Papers with data augmentation
Controllable Data Augmentation for Few-Shot Text Mining with Chain-of-Thought Attribute Manipulation (2024.findings-acl)
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| Challenge: | Existing methods for data augmentation generate new examples wildly without proper control, which hinders the usefulness of the proposed approach. |
| Approach: | They propose a chain-of-thought attribute manipulation approach that generates new data from existing examples by tweaking in the user-provided attribute. |
| Outcome: | The proposed approach generates new data from existing examples by tweaking in the user-provided, task-specific attribute, e.g., sentiment polarity or topic in movie reviews. |
Investigation on Data Adaptation Techniques for Neural Named Entity Recognition (2021.acl-srw)
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| Challenge: | Existing methods for named entity recognition use only a limited number of samples . data augmentation and selftraining are popular methods to generate additional synthetic data . |
| Approach: | They investigate the impact of data augmentation and data augmented on named entity recognition tasks. |
| Outcome: | The proposed methods improve the performance of three named entity recognition tasks. |
NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering (2021.naacl-demos)
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| Challenge: | Using a node-based framework, knowledge base question answering systems can grasp structural mappings between questions and KB queries. |
| Approach: | They propose a node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question. |
| Outcome: | The proposed framework outperforms the previous SoTA on CCKS CKBQA dataset. |
Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management (2021.tacl-1)
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| Challenge: | Existing datasets limited in size con- sidering complexity of dialogues . current trends lean towards end-to-end models while modular systems tend to be preferred in industrial applications. |
| Approach: | They propose a graph-based representation of dialogues that can be exploited for data augmentation, multi- reference training and evaluation of non-deterministic agents. |
| Outcome: | The proposed graph-based representation of dialogues can be exploited for data augmentation, multi- reference training and evaluation of non-deterministic agents. |
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization (2022.naacl-srw)
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| Challenge: | Recent studies indicated that neural methods are governed by the scaling law for the amount of training data. |
| Approach: | They propose a low-cost strategy to augment training data for abstractive summarization tasks by extracting summarized text and paraphrasing it. |
| Outcome: | The proposed strategy outperforms back-translation and self-training and is more cost-efficient when training data is small. |
UnifiedGEC: Integrating Grammatical Error Correction Approaches for Multi-languages with a Unified Framework (2025.coling-demos)
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| Challenge: | Existing tools for GEC have been developed to support research on grammatical errors, but there is no comprehensive evaluation on these models. |
| Approach: | They propose an open-source framework for Grammatical Error Correction that integrates 5 widely-used GEC models and compares their performance on 7 datasets in different languages. |
| Outcome: | The proposed framework compares 5 widely-used models on 7 datasets in different languages. |
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 . |
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding (2022.findings-aacl)
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| Challenge: | Meta-Learning requires a large number of training tasks to learn representations that transfer well to unseen tasks. |
| Approach: | They propose a method which synthesizes new tasks by linearly interpolating existing tasks. |
| Outcome: | The proposed method outperforms baselines and does not degrade performance even when it is high. |
Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking (D19-55)
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Nathanael Chambers, Timothy Forman, Catherine Griswold, Kevin Lu, Yogaish Khastgir, Stephen Steckler
| Challenge: | Illicit activity on the Web often obscures information between client and seller, such as the seller’s phone number. |
| Approach: | They propose to use a dataset to model adversarial noise in a text extraction system and propose a visual character language model to interpret unseen unicode characters. |
| Outcome: | The proposed model improves number recognition by 89% over a CRF with a CNN and shows that unicode characters can be translated to unicoding. |
KNU-HYUNDAI’s NMT system for Scientific Paper and Patent Tasks onWAT 2019 (D19-52)
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Cheoneum Park, Young-Jun Jung, Kihoon Kim, Geonyeong Kim, Jae-Won Jeon, Seongmin Lee, Junseok Kim, Changki Lee
| Challenge: | We submitted our transformer-based neural machine translation system to the translation tasks of the 6th workshop on Asian Translation (WAT 2019). |
| Approach: | They propose a transformer-based neural machine translation system for Chinese-Japanese, English-Japanese, and Korean->Japanoise translation tasks. |
| Outcome: | The proposed system performed well on the two translation tasks and was ranked first in terms of the BLEU scores in all the JPC2 subtasks. |
Data-Efficient Methods For Improving Hate Speech Detection (2023.findings-eacl)
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| Challenge: | Existing methods for hate speech detection are data-hungry and require large datasets. |
| Approach: | They propose an input-level data augmentation technique EasyMix to improve hate speech detection in english and multilingual datasets. |
| Outcome: | The proposed method improves the performance across english and multilingual datasets by 1% and 2-8%. |
AMR Parsing as Sequence-to-Graph Transduction (P19-1)
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| Challenge: | Abstract Meaning Representation (AMR) parsing is the task of transducing natural language text into AMR, a graphbased formalism used for capturing sentence-level semantics. |
| Approach: | They propose a model that treats AMR parsing as sequence-to-graph transduction by aligner-free, and can be effectively trained with limited amounts of labeled AMR data. |
| Outcome: | The proposed model outperforms all previously reported SMATCH scores on AMR 2.0 (76.3%) and AMR 1.0 (70.2%). |
Delexicalized Paraphrase Generation (2020.coling-industry)
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| Challenge: | Using convolutional neural networks, we generate delexicalized sentences . 1.29% accuracy is achieved with the generated paraphrases . |
| Approach: | They propose a neural paraphrasing model that generates delexicalized sentences . they use convolutional neural networks to pool on slot values and use pointers to locate them . |
| Outcome: | The proposed model generates delexicalized sentences with high quality . it can be used for intent classification and named entity recognition tasks . |
What is it? Towards a Generalizable Native American Language Identification System (2025.naacl-srw)
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| Challenge: | Despite their cultural and historical significance, Native American languages remain unsupported by major commercial language identification systems. |
| Approach: | They propose to curate linguistic resources across all Native American languages for robust training and tailor data augmentation to generate synthetic yet linguistically coherent training samples. |
| Outcome: | The proposed system would be generalizable across all Native American languages . it would also generate coherent training samples for low-resource languages based on Plains Apache . |
Efficient Semi-supervised Consistency Training for Natural Language Understanding (2022.naacl-industry)
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| Challenge: | Manually labeled training data is expensive, noisy, and often scarce . semi-supervised learning methods can be used to improve model performance . |
| Approach: | They explore different methods for consistency training on unlabeled data . they use human paraphrasing, back-translation, and dropout to augment unlabed data. |
| Outcome: | The proposed methods outperform purely supervised learning on unlabeled data. |
Graph Propagation based Data Augmentation for Named Entity Recognition (2023.acl-short)
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| Challenge: | Synthetic data suffer from poor diversity, which leads to performance limitations. |
| Approach: | They propose a graph-propagated data augmentation framework for named entity recognition that uses graph propagation to build relationships between labeled data and unlabeled natural texts. |
| Outcome: | The proposed framework improves on a low-resource named entity recognition dataset. |
Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking (2020.acl-main)
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| Challenge: | Existing techniques for zero-shot transfer learning for multi-domain dialogue state tracking are expensive and require human errors, delays in annotation, and normalization issues. |
| Approach: | They propose a zero-shot transfer learning technique where training data are synthesized from an abstract dialogue model and the ontology of the domain. |
| Outcome: | The proposed technique improves the state of the art on the multi-domain dialogue state tracking dataset by 21%. |
Data Augmentation by Data Noising for Open-vocabulary Slots in Spoken Language Understanding (N19-3)
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| Challenge: | Neural networks are used to understand spoken language understanding (SLU) but it is difficult to recognize the slots of unknown words or ‘open-vocabulary’ slots because of the high cost of creating a manually tagged SLU dataset. |
| Approach: | They propose to use a recurrent neural network to nois slots for data augmentation by using an attention-based bi-directional recurrence neural network. |
| Outcome: | The proposed method achieves performance improvements of up to 0.57% and 3.25 in intent prediction (accuracy) and slot filling (f1-score) and 0.53% accuracy. |
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering (2024.acl-long)
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| Challenge: | Recent advances in few-shot question answering rely on pre-trained large language models and fine-tuning in specific settings. |
| Approach: | They propose to select the most informative data for fine-tuning to improve efficiency . they use an approximate graph algorithm and unsupervised question generation to generate QA pairs . |
| Outcome: | The proposed framework improves the performance of the few-shot question answering task on the open-domain QA task. |
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP (2020.emnlp-demos)
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| Challenge: | TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets. |
| Approach: | They introduce a Python framework for adversarial attacks, data augmentation, and adversarially training in NLP. |
| Outcome: | This paper introduces a Python framework for adversarial attacks, data augmentation, and adversarially training in NLP. |
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. |
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings (2021.acl-srw)
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| Challenge: | Unsupervised cross-lingual word embedding methods learn a linear transformation matrix that maps two monolingual embeddable spaces that are separately trained with monolingual corpora. |
| Approach: | They propose a method that maps two monolingual embedding spaces that are separately trained with monolingual corpora using a pseudo-parallel corpus. |
| Outcome: | The proposed method outperforms other methods given the same amount of data and shows that using a pseudo-parallel corpus makes the source and target corpora (partially) parallel . |
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation (2021.naacl-srw)
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| Challenge: | Neural machine translation is known to show poor performance at long sentence translations . however, when the sentence length exceeds a certain value, the quality of NMT becomes inferior to that of statistical machine translation. |
| Approach: | They propose a method that uses given parallel corpora as train data to generate long sentences by concatenating two sentences at random. |
| Outcome: | The proposed method improves translation quality more when combined with back-translation. |
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data (D19-50)
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| Challenge: | Popular NLP tasks such as sentiment analysis and event extraction from social media are examples of imbalanced classification problems. |
| Approach: | They propose a method to generalise on dissimilar training and test data using a measure of similarity between datasets. |
| Outcome: | The proposed method achieves the second highest score on sentence-level propaganda classification. |
Fluent Response Generation for Conversational Question Answering (2020.acl-main)
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| Challenge: | Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA subtask. |
| Approach: | They propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. |
| Outcome: | The proposed model outperforms baseline CoQA and QuAC models in generating conversational responses. |
CEAN: Contrastive Event Aggregation Network with LLM-based Augmentation for Event Extraction (2024.eacl-long)
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| Challenge: | Event Extraction is a crucial yet arduous task in natural language processing (NLP), as its performance is hindered by laborious data annotation. |
| Approach: | They propose a Contrastive Event Aggregation Network with LLM-based Augmentation to promote low-resource learning and reduce data noise for event extraction. |
| Outcome: | The proposed approach achieves new state-of-the-art results on the ACE2005 and ERE-EN datasets. |
A Multi-Level Optimization Framework for End-to-End Text Augmentation (2022.tacl-1)
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| Challenge: | Existing methods for text augmentation perform data augmentation and downstream tasks separately. |
| Approach: | They propose a framework to perform text augmentation and the downstream task end-to-end. |
| Outcome: | The proposed framework performs text augmentation and the downstream task end-to-end on a text classification dataset. |
Retrieval Based Response Letter Generation For a Customer Care Setting (2022.naacl-industry)
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| Challenge: | Letter-like communications are a major means of customer relationship management . despite advances in natural language processing, the task of generating a response is time-consuming . |
| Approach: | They propose a deep-learning based response letter generation framework that uses data augmentation to retrieve knowledge from historical responses and utilize it to generate an appropriate response. |
| Outcome: | The proposed model outperforms baselines by significant margins while producing consistent and informative responses. |
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation (2021.acl-srw)
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| Challenge: | Existing data augmentation methods for reading comprehension lack robustness to challenge sets whose distribution is different from that of training sets. |
| Approach: | They propose a question-answer pair generation method that generates multiple diverse QA pairs from a paragraph to mitigate this problem. |
| Outcome: | The proposed model improves the accuracy of 12 challenge sets and the in-distribution accuracy. |
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models (2023.eacl-main)
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| Challenge: | Existing studies show that pretrained language models can act as knowledge bases and reason like humans. |
| Approach: | They propose to use pretrained language models to generate free-flow textual explanations about 52 health conditions across three clinical dimensions. |
| Outcome: | The proposed model can generate concise and readable text, but can be improved on medical accuracy and QA. |
Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification (2021.emnlp-main)
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| Challenge: | Existing approaches to improve generalization ability by augmenting training data with synonymous examples or adding random noises to word embeddings cannot address spurious association problem. |
