification (2021.findings-emnlp)
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| Challenge: | Existing studies show that training implicit discourse relation classifiers suffers from data sparsity. |
| Approach: | They propose a re-anchoring strategy to reduce the risk of erroneous sampling . they use Conditional VAE to estimate the risk and migrate the anchor to reduce it . |
| Outcome: | The proposed method improves the baseline classifier performance on PDTB v2.0 . |
Generative Bridging Network for Neural Sequence Prediction (N18-1)
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| Challenge: | Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing. |
| Approach: | They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence. |
| Outcome: | The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden. |
Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning (2022.emnlp-main)
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| Challenge: | Existing topic models do not make full use of word co-occurrence information to model latent topics. |
| Approach: | They propose a novel short text topic modeling framework, Topic-Semantic Contrastive Topic Model, which uses augmented data and the data characteristic to learn the relations among samples. |
| Outcome: | The proposed framework outperforms state-of-the-art baselines regardless of the data augmentation availability, producing high-quality topics and topic distributions. |
A Measure for Transparent Comparison of Linguistic Diversity in Multilingual NLP Data Sets (2024.findings-naacl)
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| Challenge: | a new study aims to assess linguistic diversity of multilingual data sets against a reference language sample . linguistic diversity is typically measured as the number of languages included in the data set . but such measures do not consider structural properties of the included languages . |
| Approach: | They propose to measure linguistic diversity against a reference language sample to maximise linguistic diversity. |
| Outcome: | The proposed measure can be used to identify the types of languages that are not represented in a data set. |
Topic-Aware Neural Keyphrase Generation for Social Media Language (P19-1)
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| Challenge: | Existing methods to extract words from source posts to form keyphrases do not exploit latent topics. |
| Approach: | They propose a sequence-to-sequence-based neural keyphrase generation framework . it allows absent keyphrases to be created, and it allows joint modeling of latent topic representations . |
| Outcome: | The proposed model outperforms extraction and generation models without exploiting latent topics. |
Multi-source Neural Topic Modeling in Multi-view Embedding Spaces (2021.naacl-main)
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| Challenge: | Recent work has used pre-trained word embeddings to address data sparsity in short-text or small document collections. |
| Approach: | They propose a neural topic modeling framework using multi-view embedding spaces to improve topic quality and deal with polysemy. |
| Outcome: | The proposed framework improves topic quality and deal with polysemy. |
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks (D19-1)
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| Challenge: | Existing models ignore conflict opinions because they are sparse in the datasets. |
| Approach: | They propose a multi-label classification model with dual attention mechanism to address these problems by excluding conflict opinions from existing models. |
| Outcome: | The proposed model addresses the problem of exclusion of conflict opinions from the datasets. |
Continuous Relational Diffusion Driven Topic Model with Multi-grained Text for Microblog (2024.lrec-main)
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| Challenge: | Existing topic models assume that there are only 0/1-state relationships between the two parties in social networks, but the relationship status in real life is more complicated. |
| Approach: | They propose a topic model that leverages unsupervised learning to mine hidden topics in document collections using multi-grained text. |
| Outcome: | The proposed model can be applied to microblog with multi-grained text to realize the representation of the relationship state and make up for the context and structural information lost by previous representation methods. |
Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios (2022.findings-emnlp)
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| Challenge: | Behavioral coding is a procedure that requires human intervention to be performed manually. |
| Approach: | They propose to use a publicly available conversation-based dataset to transfer knowledge to a low-resource behavioral coding task by meta-learning. |
| Outcome: | The proposed framework predicts target behaviors more accurately than baseline models. |
Label-Synchronous Neural Transducer for E2E Simultaneous Speech Translation (2024.acl-long)
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| Challenge: | LS-Transducer-SST dynamically decides when to emit translation tokens based on an Auto-regressive Integrate-and-Fire (AIF) mechanism. |
| Approach: | They propose a label-synchronous neural transducer for simultaneous speech translation which dynamically decides when to emit translation tokens based on an Auto-regressive Integrate-and-Fire mechanism. |
| Outcome: | The proposed model can predict translation tokens on monolingual text-only data or can be used in both decoding and training. |
Improving Dialogue Discourse Parsing via Reply-to Structures of Addressee Recognition (2023.emnlp-main)
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| Challenge: | Existing approaches to learn dialogue discourse parsing with related tasks require additional annotation, thus limiting their generality. |
| Approach: | They propose a multitasking framework that integrates dialogue discourse parsing with addressee recognition to reflect relation-based structure of dialogue. |