| Approach: | They propose an end-to-end reinforcement learning framework which jointly performs counterfactual data generation and dual sentiment classification. |
| Outcome: | The proposed framework outperforms strong data augmentation baselines on several benchmark sentiment classification datasets. |
SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task (D19-52)
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| Challenge: | Several data sets were provided for the task, but the data was limited and the distance and richness of the language pair were very challenging. |
| Approach: | They describe the SYSTRAN neural MT systems employed for the 6th Workshop on Asian Translation (WAT) they use the neural Transformer architecture learned over the provided resources and perform synthetic data generation experiments which aim at alleviating the data scarcity problem. |
| Outcome: | The proposed systems rank first according to automatic evaluations based on the neural Transformer architecture learned over the provided resources. |
Simple and effective data augmentation for compositional generalization (2024.naacl-long)
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| Challenge: | Compositional generalization is the ability of a system to correctly predict the meaning of complex sentences when trained on simpler sentences. |
| Approach: | They propose to use data augmentation methods to generate additional training data by sampling from an augmentation distribution to generalize to the out-of-distribution test data. |
| Outcome: | The proposed method outperforms existing methods that sampled from the training distribution and outperformed existing methods. |
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing approaches to improve performance of deep neural models are limited by the nature of spurious patterns in the data. |
| Approach: | They propose to use augmented data to generate spurious patterns in NLP models . they propose to generate counterfactual data for data augmentation and explanation . |
| Outcome: | The proposed approach improves performance on augmented data and on human-generated data. |
Understanding tables with intermediate pre-training (2020.findings-emnlp)
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| Challenge: | Textual entailment is well studied, but is less well studied for table enlargement . a new dataset of millions of examples is used to train the model . |
| Approach: | They adapt a table-based BERT model to recognize entailment from a dataset . they evaluate table pruning techniques as a pre-processing step to improve model efficiency . |
| Outcome: | The proposed model improves training and prediction efficiency at a moderate drop in accuracy. |
Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation (2022.acl-short)
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| Challenge: | End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations. |
| Approach: | They propose a method that augments transcriptions by sampling from suffix memory and translating them into target languages. |
| Outcome: | The proposed method delivers up to 0.9 and 1.1 BLEU points on top of augmentation with knowledge distillation on languages on CoVoST 2 and Europarl-ST. |
Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study (2024.eacl-short)
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| Challenge: | pharmacovigilance event extraction is a key field of healthcare that involves identifying, evaluating, understanding, and preventing adverse effects. |
| Approach: | They investigate the ability of large language models (LLMs) to extract adverse events from medical text. |
| Outcome: | The proposed model performs reasonably well with demonstration selection strategies, but falls short compared to fully fine-tuned small models. |
Consistent Text Categorization using Data Augmentation in e-Commerce (2023.acl-industry)
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| Challenge: | Upon closer inspection, we found inconsistencies in the labeling of similar items. |
| Approach: | They propose to improve an existing product categorization model that takes a product title as input and outputs the most suitable category out of thousands of available candidates. |
| Outcome: | The proposed model is based on a product title and outputs the most suitable category out of thousands of available candidates. |
Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function (P19-2)
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| Challenge: | Existing methods to reduce gender bias in natural language datasets are inadequate. |
| Approach: | They propose a loss function modification approach which equalizes the probabilities of male and female words in the output. |
| Outcome: | The proposed approach outperforms existing methods in several aspects, especially in reducing gender bias in occupation words. |
Robustness to Capitalization Errors in Named Entity Recognition (D19-55)
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| Challenge: | Existing methods to improve robustness to noise discard given orthographic information, which significantly degrades models' performance on well-formed text. |
| Approach: | They propose a method which allows models to learn to utilize or ignore orthographic information depending on its usefulness in the context. |
| Outcome: | The proposed approach achieves competitive robustness to capitalization errors while making negligible compromises on well-formed text and significantly improving generalization power on noisy user-generated text. |
So Different Yet So Alike! Constrained Unsupervised Text Style Transfer (2022.acl-long)
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| Challenge: | Automated transfer of text between domains does not maintain other attributes between the source and translated text. |
| Approach: | They propose a method for automatic transfer of text between domains that preserves semantic content but changes other attributes. |
| Outcome: | The proposed method retains lexical, syntactic and domain-specific constraints between domains for multiple benchmark datasets, including ones where more than one attribute change. |
Fast Bilingual Grapheme-To-Phoneme Conversion (2022.naacl-industry)
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| Challenge: | Autoregressive transformers (ART)-based grapheme-to-phoneme (G2P) models have been proposed for bi/multilingual text-to speech systems. |
| Approach: | They propose a bilingual grapheme-to-phoneme (G2P) model with autoregressive transformers for fast and exact decoding and data augmentation for predicting output length. |
| Outcome: | The proposed model achieves better performance than the previous model and 2700% faster inference speed. |
Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognition (2021.tacl-1)
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| Challenge: | Name Regularity Bias is a problem in NER models that use contextual information to predict the type of an ambiguous entity. |
| Approach: | They propose a model-agnostic training method that adds learnable adversarial noise to some entity mentions to improve their accuracy. |
| Outcome: | The proposed method outperforms feature-based models on name regularity bias . it adds learnable adversarial noise to some entity mentions, leading to gains . |
SpatialMath: Spatial Comprehension-Infused Symbolic Reasoning for Mathematical Problem-Solving (2026.findings-eacl)
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| Challenge: | Current models struggle to accurately decompose intricate visual inputs and connect perception with structured reasoning, leading to suboptimal performance. |
| Approach: | They propose a Spatial Comprehension-Infused Symbolic Reasoning Framework to integrate spatial representations into structured symbolic reasoning chains. |
| Outcome: | The proposed framework outperforms existing models in vision-intensive mathematical problems. |
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. |
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers (2023.findings-eacl)
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| Challenge: | Recent studies show that pre-trained language models (PLMs) often predict over-confidently. |
| Approach: | They propose to use ensemble learning and data augmentation to improve confidence calibration for PLMs by combining calibration techniques with a trade-off between accuracy and classification. |
| Outcome: | The proposed calibration method improves classification accuracy and confidence in pre-trained language models by combining several calibration techniques. |
PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners (2023.eacl-main)
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| Challenge: | Existing studies on prompt-based few-shot tuning focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs. |
| Approach: | They propose a framework that leverages label semantics for prompt-based tuning. |
| Outcome: | The proposed framework improves on few-shot text classification tasks by leveraging label semantics and data augmentation. |
Better Quality Estimation for Low Resource Corpus Mining (2022.findings-acl)
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| Challenge: | State-of-the-art Quality Estimation models lack robustness to out-of domain examples. |
| Approach: | They propose a method that uses multitask training, data augmentation and contrastive learning to achieve better and more robust QE performance. |
| Outcome: | The proposed method improves QE performance significantly in the MLQE challenge and the robustness of QE models when tested in the Parallel Corpus Mining setup. |
CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction (2022.lrec-1)
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| Challenge: | a corpus of English crude oil news for event extraction is presented . the corpus contains 425 news articles with approximately 11k events annotated . |
| Approach: | They present a corpus of English Crude Oil news for event extraction . it is the first of its kind for Commodity News and contributes to text mining . |
| Outcome: | The proposed corpus of English crude oil news is the first of its kind for Commodity News . the annotated news articles are compared with the standard news articles . |
Contrastive Fine-tuning Improves Robustness for Neural Rankers (2021.findings-acl)
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| Challenge: | Current state-of-the-art neural rankers can deteriorate when exposed to noisy inputs or applied to a new domain. |
| Approach: | They propose a contrastive loss and ranking loss method for fine-tuning rankers that combine ranking loss and rank loss to improve their robustness to query reformulations and noise perturbations. |
| Outcome: | The proposed method outperforms data augmentation for robustifying rankers on four passage ranking datasets. |
Multilingual Coreference Resolution in Multiparty Dialogue (2023.tacl-1)
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| Challenge: | Existing datasets for entity coreference resolution are limited to English and other languages are rare. |
| Approach: | They propose to use TV transcripts to create multilingual multiparty coreference datasets that leverage existing subtitles in Chinese and Farsi. |
| Outcome: | The proposed dataset re-annotates for coreference on TV transcripts and then leverages existing subtitle translations to create a multilingual corpus. |
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation (2021.naacl-main)
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Alexander Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, Yashar Mehdad
| Challenge: | Recent advances in summarization are driven by the availability of large datasets such as the CNN-DailyMail corpus and the New York Times corpus. |
| Approach: | They propose a method for fine-tuning pretrained models for summarization in unsupervised manner . they use Wikipedia data to produce pseudo-summaries which contain characteristics of target dataset . |
| Outcome: | The proposed method achieves state-of-the-art, zero-shot abstractive summarization performance on CNN-DailyMail dataset and compares with other methods on other datasets. |
How Well Do Multi-hop Reading Comprehension Models Understand Date Information? (2022.aacl-short)
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| Challenge: | Existing multi-hop reading comprehension datasets have reasoning shortcuts that can be used to answer comparison questions without performing multi- hop reasoning. |
| Approach: | They propose a dataset with three probing tasks in addition to the main question . they then evaluate the model's ability to understand date information . |
| Outcome: | The proposed model performs well in date comparison and number subtraction tasks. |
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning (2023.acl-short)
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| Challenge: | Recent advances in pre-trained language models have been limited when fine-tuned on small datasets. |
| Approach: | They propose to add contrastive learning to prompt-based fine-tuning to improve model performance. |
| Outcome: | The proposed approach outperforms other methods on multiple text classification benchmarks. |
Guidance-Based Prompt Data Augmentation in Specialized Domains for Named Entity Recognition (2024.acl-short)
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| Challenge: | specialized fields such as science and biology face significant challenges due to the scarcity of quality data. |
| Approach: | They propose a guidance data augmentation technique that abstracts context and sentence structure and maintains context-entity relationships for DA. |
| Outcome: | The proposed method enhances the training performance of named entity recognition tasks while maintaining context-entity relationships. |
Arithmetic Reasoning with LLM: Prolog Generation & Permutation (2024.naacl-short)
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| Challenge: | Existing work has shown that large language models can generate arithmetic and commonsense reasoning, but they are not native to mathematical operations and symbolic manipulations. |
| Approach: | They propose to use large language models to generate Prolog programs to solve math problems using a code interpreter to generate arithmetic and symbolic formulas. |
| Outcome: | The proposed model outperforms CoT generation in the GSM8K benchmark across three LLMs. |
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions (2023.tacl-1)
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| Challenge: | Existing studies on the ability of a model to make consistently correct predictions in the presence of perturbations have not been conducted in open-domain question answering (OpenQA). |
| Approach: | They propose a query-side contrastive loss to improve the dense passage retriever (DPR) to improve DPR training. |
| Outcome: | The proposed approach improves the density of the dense passage retriever (DPR) training set without sacrificing accuracy on standard test sets. |
A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution (2024.naacl-long)
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| Challenge: | Existing state-of-the-art event coreference resolution systems rely on spurious and spurious associations in the input mention pair text. |
| Approach: | They propose a rationale-centric counterfactual data augmentation method that leverages the debiasing capability of counterfact data haussed by LLM-in-the-loop to mitigate spurious association while emphasizing causation. |
| Outcome: | The proposed method achieves state-of-the-art on three popular cross-document benchmarks and demonstrates robustness in out-of domain scenarios. |
Self-Training Pre-Trained Language Models for Zero- and Few-Shot Multi-Dialectal Arabic Sequence Labeling (2021.eacl-main)
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| Challenge: | Existing approaches to fine-tune pre-trained language models for downstream tasks require labeled data. |
| Approach: | They propose to self-train pre-trained language models to improve performance on data-scarce varieties by as large as 10% F1 and 2% accuracy. |
| Outcome: | The proposed model improves zero-shot MSA-to-DA transfer by as large as 10% F1 (NER) and 2% accuracy (POS tagging). |
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding (2021.findings-emnlp)
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| Challenge: | Knowledge Distillation (KD) is a model compression algorithm that helps transfer knowledge in a large neural network into a smaller one. |
| Approach: | They propose a framework to assess adversarial robustness of multiple KD algorithms. |
| Outcome: | The proposed algorithm achieves state-of-the-art on the GLUE benchmark and out-of domain generalization and adversarial robustness compared to competitive methods. |
Learning High-Quality and General-Purpose Phrase Representations (2024.findings-eacl)
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| Challenge: | Pre-trained language models for phrasal embeddings are unnecessarily complex and require to be pre-tuned on a corpus with context sentences. |
| Approach: | They propose a framework to learn phrase representations in a context-free fashion. |
| Outcome: | The proposed framework generates superior phrase embeddings while requiring a smaller model size. |
Factual Relation Discrimination for Factuality-oriented Abstractive Summarization (2023.findings-emnlp)
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| Challenge: | Existing factuality-oriented abstractive summarization models only consider the integration of factual information and ignore the causes of factuual errors. |
| Approach: | They propose a factuality-oriented abstractive summarization model that can identify the causes of factual errors. |
| Outcome: | The proposed model outperforms state-of-the-art models in factual metrics. |
From Sentences to Proof Trees: Leveraging Language Models for Structured Reasoning (2026.eacl-srw)
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| Challenge: | Multi-hop reasoning requires a chain of facts to reflect the reasoning behind the answer. |
| Approach: | They propose an inference-guided prompting approach that performs well in natural language questions . they propose a neuro-symbolic approach to reasoning using large language models . |
| Outcome: | The proposed model outperforms all prompting strategies and fine-tunes LLMs trained specifically for proof generation. |
Exploring Zero and Few-shot Techniques for Intent Classification (2023.acl-industry)
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| Challenge: | Intent classification is the primary natural language understanding task for a virtual agent or a chatbot. |
| Approach: | They propose four different approaches to zero-shot intent classification with low-resource constraints . they use domain adaptation, data augmentation, and parametric fine-tuning to achieve this . |
| Outcome: | The proposed approaches perform well in low-resource settings for zero/few-shot intent classification . the proposed methods remove or substantially reduce the work to provide intent-utterances . |
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations (N18-2)
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| Challenge: | Neural network-based models for NLP have been growing with state-of-the-art results in various tasks. |
| Approach: | They propose a data augmentation method for labeled sentences called contextual augmentation. |
| Outcome: | The proposed method improves classifiers based on convolutional or recurrent neural networks. |
An End-to-End Contrastive Self-Supervised Learning Framework for Language Understanding (2022.tacl-1)
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| Challenge: | Existing approaches to learning data representations using contrastive learning perform data augmentation and contrastive training separately. |
| Approach: | They propose a framework that performs data augmentation and contrastive learning end-to-end . they propose to combine data augmented with text encoders to optimize for contrastive training . |
| Outcome: | Experiments on GLUE and Gururangan datasets show the proposed framework is effective in NLP. |
Generalization in Instruction Following Systems (2021.naacl-main)
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| Challenge: | Understanding and executing natural language instructions in a grounded domain is one of the hallmarks of artificial intelligence. |
| Approach: | They propose a learning strategy that involves data augmentation to improve the model's performance. |
| Outcome: | The proposed learning strategy outperforms state-of-the-art models in the blocks world domain while satisfying our expectations much better. |
[MASK] Insertion: a robust method for anti-adversarial attacks (2023.findings-eacl)
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| Challenge: | Existing studies have focused on adversarial defenses against pretrained language models. |
| Approach: | They propose an adversarial defensing algorithm that inserts tokens into input sequences . they show an improvement in accuracy between 3.2 and 11.1 absolute points . |
| Outcome: | The proposed algorithm improves model accuracy on clean and polluted inputs compared with state-of-the-art models . |
Semi-supervised Relation Extraction via Data Augmentation and Consistency-training (2023.eacl-main)
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| Challenge: | Obtaining high-quality human labelled data is an expensive and noisy process. |
| Approach: | They propose to leverage unlabelled data to improve the sample efficiency of the models. |
| Outcome: | The proposed methods can be used to extract the Cause-Effect relation between a given head entity and tail entity based on context in the input sentence. |
AD-LLM: Benchmarking Large Language Models for Anomaly Detection (2025.findings-acl)
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Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao
| Challenge: | Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. |
| Approach: | They propose a benchmark that evaluates how large language models (LLMs) can help with NLP anomaly detection. |
| Outcome: | The proposed model can perform zero-shot detection without tasks-specific training, data augmentation and model selection, and it can suggest unsupervised AD models. |
Semantically Equivalent Adversarial Rules for Debugging NLP models (P18-1)
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| Challenge: | Complex machine learning models are often brittle, making different predictions for input instances that are extremely similar semantically. |
| Approach: | They propose to generalize semantically equivalent adversarial rules that induce adversaries on many instances to detect brittle models. |
| Outcome: | The proposed rules generate high-quality local adversaries for more instances than humans and induce four times as many mistakes as human experts. |
Towards more equitable question answering systems: How much more data do you need? (2021.acl-short)
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| Challenge: | Question answering datasets in English are relatively new, but lack of linguistic diversity in the field is a challenge. |
| Approach: | They propose to use translation and cross-lingual transfer to produce QA systems in multiple languages to improve their performance. |
| Outcome: | The proposed approaches take advantage of existing resources to produce QA systems in multiple languages. |
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing (2023.tacl-1)
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| Challenge: | Existing work on cross-lingual semantic parsing has focused on English . a few-shot approach to parse from natural languages is comparatively unexplored . |
| Approach: | They propose a method that minimizes cross-lingual divergence between probabilistic latent variables by Optimal Transport. |
| Outcome: | The proposed method improves performance even without parallel input translations on two datasets. |
GerMedIQ: A Resource for Simulated and Synthesized Anamnesis Interview Responses in German (2025.acl-srw)
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Justin Hofenbitzer, Sebastian Schöning, Belle Sebastian, Jacqueline Lammert, Luise Modersohn, Martin Boeker, Diego Frassinelli
| Challenge: | Text corpora in non-English clinical contexts is scarce due to privacy restrictions and restricted access to secure environments. |
| Approach: | They propose to use Large Language Models to generate synthetic data using a German medical interview questions corpus. |
| Outcome: | The proposed dataset generates comparable responses to human-generated questions. |
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs (2021.eacl-main)
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| Challenge: | Paraphrase generation requires many annotated paraphrase pairs, which are expensive to obtain. |
| Approach: | They propose a model that learns to disentangle the semantics and syntax of a sentence from unannotated texts. |
| Outcome: | The proposed model learns to disentangle the semantics and syntax of a sentence from a collection of unannotated texts. |
Generative Data Augmentation for Commonsense Reasoning (2020.findings-emnlp)
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Yiben Yang, Chaitanya Malaviya, Jared Fernandez, Swabha Swayamdipta, Ronan Le Bras, Ji-Ping Wang, Chandra Bhagavatula, Yejin Choi, Doug Downey
| Challenge: | Recent advances in commonsense reasoning depend on large-scale human-authored training data. |
| Approach: | They propose a generative data augmentation technique that augments human-authored training data by using pretrained language models. |
| Outcome: | The proposed technique outperforms existing methods on commonsense reasoning benchmarks and enhances out-of-distribution generalization. |
Few-shot learning through contextual data augmentation (2021.eacl-main)
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| Challenge: | Various strategies have been explored to learn from a journalist's post-edits . state-of-the-art APE systems require large numbers of post- edits for training . |
| Approach: | They propose to teach a pre-trained machine translation model to translate previously unseen words accurately . they extend a data augmentation approach to create training examples with similar contexts . |
| Outcome: | The proposed model improves accuracy on the scale of one to five examples with only 1 to 5 examples. |
Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting (N19-1)
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J. Edward Hu, Huda Khayrallah, Ryan Culkin, Patrick Xia, Tongfei Chen, Matt Post, Benjamin Van Durme
| Challenge: | Lexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in machine translation or monolingual text rewriting tasks. |
| Approach: | They propose a vectorized dynamic beam allocation algorithm which extends work in lexically-constrained decoding to work with batching. |
| Outcome: | The proposed method improves on natural language inference, question answering and machine translation tasks by fivefold . |
RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library (2026.findings-eacl)
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| Challenge: | Existing methods for generating high-quality reasoning data are limited in quality and availability. |
| Approach: | They propose a method that constructs mathematical operations and generates verifiable graphs that are back-translated into complex problems. |
| Outcome: | The proposed method achieves a 6.3% performance gain over existing methods on LLaMA-3-8B and outperforms others with only half the training data (50k vs. 100k). |
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks (2022.acl-short)
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| Challenge: | Experimental results show text smoothing outperforms data augmentation methods by a substantial margin. |
| Approach: | They propose to use a masked language model to convert a token to a smoothed representation by converting a sentence from its one-hot representation to 'controllable smoothes' they propose to combine text smoothing with other data augmentation methods to achieve better performance. |
| Outcome: | The proposed method outperforms mainstream data augmentation methods by a substantial margin on different datasets in a low-resource regime. |
Thesis Proposal: Auditing and Mitigating Demographic Bias in Multi-Stage Retrieval Systems for Criminal Justice Applications (2026.acl-srw)
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| Challenge: | racial descriptors alter embedding similarity scores and retrieval rankings, a new study shows . rife-specific biases can displace relevant records outside top-10 results, the study concludes . |
| Approach: | They propose to detect, measure, and mitigate racial bias in NLP systems deployed in criminal justice contexts . they propose to develop and evaluate debiasing techniques, validate synthetic findings on authentic law enforcement data . |
| Outcome: | The proposed research examines how bias propagates across retrieval pipelines . it shows that racial descriptors alter embedding similarity scores and retrieval rankings . |
Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges (2024.findings-acl)
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Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, Shafiq Joty
| Challenge: | Data augmentation (DA) is a key technique for enhancing model performance by diversifying training examples without the need for additional data collection. |
| Approach: | They examine various strategies that utilize LLMs for data augmentation, including a novel exploration of learning paradigms where LLM-generated data is used for diverse forms of further training. |
| Outcome: | The proposed approach addresses the primary open challenges faced by LLMs in the field of large language models and aims to serve as a comprehensive guide for researchers and practitioners. |
Computational Blueprints: Generating Isomorphic Math Problems with Large Language Models (2025.emnlp-industry)
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| Challenge: | Existing studies on mathematics problem generation focus on data augmentation rather than direct educational deployment. |
| Approach: | They propose a task to generate structurally consistent variants of source problems . they also propose 'Computational Blueprints for Isomorphic Twins' |
| Outcome: | The proposed task produces structurally consistent variants of source problems . the authors show that it is superior on generation accuracy and cost-effectiveness at scale . |
Rethinking Data Augmentation in Text-to-text Paradigm (2022.coling-1)
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| Challenge: | Existing approaches to augment training data are limited or marginal, or even diminishing or adverse especially given original training corpus is relatively sufficient or the backbone classifiers are PLM based. |
| Approach: | They propose to integrate text-to-text language models and construct a new two-phase framework for augmentation using two novel schemes. |
| Outcome: | The proposed framework synthesizes new samples benefiting from the knowledge learned from pre-trained language models on two public classification datasets and shows remarkable gains. |
Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining (2023.eacl-main)
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| Challenge: | Existing studies on robustness of pretrained multilingual models are limited to the English language. |
| Approach: | They propose to use data augmentation and contrastive loss term to boost robustness of multilingual models in cross-lingual settings. |
| Outcome: | The proposed model outperforms existing models on clean and noisy data in the cross-lingual setting. |
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation (D18-1)
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| Challenge: | Existing methods for data augmentation for text-based tasks such as machine translation are limited due to noise and noise. |
| Approach: | They propose a data augmentation policy with desirable properties as an optimization problem and propose 'SwitchOut' switchout randomly replaces words in both the source and target sentences with other random words from their corresponding vocabularies. |
| Outcome: | The proposed method outperforms strong alternatives such as word dropout on three translation datasets. |
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages (D19-1)
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| Challenge: | Large annotated treebanks are available for only a tiny fraction of the world's languages, and there is a wealth of literature on strategies for parsing with few resources. |
| Approach: | They propose three strategies for improving low-resource parsers: data augmentation, cross-lingual training, and transliteration. |
| Outcome: | The proposed methods improve low-resource parsers by using data augmentation, cross-lingual training, and transliteration. |
Label Anchored Contrastive Learning for Language Understanding (2022.naacl-main)
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| Challenge: | a novel approach to contrastive learning for language understanding is not fully explored . contrastive training has been widely applied to self-supervised representation learning . |
| Approach: | They propose a label anchored contrastive learning approach for language understanding using a class label. |
| Outcome: | The proposed approach improves on GLUE and CLUE benchmarks by 4.1% compared to the state-of-the-art approaches . the proposed approach also improves under the few-shot and data imbalance settings . |
AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports (2024.lrec-main)
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Lukas Lange, Marc Müller, Ghazaleh Haratinezhad Torbati, Dragan Milchevski, Patrick Grau, Subhash Chandra Pujari, Annemarie Friedrich