| Outcome: | The proposed framework outperforms baselines on the Molweni and STAC datasets. |
BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources (2026.acl-long)
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| Challenge: | Existing reviews focus on a few high-resource languages or embed Indian languages within broad multilingual settings, limiting coverage of low-resourced and culturally diverse varieties. |
| Approach: | They present a unified survey of Indian NLP resources, covering 200+ datasets, 50+ benchmarks, and 100+ models, tools, and systems across text, speech, multimodal, and culturally grounded tasks. |
| Outcome: | The proposed survey covers 200+ datasets, 50+ benchmarks, and 100+ models, tools, and systems across text, speech, multimodal, and culturally grounded tasks. |
AGILe: The First Lemmatizer for Ancient Greek Inscriptions (2022.lrec-1)
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| Challenge: | Existing models for ancient Greek inscriptions are not performant on epigraphic data due to language differences . a lemmatizer for ancient inscription data can enable meaningful generalizations, we show . |
| Approach: | They propose to train an automatic lemmatizer for ancient Greek inscriptions with 80% accuracy . they also show that existing models are not performant on epigraphic data . |
| Outcome: | The proposed model achieves above 80% accuracy on epigraphic data, and makes it available to the community. |
Target-Side Augmentation for Document-Level Machine Translation (2023.acl-long)
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| Challenge: | Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data. |
| Approach: | They propose a document-level machine translation model that generates many potential translations for each source document and smoothes the distribution. |
| Outcome: | The proposed method outperforms the previous best system by 2.30 s-BLEU on News and achieves new state-of-the-art on News . |
Low-resource Neural Machine Translation: Benchmarking State-of-the-art Transformer for Wolof<->French (2022.lrec-1)
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| Challenge: | Neural machine translation (NMT) systems can translate between French (FR) 1 and Wolof (WO, ISO 639-3), a lowresource Niger-Congo language mainly spoken in Senegal (Gamble, 1950). |
| Approach: | They propose two neural machine translation systems based on sequence-to-sequence with attention and Transformer architectures to translate between French (FR) 1 and Wolof (WO, ISO 639-3). |
| Outcome: | The proposed models outperform the classic sequence-to-sequence model in all settings and are less sensitive to noise. |
Hypergraph-Based Session Modeling: A Multi-Collaborative Self-Supervised Approach for Enhanced Recommender Systems (2024.lrec-main)
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| Challenge: | Presently, graph-based recommendations are limited by session dependencies and data sparsity in real-world scenarios. |
| Approach: | They propose a method which uses multi-collaborative self-supervised learning in hypergraph neural networks to model item transitions and to mitigate the challenges of data sparsity. |
| Outcome: | The proposed method outperforms existing methods in a number of domains and consistently outperformed existing methods. |
Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users (2024.lrec-main)
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Yejin Kim, Scott Rome, Kevin Foley, Mayur Nankani, Rimon Melamed, Javier Morales, Abhay K. Yadav, Maria Peifer, Sardar Hamidian, H. Howie Huang
| Challenge: | Current approaches focus on improving ranking performance at the cost of escalating complexity and complicating the task. |
| Approach: | They propose a hybrid multi-task learning approach that trains on user-item and item-i item interactions. |
| Outcome: | The proposed approach improves accuracy, relevance, and diversity of user recommendations even for cold-start users. |
Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction (2023.findings-acl)
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| Challenge: | Existing models for few-shot relation extraction (RE) are not suitable for continual few-sshot RE. |
| Approach: | They propose a new model to train a model for new relations with few labeled training data. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
Explaining Matters: Leveraging Definitions and Semantic Expansion for Sexism Detection (2025.acl-long)
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| Challenge: | Existing tools for sexism detection fail to capture subtle distinctions within sexist content, limiting their practical applicability. |
| Approach: | They propose two techniques to address class imbalance and nuanced nature of sexist language . definition-based data augmentation leverages category-specific definitions to generate semantically-aligned examples . |
| Outcome: | The proposed techniques improve accuracy across all tasks and improve reliability. |
SynGraph: A Dynamic Graph-LLM Synthesis Framework for Sparse Streaming User Sentiment Modeling (2025.findings-acl)
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| Challenge: | Traditional sentiment analysis methods focus on static reviews, failing to capture temporal relationship between user sentiment rating and textual content. |
| Approach: | They propose a dynamic graph-based framework that addresses data sparsity in streaming reviews. |
| Outcome: | The proposed framework reduces data sparsity by categorizing users into mid-tail, long-tail and extreme scenarios and incorporating LLM enhancements within a dynamic graph-based structure. |
Zero-shot Learning for Multilingual Discourse Relation Classification (2024.lrec-main)
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| Challenge: | Discourse analysis is a hard task, but data is limited for other languages. |
| Approach: | They propose to use zero-shot learning to combine discourse relation data . they compare two versions of the same text with different labels . |
| Outcome: | The proposed method can be applied to languages, frameworks, or similarity measures. |