| Challenge: | Abstract: Natural language processing can help with managing large amounts of unstructured information. |
| Approach: | They propose to annotate a CC-BY-SA-licensed dataset of cyber threat reports . they use named entities, temporal expressions, and cybersecurity-specific concepts . |
| Outcome: | The proposed dataset annotates reports with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. |
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (C18-1)
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| Challenge: | Existing work which augments an utterance without considering its relation with other utterrances, however, has failed to improve the language understanding module. |
| Approach: | They propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data. |
| Outcome: | The proposed framework achieves 6.38 and 10.04 F-scores on the Airline Travel Information System dataset and a newly created semantic frame annotation on the Stanford Multi-turn, Multi-domain Dialogue Dataset. |
Always Keep your Target in Mind: Studying Semantics and Improving Performance of Neural Lexical Substitution (2020.coling-main)
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| Challenge: | Lexical substitution is a powerful technology used in various NLP applications . it generates plausible words that can replace a given word in a textual context . |
| Approach: | They propose to use a large-scale comparative study to compare lexical substitution methods . they compare existing and new methods using word sense induction datasets . |
| Outcome: | The proposed methods improve competitive results by incorporating information about the target word into the models. |
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 . |
Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection (2020.emnlp-main)
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| Challenge: | Existing approaches to disfluency detection heavily depend on labeled data. |
| Approach: | They propose a Planner-Generator based disfluency generation model that generates natural disfluent texts as augmented data. |
| Outcome: | The proposed model outperforms baselines and leads to state-of-the-art performance on Switchboard corpus. |
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration (2024.eacl-long)
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| Challenge: | Large language models (LLMs) have shown remarkable generalization capabilities, performing well on various tasks such as question answering (QA), complex reasoning, and code generation. |
| Approach: | They propose to augment training data of smaller language models with automatically generated counterfactuals (CF) instances to improve out-of-domain (OOD) performance of SLMs in extractive question answering setup. |
| Outcome: | The proposed approach improves out-of-domain (OOD) performance of small language models in extractive question answering setup. |
White-box Testing of NLP models with Mask Neuron Coverage (2022.findings-naacl)
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| Challenge: | Recent research has shown that black-box testing is not applicable to NLP models. |
| Approach: | They propose a set of white-box testing methods that are customized for transformer-based NLP models and adapt them to a black-box test suite. |
| Outcome: | The proposed methods can reduce testing suites by 60% while retaining failing tests, thereby concentrating faultdetection power of the test suite. |
Retrieval-guided Counterfactual Generation for QA (2022.acl-long)
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| Challenge: | Recent work shows that data augmentation using counterfactuals can help ameliorate this weakness. |
| Approach: | They propose a Retrieve-Generate-Filter technique to generate counterfactuals using QA framework and question generation model trained on original task data. |
| Outcome: | The proposed method improves performance on out-of-domain and challenging evaluation sets over and above existing methods. |
Constructing contrastive samples via summarization for text classification with limited annotations (2021.findings-emnlp)
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| Challenge: | Various contrastive learning methods have been developed and lead to state-of-the-art performance in many computer vision tasks. |
| Approach: | They propose a method to construct efficient contrastive samples using text summarization to gain better representations of text classification tasks with limited annotations. |
| Outcome: | The proposed framework gains better representations on text classification tasks with limited annotations and is compared with existing methods on real-world text classification datasets. |
Multi-View Incongruity Learning for Multimodal Sarcasm Detection (2025.coling-main)
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Diandian Guo, Cong Cao, Fangfang Yuan, Yanbing Liu, Guangjie Zeng, Xiaoyan Yu, Hao Peng, Philip S. Yu
| Challenge: | Existing methods for multimodal sarcasm detection rely on spurious correlations, demonstrating poor generalizability beyond training environments. |
| Approach: | They propose a method that integrates multimodal incongruities via contrastive learning for multimodal sarcasm detection by using three views to drive multi-view learning. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets and shows that it is more generalizable than existing methods. |
Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods to increase training data in low-resource domains may not be effective due to data scarcity. |
| Approach: | They propose a method to transform a high-resource domain into a low-resourced domain by changing its style-related attributes to generate synthetic data for training. |
| Outcome: | The proposed method can significantly improve results on five domain pairs under different data regimes. |
Table-based Fact Verification with Self-labeled Keypoint Alignment (2022.coling-1)
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| Challenge: | Existing methods for fact verification rely on graph feature or data augmentation but fail to investigate evidence correlation between statement and table effectively. |
| Approach: | They propose a self-labeled keypoint alignment model to explore correlation between statement and table . they propose integrating a mixture-of experts block to integrate interacted information . |
| Outcome: | The proposed model outperforms the state-of-the-art models and captures interpretable evidence words on three widely-studied datasets. |
In-Contextual Gender Bias Suppression for Large Language Models (2024.findings-eacl)
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| Challenge: | Prior work has proposed debiasing methods that require human labelled examples, data augmentation and fine-tuning of LLMs, which are computationally expensive. |
| Approach: | They propose to suppress gender biases by providing textual preambles from manually designed templates and real-world statistics without accessing model parameters. |
| Outcome: | The proposed methods suppress gender biases in English LLMs using a CrowsPairs dataset without accessing model parameters. |
Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference (2022.coling-1)
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| Challenge: | Recent studies show that NLP models trained on standard English produce biased outcomes against underrepresented English varieties. |
| Approach: | They propose a morphosyntactically-informed rule-based translation method that uses a greedy algorithm to debiase NLP models. |
| Outcome: | The proposed framework outperforms large language models while maintaining or improving the prediction performance. |
REARANK: Reasoning Re-ranking Agent via Reinforcement Learning (2025.emnlp-main)
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| Challenge: | REARANK is a large language model-based listwise reasoning reranking agent . it explicitly reasons be- fore reranked results, significantly improving performance and interpretability. |
| Approach: | They propose a large language model-based listwise reasoning reranking agent that explicitly reasons be- fore reranked lists. |
| Outcome: | The proposed agent outperforms GPT-4 on reasoning-intensive benchmarks and surpasses GPL-4 on BRIGHT benchmarks. |
OpenGraph: Towards Open Graph Foundation Models (2024.findings-emnlp)
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| Challenge: | Graph Neural Networks (GNNs) have emerged as promising techniques for encoding structural information and improving performance in tasks like link prediction and node classification. |
| Approach: | They propose a graph foundation model that generalizes to unseen graph data with different properties. |
| Outcome: | The proposed model achieves remarkable zero-shot graph learning performance across various settings. |
Improving Contrastive Learning in Emotion Recognition in Conversation via Data Augmentation and Decoupled Neutral Emotion (2024.eacl-long)
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| Challenge: | Existing methods to model context of utterances and speaker are inadequate . despite the improvements, there are still intrinsic challenges in the ERC dataset . |
| Approach: | They propose a supervised contrastive learning method specifically oriented for ERC task . they employ a data augmentation method emulating the emotion dynamics in a conversation and a method addressing the predominance and ambiguity of neutral emotion. |
| Outcome: | The proposed method emulates the emotion dynamics in a conversation and addresses the predominance and the ambiguity of neutral emotion. |
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning (2021.emnlp-main)
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| Challenge: | Recent studies describe how to apply contrastive learning to the language domain but it is difficult to apply data augmentation methods directly to language modeling. |
| Approach: | They propose a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning. |
| Outcome: | The proposed method outperforms baseline models on sentence-level tasks with only 70% of memory compared to the baseline model. |
Asking and Answering Questions to Extract Event-Argument Structures (2024.lrec-main)
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| Challenge: | Traditionally, corpora are limited to arguments within the same sentence, and inter-sentential arguments are more challenging and have received less attention. |
| Approach: | They propose a question-answering approach to extract document-level event-argument structures by automating questions for each argument type an event may have. |
| Outcome: | The proposed model outperforms previous models and is especially beneficial to extract arguments that appear in different sentences than the event trigger. |
Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality (2022.emnlp-main)
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| Challenge: | Recent visuolinguistic pre-trained models fail miserably on the Winoground dataset, which challenges models to match paired images and English captions. |
| Approach: | They propose to annotate a Winoground dataset that challenges visuolinguistic models to match paired images and English captions with items constructed to overlap lexically but differ in meaning. |
| Outcome: | The proposed dataset challenges models to match paired images and English captions with items constructed to overlap lexically but differ in meaning. |
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene (2021.findings-acl)
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| Challenge: | Existing approaches to fine-tune pre-trained models to downstream tasks are limited by labeled examples. |
| Approach: | They propose to apply post-training on unlabeled task data before fine-tuning by contrastive learning that considers either token-level or sequence-level similarity. |
| Outcome: | Empirical results show that contrastive masked language modeling surpasses other methods in few-shot settings without the need for data augmentation. |
UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP (2021.acl-long)
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| Challenge: | Transfer learning has yielded state-of-the-art (SoTA) results, but annotated data for every target task in every target language is rare, especially for low-resource languages. |
| Approach: | They propose a framework for unsupervised data augmentation for zero-resource transfer learning scenarios that performs simultaneous self-training with data hausse and unsupervised sample selection. |
| Outcome: | The proposed framework outperforms baselines on three zero-resource transfer tasks and achieves SoTA results in all the tasks. |
Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
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| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
| Approach: | They propose a set of sentence-level resampling methods to reduce data imbalance . they use a training sentence to compute the importance of each training sentence based on its tokens and entities . |
| Outcome: | The proposed methods outperform sub-sentence-level resampling, data augmentation, and loss functions on multiple corpora. |
Decoding Part-of-Speech from Human EEG Signals (2022.acl-long)
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| Challenge: | Recent studies have shown that EEG signal magnitude and topography depend on word length, frequency and open vs. closed class. |
| Approach: | They propose to use EEG to predict Part-of-Speech (PoS) tags from neural signals measured at millisecond resolution during text reading. |
| Outcome: | The proposed techniques outperform linear-SVMs on PoS tagging of unigram and bigram data. |
CoCoa: An Encoder-Decoder Model for Controllable Code-switched Generation (2022.emnlp-main)
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| Challenge: | Generating code-switched text with fine-grained control on the degree of code-witching and the lexical choices used to convey formality has been well-explored. |
| Approach: | They propose to generate code-switched text with fine-grained control on the degree of code-changing and lexical choices used to convey formality. |
| Outcome: | The proposed model can be invoked at test-time to synthesize code-switched text faithful to syntactic and lexical attributes relevant to code-witching. |
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)
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| Challenge: | Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance. |
| Approach: | They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities. |
| Outcome: | The proposed framework outperforms baseline methods on low-resource tasks. |
Medical Knowledge-enriched Textual Entailment Framework (2020.coling-main)
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| Challenge: | Existing approaches to achieving robust medical question answering systems lack a textual entailment framework that can capture the con-text beyond the sentence. |
| Approach: | They propose a medical knowledge-enriched textual entailment framework that can acquire a semantic and global representation of the input medical text with the help of a relevant domain-specific knowledge graph. |
| Outcome: | The proposed framework achieves 8.27% improvement over existing language models on MEDIQA-RQE dataset. |
HARALD: Augmenting Hate Speech Data Sets with Real Data (2022.findings-emnlp)
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| Challenge: | Hate speech detection depends on the availability of variable labeled data. |
| Approach: | They propose a method that uses real unlabelled data from online platforms to augment existing models by harvesting and processing it. |
| Outcome: | The proposed approach improves the classification performance of hate speech classification models. |
An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters (2023.eacl-main)
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| Challenge: | Medical doctors spend 52 to 102 minutes per day writing clinical notes from patient encounters. |
| Approach: | They propose to use a new dataset to generate automated and manual clinical notes from doctor-patient conversations in a clinical setting. |
| Outcome: | The proposed model could reduce the time spent writing clinical notes from doctor-patient conversations in a clinical setting. |
Data Augmentation for Text Generation Without Any Augmented Data (2021.acl-long)
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| Challenge: | Existing methods for data augmentation need to define or choose proper data mapping functions to create augmented samples. |
| Approach: | They propose to use data mapping functions to augment text samples without using specific mapping functions. |
| Outcome: | The proposed approach can approximate or even surpass popular data augmentation methods on two text generation tasks with a convergence rate guarantee. |
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 Practical Method for Generating String Counterfactuals (2025.findings-naacl)
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| Challenge: | Interventions targeting the representation space of language models (LMs) have emerged as an effective means to influence model behavior. |
| Approach: | They propose a method to convert representation counterfactuals into string counterf actuals and analyze the linguistic alterations corresponding to the intervention. |
| Outcome: | The proposed method analyzes linguistic alterations corresponding to a given representation space intervention and interprets features utilized to encode a specific concept. |
Data Expansion Using WordNet-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection (2022.lrec-1)
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| Challenge: | Existing methods to identify cyberbullying from text are limited due to the complexity of the content and the lack of labeled large-scale corpus. |
| Approach: | They propose a data augmentation-based approach that could enhance the automatic detection of cyberbullying in social media texts. |
| Outcome: | The proposed approach overcomes limitations of social media posts with word sense disambiguation and synonymy relation . results show that the proposed approach improves on the existing classifiers with and without data augmentation. |
RCL: Relation Contrastive Learning for Zero-Shot Relation Extraction (2022.findings-naacl)
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| Challenge: | Existing approaches to extract relations require large-scale labeled data. |
| Approach: | They propose a Relation Contrastive Learning framework to mitigate similar relations and similar entities problems by optimizing a contrastive instance loss with a relation classification loss on seen relations. |
| Outcome: | The proposed framework can learn subtle difference between instances and achieve better separation between different relation categories in the representation space simultaneously. |
Enhancing Hate Speech Classifiers through a Gradient-assisted Counterfactual Text Generation Strategy (2025.findings-emnlp)
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| Challenge: | Strong attribute control can distort meaning, while prioritizing semantic preservation may weaken attribute alignment. |
| Approach: | They propose a method that restricts accepted samples to text meeting a minimum BERTScore threshold and applies gradient-assisted proposal generation to improve attribute alignment. |
| Outcome: | a new method for counterfactual text generation improves attribute alignment and semantic preservation . the proposed method achieved the best macro F1-score in two of three test sets . |
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021.findings-emnlp)
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| Challenge: | Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. |
| Approach: | They propose a data augmentation technique that leverages large-scale language models to generate real text samples from a mixture of real samples. |
| Outcome: | The proposed method outperforms existing methods on diverse classification tasks. |
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification (2020.acl-main)
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| Challenge: | Xie et al., 2016) demonstrate that semi-supervised learning models suffer from over-fitting when there is only limited labeled data. |
| Approach: | They propose a semi-supervised learning method for text classification using a data augmentation method called TMix. |
| Outcome: | The proposed method outperforms pre-trained and fine-tuned models on several text classification benchmarks. |
Self-Guided Contrastive Learning for BERT Sentence Representations (2021.acl-long)
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| Challenge: | Existing methods to derive sentence embeddings from pre-trained Transformers are unclear . a self-guided training method is used to fine-tune BERT in a supervised fashion . |
| Approach: | They propose a contrastive learning method that utilizes self-guidance to improve BERT sentence representations. |
| Outcome: | The proposed method is more effective than baselines on diverse sentence-related tasks and robust to domain shifts. |
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation (2022.acl-long)
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| Challenge: | Existing continual relation learning methods rely on labeled training data for learning new tasks, which can be expensive and time-consuming. |
| Approach: | They propose a method that embeds space regularization and data augmentation to learn relational patterns with very few labeled data while avoiding catastrophic forgetting of previous task knowledge. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods in CFRL task 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. |
Syntactic Data Augmentation Increases Robustness to Inference Heuristics (2020.acl-main)
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| Challenge: | Pretrained neural models lack sensitivity to word order on controlled challenge sets . augmentation methods that improve accuracy on standard training sets may be a problem . |
| Approach: | They propose to augment standard training sets with syntactically informative examples by applying syntastic transformations to sentences from the MNLI corpus. |
| Outcome: | The proposed method improved BERT’s accuracy on controlled examples that diagnose sensitivity to word order from 0.28 to 0.73 without affecting performance on the MNLI test set. |
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness (2022.findings-acl)
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| Challenge: | Data modification has been proposed as an effective solution for generalizing to out-of-domain (OOD) inputs. |
| Approach: | They propose to use data modification to generalize to out-of-domain inputs . they also analyze their adversarial robustness using a synthetic dataset . |
| Outcome: | The proposed data modification strategies improve OOD accuracy and AR, but data filtering hurts OOD on other tasks. |
Machine Reading Comprehension as Data Augmentation: A Case Study on Implicit Event Argument Extraction (2021.emnlp-main)
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| Challenge: | Existing datasets are too small to train a model for capturing regularities underlying how event arguments are extracted. |
| Approach: | They propose to bridge implicit EAE with machine reading comprehension (MRC) by building a unified training framework and explicit data augmentation regimes via MRC. |
| Outcome: | The proposed method obtains state-of-the-art performance on two benchmarks and demonstrates superior results in a data-low scenario. |
Learning to Ignore Adversarial Attacks (2023.eacl-main)
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| Challenge: | Despite the strong performance of current NLP models, they can be brittle against adversarial inputs. |
| Approach: | They propose a rationale model that explicitly learns to ignore adversarial tokens . their approach leads to sizable improvements in robustness over baseline models . |
| Outcome: | The proposed model outperforms data augmentation with adversarial examples and closes the gap between model performance and an attacked test set. |
COF: Adaptive Chain of Feedback for Comparative Opinion Quintuple Extraction (2025.coling-main)
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| Challenge: | Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text. |
| Approach: | They propose a model-unaware adaptive chain-of-feedback method to extract quintuples from product review text. |
| Outcome: | The proposed method improves performance on three benchmarks. |
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective (2022.coling-1)
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| Challenge: | Existing knowledge graph completion models require only a few associative triples to complete a relationship. |
| Approach: | They propose to perform data augmentation from two perspectives to solve the FKGC problem by inferring new triple facts from existing models. |
| Outcome: | The proposed framework can be applied to a number of existing models. |
TransGEC: Improving Grammatical Error Correction with Translationese (2023.findings-acl)
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| Challenge: | Experimental results show that data augmentation improves accuracy over strong baselines. |
| Approach: | They propose to use translationese as input for GEC data augmentation to overcome stylistic discrepancies . they propose to obtain human-translated texts with a more similar style to non-native texts . |
| Outcome: | The proposed method improves correction accuracy over strong baselines on four GEC benchmarks. |
Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck? (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit a puzzling disparity in their formal linguistic competence, even after training on trillions of tokens. |
| Approach: | They pre-train Large Language Models on 100M-token corpora and inject a minimal amount of synthetic data targeting specific linguistic phenomena into the model. |
| Outcome: | The proposed intervention significantly improves model performance in 8 out of the 9 worst-performing BLiMP paradigms. |
Data-Centric Explainable Debiasing for Improving Fairness in Pre-trained Language Models (2024.findings-acl)
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| Challenge: | Existing data-centric debiasing strategies mainly leverage explicit bias words for counterfactual data augmentation to balance the training data. |
| Approach: | They propose a method which uses an explainability method to search for implicit bias words to assist in debiasing PLMs. |
| Outcome: | Extensive results show that the proposed method achieves state-of-the-art debiasing performance and strong generalization while maintaining predictive abilities. |
Adaptive Feature Selection for End-to-End Speech Translation (2020.findings-emnlp)
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| Challenge: | End-to-end speech translation (E2E) models that directly maps audio to a foreign text are not efficient. |
| Approach: | They propose adaptive feature selection (AFS) for encoder-decoder based E2E ST. |
| Outcome: | The proposed model outperforms the existing model on LibriSpeech En-Fr and MuST-C with a BLEU score of 18.56. |
On Evaluation Protocols for Data Augmentation in a Limited Data Scenario (2025.coling-main)
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| Challenge: | Textual data augmentation (DA) is a prolific field of study where novel techniques to create artificial data are regularly proposed. |
| Approach: | They propose to use textual data augmentation (DA) to generate new sentences for text classification in a limited data setting. |
| Outcome: | The proposed methods perform better on small data settings and on large datasets, but they are not as effective on large data sets. |
Improving Chinese Grammatical Error Detection via Data augmentation by Conditional Error Generation (2022.findings-acl)
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| Challenge: | Chinese Grammatical Error Detection is a non-automatic method to detect grammatical errors in texts. |
| Approach: | They propose a Conditional Non-Autoregressive Error Generation model for Chinese grammatical errors that uses a masking and prediction method to generate a context-dependent error. |
| Outcome: | The proposed method achieves better performance than all compared data augmentation methods on the CGED-2018 and CGAD-2020 benchmarks. |
Synonym relations affect object detection learned on vision-language data (2024.findings-naacl)
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| Challenge: | a recent study shows that vision-language models that accept textual input are not robust to variations in how input is provided. |
| Approach: | They propose two approaches to improve vision-language object detectors' performance . they use back-translation and class embedding enrichment to improve their models . |
| Outcome: | The proposed approaches improve performance on synonyms from mAP@0.3=33.87% to 37.93%. |
Learning Invariant Representation Improves Robustness for MRC Models (2022.findings-emnlp)
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| Challenge: | Existing approaches to improve machine reading comprehension models are vulnerable and not robust to adversarial examples. |
| Approach: | They propose to construct positive example pairs which have same answer by augmentation and then introduce stability and contrastive loss to improve invariance of representation. |
| Outcome: | The proposed approach boosts the robustness of QA models across different tasks and attack sets significantly and consistently. |
Generation-Based Data Augmentation for Offensive Language Detection: Is It Worth It? (2023.eacl-main)
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| Challenge: | generative data augmentation has been shown to be effective in offensive language detection but the potential for bias injection has not been investigated. |
| Approach: | They propose to investigate the robustness of models trained on generated data in a variety of data augmentation setups and analyze models using the HateCheck suite. |
| Outcome: | The proposed model training setups on four English offensive language datasets are robust and robust, while the generative DA setups do not present bias injection issues. |
Enhancing Unsupervised Sentence Embeddings via Knowledge-Driven Data Augmentation and Gaussian-Decayed Contrastive Learning (2025.acl-long)
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| Challenge: | Existing methods for data augmentation neglect fine-grained knowledge, such as entities and quantities, leading to insufficient diversity and high data noise. |
| Approach: | They propose a pipeline-based data augmentation method via LLMs and introduce the Gaussian-decayed gradient-assisted Contrastive Sentence Embedding (GCSE) model to enhance unsupervised sentence embeddings. |
| Outcome: | The proposed method achieves state-of-the-art performance in semantic textual similarity tasks using fewer data samples and smaller LLMs. |
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models (2025.acl-long)
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Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Recent code large language models have demonstrated impressive performance on code-related tasks. |
| Approach: | They propose a paradigm that learns from expert battles to address these limitations . they create an arena where leading LLMs challenge each other with evaluations . |
| Outcome: | The proposed model improves on existing models by leveraging expert battles . it achieves state-of-the-art performance even without relying on proprietary models . |
Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection (2023.emnlp-main)
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| Challenge: | Existing methods for few-shot cross-lingual transfer learning are limited in target languages due to the scarcity of resources. |
| Approach: | They propose a method which interpolates pairs of instances based on the angle of their representations and propose augmentation methods to enhance few-shot cross-lingual abusive language detection. |
| Outcome: | The proposed method improves few-shot cross-lingual abusive language detection in seven languages typologically distinct from English and three different domains. |
Text Augmentation in a Multi-Task View (2021.eacl-main)
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| Challenge: | a multi-task view of data augmentation allows for a more robust performance than traditional augmentation. |
| Approach: | They propose a multi-task view of data augmentation where original and augmented samples are weighted substantively during training. |
| Outcome: | The proposed model improves on three benchmark text classification datasets. |
Towards Summarizing Healthcare Questions in Low-Resource Setting (2022.coling-1)
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| Challenge: | Existing methods to generate large-scale datasets are difficult in closed domains where human annotation requires domain expertise. |
| Approach: | They propose a method to generate diverse and semantic questions in a low-resource setting with the aim of summarizing healthcare questions. |
| Outcome: | The proposed method generates diverse, fluent, and informative summarized questions on healthcare question summarization datasets. |
Handling Rare Word Problem using Synthetic Training Data for Sinhala and Tamil Neural Machine Translation (L18-1)
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| Challenge: | Lack of parallel training data influences rare word problem in Neural Machine Translation systems, especially for underresourced languages. |
| Approach: | They propose to use Parts of Speech tagging and morphological analysis as syntactic features to prune generated synthetic sentence pairs that do not adhere to language syntax. |
| Outcome: | The proposed methods show that they can prune sentences that do not adhere to language syntax over Sinhala to Tamil and Tamil to Sinhalak translation systems. |
Contrastive Learning with Keyword-based Data Augmentation for Code Search and Code Question Answering (2023.eacl-main)
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| Challenge: | Recent work on code search proposes data augmentation of queries for contrastive learning. |
| Approach: | They propose to augment query-code pairs with key words to preserve key words . they use keyDAC to fine-tune various pre-trained language models . |
| Outcome: | The proposed approach outperforms the current state-of-the-art in code search and question answering tasks. |
Towards Understanding Gender Bias in Relation Extraction (2020.acl-main)
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Andrew Gaut, Tony Sun, Shirlyn Tang, Yuxin Huang, Jing Qian, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | Existing bias mitigation techniques have a negative effect on NRE, a study finds . |
| Approach: | They create a dataset to analyze gender bias in relation extraction systems . they find that existing bias mitigation techniques have a negative effect on NRE . |
| Outcome: | The proposed dataset analyzes gender bias in relation extraction systems using a 10% human annotated test set. |
A little goes a long way: Improving toxic language classification despite data scarcity (2020.findings-emnlp)
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| Challenge: | Existing methods for toxic language classification have not been thoroughly explored. |
| Approach: | They propose to use data augmentation to generate new synthetic data from labeled seed datasets to improve toxic language classification. |
| Outcome: | The proposed techniques perform well on very scarce toxic language datasets while performing worse on shallower models. |
MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning (2024.emnlp-main)
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| Challenge: | a method to combine the advantages of open-source and tool-free LLMs remains to be explored. |
| Approach: | They propose a method to integrate open-source LLMs with external Python interpreters and augment math reasoning data. |
| Outcome: | The proposed method improves on GSM8K and MATH with the use of external tools. |
MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning (2022.findings-acl)
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| Challenge: | Existing methods to infer logical relations with annotated training data suffer from over-fitting and poor generalization problems due to the dataset sparsity. |
| Approach: | They propose a MEta-path guided contrastive learning method for logical ReasonIng of text that performs self-supervised pre-training on abundant unlabeled text data. |
| Outcome: | The proposed method outperforms the baselines on two logical reasoning benchmarks with significant improvements. |
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning (2020.acl-main)
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| Challenge: | Existing active learning models for text spam detection tasks are based on pool-based active learning, but the annotating process is laborious and time consuming for humans. |
| Approach: | They propose a semi-supervised active learning model to address spam imbalances . they propose masked attention learning approach and character variation graph-enhanced augmentation procedure . |
| Outcome: | The proposed model can improve the performance of existing models for Chinese spam detection task. |
DSM: Question Generation over Knowledge Base via Modeling Diverse Subgraphs with Meta-learner (2022.emnlp-main)
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| Challenge: | Existing methods on knowledge base question generation learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraph. |
| Approach: | They propose a graph contrastive learning-based retriever to model diverse subgraphs with meta-learner to learn semantics-specific and semantics agnostic knowledge on and across these tasks. |
| Outcome: | The proposed approach reduces learning difficulty and improves performance on two widely-adopted benchmarks on KBQG. |
Generating Mental Health Transcripts with SAPE (Spanish Adaptive Prompt Engineering) (2024.naacl-long)
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| Challenge: | Large language models can generate synthetic data resembling real-world data, but their generative performance depends on the quality of the prompt used to instruct the model. |
| Approach: | They propose a Spanish Adaptive Prompt Engineering method that uses genetic algorithms to generate and select prompts that resemble real-world data. |
| Outcome: | The proposed method produces Spanish therapy transcripts that more closely resemble authentic therapy transcript compared to other prompt engineering techniques that are based on Reflexion and Chain-of-Thought. |
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification (2021.findings-acl)
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| Challenge: | Existing methods to reduce disparities in model outcomes have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training. |
| Approach: | They propose to use certified word substitution robustness methods to improve equality of odds and equality of opportunity on multiple text classification tasks. |
| Outcome: | The proposed methods improve equality of odds and equality of opportunity on multiple text classification tasks. |
ReLearn: Unlearning via Learning for Large Language Models (2025.acl-long)
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Haoming Xu, Ningyuan Zhao, Liming Yang, Sendong Zhao, Shumin Deng, Mengru Wang, Bryan Hooi, Nay Oo, Huajun Chen, Ningyu Zhang
| Challenge: | Existing methods for unlearning large language models often rely on reverse optimization to reduce target token probabilities. |
| Approach: | They propose a data augmentation and fine-tuning pipeline for effective unlearning . they propose augmentation, evaluation frameworks to measure contextual forgetting . |
| Outcome: | The proposed framework achieves targeted forgetting while preserving high-quality outputs. |
Cross-language Sentence Selection via Data Augmentation and Rationale Training (2021.acl-long)
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| Challenge: | a new approach to cross-language sentence selection is proposed for low-resource contexts . a cross-lingual embedding-based model is proposed that avoids translation entirely . |
| Approach: | They propose a cross-lingual embedding-based query relevance model that uses data augmentation and negative sampling techniques to directly learn a query-sentence pair. |
| Outcome: | The proposed approach performs better than state-of-the-art models on noisy parallel data . consistent improvements are seen across three language pairs over state- of-the art models . |
DISCO: Distilling Counterfactuals with Large Language Models (2023.acl-long)
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| Challenge: | high-quality counterfactual data is scarce for most tasks and not easily generated at scale. |
| Approach: | They propose a method for automatically generating high-quality counterfactual data at scale . they use a large general language model to generate phrasal perturbations and filter them . |
| Outcome: | The proposed method is task-agnostic and can be applied to the task of natural language inference. |
Effective Data Augmentation for Sentence Classification Using One VAE per Class (2022.coling-1)
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| Challenge: | Variational auto-encoders and its conditional variant the Conditional-VAE (CVAE) are often used to generate new textual data, but they require more complex manipulations to ensure that the generated examples are useful. |
| Approach: | They propose a simple way to use Variational Auto-Encoders (VAE) for data augmentation by training one VAE per class. |
| Outcome: | The proposed method outperforms generative models on binary classification tasks and several dataset sizes on four different tasks. |
Evaluation of Question Answer Generation for Portuguese: Insights and Datasets (2024.findings-emnlp)
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| Challenge: | Automatic question generation is an increasingly important task that can be applied in educational settings, data augmentation for question-answering (QA), and conversational systems. |
| Approach: | They adapt and apply QAG approaches to generate question-answer pairs given context and look into strategies for error filtering and their effects. |
| Outcome: | The proposed methods can generate question-answer pairs in Portuguese, a widely spoken language that is underrepresented in natural language processing research. |
How low is too low? A monolingual take on lemmatisation in Indian languages (2021.naacl-main)
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| Challenge: | Prior work on ML based lemmatization focused on high resource languages, where data sets (word forms) are readily available. |
| Approach: | They propose to use neural methods to relate inflected forms of words to their dictionary form to reduce the sparse data problem. |
| Outcome: | The proposed methods can give competitive accuracy even in low resource setting. |
Realistic Data Augmentation Framework for Enhancing Tabular Reasoning (2022.findings-emnlp)
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| Challenge: | Existing approaches to constructing training data for Natural Language Inference (NLI) tasks are expensive and time consuming. |
| Approach: | They propose a semi-automated framework for data augmentation for tabular inference . framework generates hypothesis templates transferable to similar tables . authors say framework could generate human-like tabular examples . |
| Outcome: | The proposed framework generates human-like tabular inference examples . it is based on human-written constraints and premise paraphrasing . |
Can Synthetic Translations Improve Bitext Quality? (2022.acl-long)
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| Challenge: | Synthetic translations have been used for a wide range of NLP tasks, but it remains unclear how they differ from naturally occurring data. |
| Approach: | They propose to use a semantic equivalence classifier to improve bitext quality without additional bilingual supervision to replace the originals. |
| Outcome: | The proposed samples improve bitext quality without additional bilingual supervision and are validated intrinsically and extrinsically through bilingual induction and MT tasks. |
ParaZh-22M: A Large-Scale Chinese Parabank via Machine Translation (2022.coling-1)
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| Challenge: | Paraphrasing is an important data augmentation approach for natural language processing (NLP). |
| Approach: | They propose to extract sentence-level paraphrases from multiple Chinese translations and construct a larger Chinese parabank with 22M sentence pairs. |
| Outcome: | The proposed parabank is the largest to date in Chinese, but limited by one-to-many translation data. |
An Analysis of Simple Data Augmentation for Named Entity Recognition (2020.coling-main)
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| Challenge: | Recent studies have focused on using data augmentation techniques on sentence-level and sentence-pair natural language processing tasks such as text classification. |
| Approach: | They propose to use data augmentation techniques for named entity recognition to increase model performance. |
| Outcome: | The proposed techniques boost performance for both recurrent and transformer-based models, especially for small training sets. |
Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text (P19-1)
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| Challenge: | Existing sentiment detection methods are trained on sentiment-labeled monolingual text. |
| Approach: | They propose a method for synthesizing labeled code-switched text from monolingual text. |
| Outcome: | The proposed method improves sentiment labeling accuracy for three languages. |
Controllable Paraphrase Generation for Semantic and Lexical Similarities (2024.lrec-main)
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| Challenge: | Lexically diverse paraphrases are crucial in data augmentation because they enhance the linguistic diversity of the corpus. |
| Approach: | They propose a controllable model for semantic and lexical similarities by attaching tags to the head of the input sentence. |
| Outcome: | The proposed model can paraphrase an input sentence according to the tags specified. |
EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification (2022.naacl-main)
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| Challenge: | Existing methods for data augmentation do not fully exploit the potential of DA in NLP. |
| Approach: | They propose an easy and plug-in framework for data augmentation to support effective text classification. |
| Outcome: | The proposed framework outperforms existing methods in most cases, but not using agent networks or pre-trained generation networks. |
Scaling up Discourse Quality Annotation for Political Science (2022.lrec-1)
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| Challenge: | Existing annotations on deliberative quality are time-consuming and suffer from class imbalance . ephd thesis: deliberation is not only the output of the decision making, but also the discussion that leads up to it. |
| Approach: | They propose to use data augmentation techniques to improve deliberative quality predictions in a standard dataset. |
| Outcome: | The proposed methods outperform classifiers based on linguistic features and argument quality annotations with or without data augmentation. |
Revisiting the Reliability of Language Models in Instruction-Following (2026.acl-long)
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| Challenge: | Several benchmarks have been proposed to measure instruction-following accuracy, but these scores do not translate to reliable services in real-world use. |
| Approach: | They propose a new metric reliable@k and develop an automated pipeline to generate cousin prompts. |
| Outcome: | The proposed model can be instantiated with cousin prompts and generates high-quality cousin prompt data. |
Continuous Language Generative Flow (2021.acl-long)
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| Challenge: | Recent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformers. |
| Approach: | They propose a flow-based language generation model that adapts flow-derived generative models to language generation via continuous input embeddings, adapted affine coupling structures, and a novel architecture for autoregressive text generation. |
| Outcome: | The proposed model improves on QG and NMT and improves performance over baselines on SQuAD and TVQA and NML16. |
Logical DA: Enhancing Data Augmentation for Logical Reasoning via a Multi-Agent System (2025.findings-acl)
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| Challenge: | Existing data augmentation paradigms isolate data synthesis from label validation, thereby reducing their utility for complex reasoning tasks. |
| Approach: | They propose a framework for enhancing reasoning-focused data augmentation in few-shot learning scenarios that integrates four agents through two synergistic phases: diverse data generation and label verification. |
| Outcome: | The proposed framework achieves the highest average improvement in task accuracy in both fine-tuning and in-context learning paradigms. |
Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis (2021.emnlp-main)
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| Challenge: | Existing methods for data augmentation address data deficiencies and semantic consistency, but they ignore the second issue. |
| Approach: | They propose a semantics-preserving data augmentation approach that preserves the semantics of a textual sequence. |
| Outcome: | The proposed method achieves better performance on publicly available datasets and stock price/risk movement prediction scenarios. |
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation (N19-1)
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| Challenge: | Previous work focused on generating semantically similar paraphrases without considering diversity. |
| Approach: | They propose a method to obtain highly diverse paraphrases without compromising on paraphrasing quality by using monotone submodular function maximization. |
| Outcome: | The proposed method is effective on multiple tasks such as intent classification and paraphrase recognition. |
Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluation (2022.findings-emnlp)
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| Challenge: | Existing approaches to generalize compositionally are inadequate, but there is no evidence for this. |
| Approach: | They propose a model-agnostic algorithm for subsampling instances with diverse structures from a labeled instance pool with structured outputs. |
| Outcome: | The proposed algorithm leads to comparable or better generalization than prior algorithms in 9 out of 10 dataset-split type pairs. |
CHEF in the Language Kitchen: A Generative Data Augmentation Leveraging Korean Morpheme Ingredients (2023.emnlp-main)
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| Challenge: | Korean morphological variations present unique opportunities and challenges in natural language processing (NLP), necessitating an advanced understanding of morpheme-based sentence construction. |
| Approach: | They propose a method to replicate morphological transformations inherent in Korean sentences based on lexical and functional morphemes through generative data augmentation. |
| Outcome: | The proposed method improves performance in Korean multiple classification datasets without incurring external data usage. |
MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusion (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown impressive progress in mathematical problem-solving . current approaches to enhance mathematical reasoning focus on instance-level modifications . |
| Approach: | They propose a framework that enhances mathematical reasoning through cross-problem instruction synthesis. |
| Outcome: | The proposed framework boosts mathematical reasoning by 18.0 points while maintaining high data efficiency. |
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation (2022.findings-emnlp)
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| Challenge: | Existing approaches to produce counterfactuals rely on small perturbations via minimal edits, resulting in simplistic changes. |
| Approach: | They propose a novel approach to produce counterfactuals that allow for larger edits and linguistic diversity while still bearing similarity to the original document. |
| Outcome: | The proposed approach outperforms existing methods for generalizing natural language models under select settings. |
Improving the Robustness of Summarization Systems with Dual Augmentation (2023.acl-long)
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| Challenge: | Experimental results show that state-of-the-art summarization models have a significant decrease in performance on adversarial and noisy test sets. |
| Approach: | They propose a SummAttacker approach to generate adversarial samples based on pre-trained language models that can generate word-level synonym substitution and noise. |
| Outcome: | The proposed model performs better on noisy, attacked, and clean datasets than baseline models and is more robust on noisy and attacked datasets. |
MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling (2024.findings-emnlp)
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| Challenge: | Recent MEE methods focus on weak alignment strategies and data augmentation with simple classification models. |
| Approach: | They propose a unified template filling model that connects textual and visual modalities via textual prompts. |
| Outcome: | The proposed model surpasses the current SOTA on textual EAE by +7% F1 and performs generally better than the second-best systems for multimedia EAE. |
Can We Improve Model Robustness through Secondary Attribute Counterfactuals? (2021.emnlp-main)
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| Challenge: | Recent research has explored how models rely on spurious correlations and how counterfactual data augmentation (CDA) can mitigate such issues. |
| Approach: | They propose a context-aware methodology which takes into account the impact of secondary attributes on the model’s predictions and increases sensitivity for secondary attributes over reweighted counterfactually augmented data. |
| Outcome: | The proposed approach improves sliced accuracy on the original dataset by 7% compared to existing methods and provides guidelines to extend this to other tasks. |
CoPrUS: Consistency Preserving Utterance Synthesis towards more realistic benchmark dialogues (2025.coling-main)
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| Challenge: | Large-scale Wizard-Of-Oz dialogue datasets lack certain types of utterances, which would make them more realistic. |
| Approach: | They propose to use a large language model to create and repair communication errors in an automatic pipeline. |
| Outcome: | The proposed method is based on linguistic theory and uses a state-of-the-art Large Language Model (LLM) to create the error and repair it. |
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers? (2020.findings-emnlp)
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| Challenge: | Task-agnostic data augmentations have proven widely effective in computer vision, even on pretrained models. |
| Approach: | They examine the effects of two types of task-agnostic data augmentation on pretrained transformers using 5 classification tasks and 6 datasets. |
| Outcome: | The proposed techniques improve performance on 5 classification tasks, 6 datasets, and 3 variants of modern pretrained transformers. |
Domain Transfer based Data Augmentation for Neural Query Translation (2020.coling-main)
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| Challenge: | Query translation (QT) is a critical factor in successful cross-lingual information retrieval (CLIR). |
| Approach: | They propose to extend query translation (QT) with a domain transfer procedure to revise synthetic candidates to search-aware examples. |
| Outcome: | The proposed method outperforms baselines and domain transfer methods on translation quality and retrieval accuracy. |
PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs (2024.findings-acl)
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| Challenge: | Existing large language models are pre-trained on unstructured data, which leads to poor performance when dealing with structured data. |
| Approach: | They propose a framework to train large language models to act as verifier modules and to apply iterative corrections offline. |
| Outcome: | The proposed framework improves graph-based generative capability of large language models by iterating corrective instructions on three graph-derived datasets. |
Improving Model Factuality with Fine-grained Critique-based Evaluator (2025.acl-long)
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Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin, Yuning Mao, Quintin Fettes, Arya Talebzadeh, Sinong Wang, Han Fang, Carolyn Rose, Daniel Fried, Hejia Zhang
| Challenge: | Factuality evaluation aims to detect factual errors produced by language models and guide the development of more factual models. |
| Approach: | They propose a framework that leverages FenCE to improve the factuality of LM generators by constructing training data. |
| Outcome: | The proposed framework improves the factuality of LM generators by enhancing their training data. |
Follow the Beaten Path: The Role of Route Patterns on Vision-Language Navigation Agents Generalization Abilities (2025.naacl-long)
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| Challenge: | Vision and language navigation (VLN) is a challenging task towards the creation of embodied agents. |
| Approach: | They propose a solution that combines visual and linguistic features to enable VLN . they propose augmentation of the training data to fill the gap in missing patterns . |
| Outcome: | The proposed solution fills the gap in missing patterns of training data. |
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data (2021.emnlp-main)
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| Challenge: | Unsupervised Data Augmentation (UDA) is a semisupervised learning method that penalizes differences between a model's predictions on unlabeled examples and corresponding 'noised' examples produced via data augmentation. |
| Approach: | They propose to use a consistency loss to penalize differences between models' predictions on unlabeled and unlabed examples to enforce consistency between models and their perturbed counterparts. |
| Outcome: | The proposed method is able to penalize differences between models' outputs on unlabeled and unlabed examples without complex data augmentation. |
Learning to Ask Unanswerable Questions for Machine Reading Comprehension (P19-1)
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| Challenge: | Existing models for extractive reading comprehension are not good at deciding whether no answer is presented in the context. |
| Approach: | They propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. |
| Outcome: | The proposed model performs better on the SQuAD 2.0 dataset than the baseline model and the BERT-large model. |
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation (D19-1)
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Po-Sen Huang, Robert Stanforth, Johannes Welbl, Chris Dyer, Dani Yogatama, Sven Gowal, Krishnamurthy Dvijotham, Pushmeet Kohli
| Challenge: | Recent work has exposed the vulnerabilities of neural NLP models, e.g. with small, semantically invariant input alterations. |
| Approach: | They propose to model text classification under synonym replacements or character flip perturbations and then use a formal model verification method to verify its robustness. |
| Outcome: | The proposed models show little difference in terms of nominal accuracy, but have much improved verified accuracy under perturbations and come with an efficiently computable formal guarantee on worst case adversaries. |
AESOP: Paraphrase Generation with Adaptive Syntactic Control (2021.emnlp-main)
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| Challenge: | Existing models for paraphrase generation use fixed syntactic structures for all input sentences. |
| Approach: | They propose to add syntactical control to a pretrained language model to generate fluent paraphrases using a retrieval-based selection module. |
| Outcome: | The proposed model achieves state-of-the-art on semantic preservation and syntactic conformation on two benchmark datasets with ground-truth syntaktic control from human-annotated exemplars. |
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. |
Reverse Thinking Makes LLMs Stronger Reasoners (2025.naacl-long)
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Justin Chen, Zifeng Wang, Hamid Palangi, Rujun Han, Sayna Ebrahimi, Long Le, Vincent Perot, Swaroop Mishra, Mohit Bansal, Chen-Yu Lee, Tomas Pfister
| Challenge: | Reverse-Enhanced Thinking (RevThink) is a framework for large language models to perform reverse thinking. |
| Approach: | They propose a framework for enhancing forward-backward reasoning by collecting data from a teacher model and employing three objectives to train a student model in a multi-task learning fashion. |
| Outcome: | The proposed framework outperforms a fine-tuning method trained on 10x more forward reasoning on 12 datasets covering commonsense, math, and logical reasoning. |
Towards Learning (Dis)-Similarity of Source Code from Program Contrasts (2022.acl-long)
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| Challenge: | Existing models that focus on identifying functional (dis)similarity of source code get confused when trying to identify functional (Dis)-similarities. |
| Approach: | They propose to pre-train a Transformer model with such automatically generated program contrasts to better identify similar code in the wild and differentiate vulnerable programs from benign ones. |
| Outcome: | The proposed model outperforms existing models in vulnerability and code clone detection tasks even with much less data. |
FiNE: Filtering and Improving Noisy Data Elaborately with Large Language Models (2025.naacl-long)
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| Challenge: | Currently, there are two mainstream methods for improving data integrity: data filtering and data augmentation. |
| Approach: | They propose a method to improve data integrity by combining data filtering and data augmentation with LLMs. |
| Outcome: | The proposed method surpasses the open-source chat version on HalluQA by 8.45 on the open source version. |
Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System (2022.lrec-1)
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| Challenge: | Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time. |
| Approach: | They propose to build a multimodal dialogue system for children learning basic math concepts using limited datasets. |
| Outcome: | The proposed system improves the Natural Language Understanding (NLU) module of a task-oriented SDS pipeline with limited dataset resources. |
Diffusion Based Counterfactual Augmentation for Dual Sentiment Classification (2024.lrec-main)
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| Challenge: | Existing methods to augment sentiment models have failed to mitigate spurious association problem inherent in the original data. |
| Approach: | They propose a framework for enhancing sentiment models using an antonymous paradigm and contrastive learning to generate high-quality samples. |
| Outcome: | The proposed framework achieves state-of-the-art performance on four benchmark datasets. |
ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation (2023.acl-long)
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| Challenge: | Paraphrase generation is a long-standing task in natural language processing (NLP). |
| Approach: | They propose to generate large-scale syntactically diverse paraphrase datasets by abstract meaning representation back-translation. |
| Outcome: | The proposed dataset is syntactically more diverse than existing datasets while maintaining good semantic similarity. |
Generating Artificial Texts as Substitution or Complement of Training Data (2022.lrec-1)
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| Challenge: | Existing approaches to generate text for supervised learning tasks use transformers to generate learning data. |
| Approach: | They propose to use transformers to generate supervised learning data for supervised machine learning tasks and propose to train a neural language model trained on the original training texts. |
| Outcome: | The proposed models can be used in a certain extend but require pre-processing to significantly improve performance. |
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language Model (2022.coling-1)
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| Challenge: | Neural network models have a large number of parameters to train, but data augmentation is relatively under-explored in natural language processing. |
| Approach: | They propose a bi-directional conditional Masked Language Model (CMLM) that can be conditional on both left and right contexts and the label. |
| Outcome: | The proposed method achieves the best performance on four translation datasets and yields up to 1.90 BLEU points over the baseline. |
READIN: A Chinese Multi-Task Benchmark with Realistic and Diverse Input Noises (2023.acl-long)
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| Challenge: | Existing benchmarks for Chinese inputs often lack a realistic representation of real-world noises. |
| Approach: | They construct a Chinese multi-task benchmark with REalistic and Diverse input noises . they use pinyin input and speech input to recruit speakers from diverse dialects based on their inputs - a feature that is important for Chinese NLP benchmarks if it is implemented in real-world applications. |
| Outcome: | The proposed benchmarks are based on four different tasks and are designed to maximize diversity. |
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection (2021.emnlp-main)
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| Challenge: | Existing approaches to solve domain shifts in NLP tasks require additional pre-training . current approaches focus on the downstream corpus when it is small, but are not effective . |
| Approach: | They propose a task-adapted pre-training framework that can be used when the downstream corpus is too small for additional pre-tuning. |
| Outcome: | The proposed framework outperforms baseline methods on biomedical, computer science, news, and movie reviews tasks. |
Text Augmentation Using Dataset Reconstruction for Low-Resource Classification (2023.findings-acl)
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| Challenge: | Existing methods for text classification use labeled data, but labeles are expensive and difficult to obtain. |
| Approach: | They propose a novel method of data augmentation using the text-generation capabilities of language models. |
| Outcome: | The proposed method improves the current state-of-the-art methods for data augmentation on multi-class datasets. |
Multiword Expression aware Neural Machine Translation (2020.lrec-1)
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| Challenge: | Multiword Expressions (MWEs) are a pervasive phenomenon in all natural languages and challenge NLP applications because of their unpredictable morpho-syntactic and lexico--semantic behaviour. |
| Approach: | They propose to use linguistic resources to improve MWE translation and MWE generation by up to 5.09 BLEU points on MWE test sets. |
| Outcome: | The proposed annotation and data augmentation improve translation quality and increase performance by up to 5.09 BLEU points on MWE test sets. |
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition (2022.coling-1)
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| Challenge: | Existing approaches for few-shot Named Entity Recognition (NER) are evaluated mainly under in-domain settings, but little is known about how these models perform in cross-domain NER using labeled in- domain examples. |
| Approach: | They propose to use a rationale-centric data augmentation method to improve model generalization ability by allowing model to learn from a few labeled examples in a new target domain. |
| Outcome: | The proposed method improves the performance of cross-domain NER tasks compared to the counterfactual data augmentation and prompt-tuning methods. |
ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)
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| Challenge: | Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair. |
| Approach: | They propose a novel binary paraphrase classification task that annotates the degree of paraphrase between sentences and a new annotation schema that labels the minimum spans of tokens in a sentence that don't have the corresponding paraphrases in the other sentence. |
| Outcome: | The proposed dataset can be used to train an automatic scorer for language generation evaluation. |
Good-Enough Example Extrapolation (2021.emnlp-main)
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| Challenge: | GE3 is a data augmentation protocol that can be used to increase text examples from one class onto another. |
| Approach: | They propose a data augmentation protocol that extrapolates the hidden space distribution of text examples from one class onto another to investigate whether this bias is valid for data augmented. |
| Outcome: | The proposed protocol improves on three text classification datasets for various data imbalance scenarios. |
Data Augmentation for Multiclass Utterance Classification – A Systematic Study (2020.coling-main)
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| Challenge: | a lack of sufficient training data for some categories can cause imbalanced data distributions . a weak classifier may miscategorize a request, resulting in customer dissatisfaction . |
| Approach: | They propose to use random resampling, word-level transformations and neural text generation to augment existing data to cope with imbalanced data. |
| Outcome: | The proposed methods improve utterance classification results by drawing on utterant variation. |
Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data (2023.findings-acl)
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| Challenge: | a recent study shows that self-training can improve upon fully supervised baselines in low-resource settings for several sequence-to-sequence tasks. |
| Approach: | They propose to use pseudo-labeling to label unsupervised data and add it to the training pool. |
| Outcome: | The proposed setup improves on the unsupervised data by using pseudo-labeling . the proposed setup provides 0.4% absolute WER and 2.1 BLEU points for En–De . |
SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples (2023.emnlp-main)
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| Challenge: | Manually collecting challenging negative examples can be costly and domain-specific. |
| Approach: | They propose a method that synthesizes negative examples from only positive ones using a mask infilling model. |
| Outcome: | The proposed method closes 69.6% of the performance gap on a dataset where half of the evaluation examples are unanswerable compared to a model trained on SQuAD 2.0. |
GDA: Grammar-based Data Augmentation for Text Classification using Slot Information (2023.findings-emnlp)
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| Challenge: | Recent studies suggest data augmentation approaches to resolve the low-resource problem in natural language processing tasks. |
| Approach: | They propose to use slot information to augment sentences using a set of injective relations between a sentence’s semantics and its syntactical structure to augment the dataset. |
| Outcome: | The proposed approach outperforms all other data augmentation methods by 19.38%. |
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation (2020.lrec-1)
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| Challenge: | Morphological inflection is the process that generates the word form given its lexeme and morphological properties. |
| Approach: | They propose to use language models and data augmentation to improve morphological inflection without annotating more data. |
| Outcome: | The proposed model improves by 1.5% with the langauge model and by 9% with the data augmentation. |
Consecutive Question Generation via Dynamic Multitask Learning (2022.findings-emnlp)
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| Challenge: | , . ; ) ()((); ()) .())((2): ""(). |
| Approach: | They propose a sequential sequential question-answer generation task with auxiliary tasks generating other elements to generate logically related question-anchor pairs to understand a passage. |
| Outcome: | The proposed framework improves question generation significantly and benefit multiple related tasks. |
Simple Entity-Centric Questions Challenge Dense Retrievers (2021.emnlp-main)
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| Challenge: | Open-domain question answering has exploded in popularity due to the success of dense retrieval models. |
| Approach: | They construct a set of simple, entity-rich questions based on facts from Wikidata and test their models against supervised datasets. |
| Outcome: | The proposed model outperforms sparse retrieval methods on open-domain question answering datasets by a large margin. |
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering (2021.emnlp-main)
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| Challenge: | Existing methods address this issue by introducing an auxiliary task such as visual grounding, cycle consistency, or debiasing. |
| Approach: | They propose a data augmentation pipeline to turn “known” knowledge into training examples for VQA. |
| Outcome: | The proposed model can handle multi-modal information and is based on human-annotated examples. |
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. |
CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic Triples (2025.emnlp-main)
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| Challenge: | Spurious correlations are patterns that appear in datasets but do not represent genuine relationships. |
| Approach: | They propose a more general form of counterfactual data augmentation that tackles multiple biases . they propose 'CoBA' that decomposes text into subject-predicate-object triples and modifies them to disrupt spurious correlations. |
| Outcome: | The proposed framework reduces biases and strengthens out-of-distribution resilience. |
Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation (2022.coling-1)
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| Challenge: | Large-scale pretrained language models have led to significant improvements in Natural Language Processing, but they come at the cost of high computational and storage requirements. |
| Approach: | They propose to distill knowledge from larger models to smaller ones through pseudo-labels on task-specific datasets. |
| Outcome: | The proposed approach improves on the SST-2, MRPC, YELP-2, and TREC-6 datasets. |
Improving Language Models’ Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary (2023.emnlp-main)
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| Challenge: | Existing studies have exploited data augmentation or implemented specialised loss functions to alleviate the inconsistent behaviour issue, but they consume expensive training resources and can only handle a certain consistency type. |
| Approach: | They propose a method that allows PLMs to capture accurate meaning by learning precise interrelationships between concepts from word-definition pairs in a dictionary. |
| Outcome: | The proposed method can improve multiple types of consistency and integrate pre-trained knowledge with PLMs’ pre-training knowledge. |
QASem Parsing: Text-to-text Modeling of QA-based Semantics (2022.emnlp-main)
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| Challenge: | Existing work suggests the appeals of incorporating explicit semantic representations into NLP . semi-structured natural language structures provide an intermediate meaning-capturing representation . |
| Approach: | They propose a semi-structured natural-language representation of textual information . they examine input and output linearization strategies and multitask learning . |
| Outcome: | The proposed model is based on pre-trained sequence-to-sequence language models . it is easy to use and can be used for downstream tasks that benefit from it . |
MCLF: A Multi-grained Contrastive Learning Framework for ASR-robust Spoken Language Understanding (2023.findings-emnlp)
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| Challenge: | Trending ASR-robust SLU systems have seen impressive improvements through global contrastive learning, but they can easily lead to severe semantic changes. |
| Approach: | They propose a two-stage multi-grained contrastive learning framework to improve ASR robustness . they first adapt pre-trained language models to downstream SLU datasets and then fine-tune it on the corresponding dataset. |
| Outcome: | The proposed framework improves on four datasets and four BERT-like backbone models. |
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. |
Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems (2022.emnlp-main)
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| Challenge: | Numerical Question Answering is the task of answering questions that require numerical capabilities. |
| Approach: | They propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets. |
| Outcome: | The proposed approach relieves existing systems’ lack of robust numerical capabilities. |
Guiding Through Complexity: What Makes Good Supervision for Hard Reasoning Tasks? (2025.naacl-long)
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| Challenge: | Using weak teacher models to effectively supervise LLMs can improve performance on hard reasoning tasks. |
| Approach: | They propose two data-driven supervision strategies that offer supervision data at different quality levels upon tasks of varying complexity. |
| Outcome: | The proposed methods outperform "perfectly correct" supervision on harder subtasks even when the outcome error rate is high. |
MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning (2024.acl-long)
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Chengpeng Li, Zheng Yuan, Hongyi Yuan, Guanting Dong, Keming Lu, Jiancan Wu, Chuanqi Tan, Xiang Wang, Chang Zhou
| Challenge: | In math reasoning with large language models, fine-tuning data augmentation by query evolution and diverse reasoning paths is empirically verified effective. |
| Approach: | They propose to fine-tune data augmentation by query evolution and diverse reasoning paths. |
| Outcome: | The proposed model achieves new state-of-the-art on GSM8K and MATH. |
Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks (2023.emnlp-main)
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Haoqi Zheng, Qihuang Zhong, Liang Ding, Zhiliang Tian, Xin Niu, Changjian Wang, Dongsheng Li, Dacheng Tao
| Challenge: | Text classification tasks often encounter few-shot scenarios with limited labeled data, and addressing data scarcity is crucial. |
| Approach: | They propose a self-evolution learning (SE) based mixup approach for data augmentation in text classification which generates more adaptive and model-friendly pseudo samples for the model training. |
| Outcome: | The proposed approach can generate more adaptive and model-friendly pseudo samples for the model training. |
DisentQA: Disentangling Parametric and Contextual Knowledge with Counterfactual Question Answering (2023.acl-long)
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| Challenge: | Question answering models have access to two sources of knowledge during inference time: parametric knowledge and contextual knowledge. |
| Approach: | They propose a new paradigm in which QA models are trained to disentangle the two sources of knowledge. |
| Outcome: | The proposed model generates two answers for a given question based on parametric and contextual knowledge. |
A Comparison of Strategies for Source-Free Domain Adaptation (2022.acl-long)
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| Challenge: | Existing research on domain adaptation without access to training data is limited due to privacy concerns. |
| Approach: | They compare active learning, self-training, and data augmentation strategies for source-free domain adaptation with a shared task. |
| Outcome: | The proposed algorithms yield consistent gains across all SemEval 2021 Task 10 tasks and domains, but they are unreliable for source-free domain adaptation. |
Evaluating Topic Model on Asymmetric and Multi-Domain Financial Corpus (2024.lrec-main)
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| Challenge: | Recent research attempts to quantify the exposure of market assets to various risks from text and how assets react if the risk materializes itself. |
| Approach: | They propose two new metrics to evaluate the behavior of different types of topic models with respect to pitfalls previously mentioned about document risk distribution extraction. |
| Outcome: | The proposed models can be used to extract unbiased risk information from financial domain data and correct coherence imbalances. |
Generalized Data Augmentation for Low-Resource Translation (P19-1)
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| Challenge: | Low-resource language pairs with a lack of parallel data pose challenges for machine translation . data augmentation using monolingual data is an effective way to alleviate the problem . |
| Approach: | They propose a general framework for data augmentation for low-resource machine translation using monolingual data and a related high-resourced language. |
| Outcome: | The proposed method improves translation quality by 1.5 to 8 BLEU points under extreme low-resource settings compared to baselines. |
Towards Better Characterization of Paraphrases (2022.acl-long)
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| Challenge: | Existing models of natural language processing lack generalization and performance . existing models are often overreliant on learned spurious correlations resulting in poor generalization. |
| Approach: | They propose to use word position deviation and lexical deviation to characterize paraphrase pairs without expert human annotation. |
| Outcome: | The proposed metrics improve generalizability of models trained on the dataset and can be used to generate specific forms of paraphrases for data augmentation or robustness testing of NLP models. |
Multilingual and Cross-Lingual Intent Detection from Spoken Data (2021.emnlp-main)
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Daniela Gerz, Pei-Hao Su, Razvan Kusztos, Avishek Mondal, Michał Lis, Eshan Singhal, Nikola Mrkšić, Tsung-Hsien Wen, Ivan Vulić
| Challenge: | a systematic study on multilingual and cross-lingual intent detection from spoken data is presented . current work on intent detection is limited to English, and standard benchmarks exist only in English. |
| Approach: | They present a systematic study on multilingual and cross-lingual intent detection from spoken data. |
| Outcome: | The proposed resource is called MInDS-14, and it provides strong intent detection in most target languages. |
Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents (2026.acl-long)
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| Challenge: | Vision-and-Language Navigation (VLN) is a subfield of embodied AI that integrates natural language understanding, visual perception, and sequential decision-making to allow autonomous agents to navigate and interact within visual environments. |
| Approach: | They propose a modular framework that introduces structured, skill-based reasoning into Transformer-based VLN agents. |
| Outcome: | The proposed framework decomposes navigation into atomic skills handled by a specialized agent. |
A Quality-based Syntactic Template Retriever for Syntactically-Controlled Paraphrase Generation (2023.emnlp-main)
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| Challenge: | Existing syntactically-controlled paraphrase generation models perform well with human-annotated or well-chosen syntaktic templates. |
| Approach: | They propose a quality-based Syntactic Template Retriever to retrieve templates based on the quality of the to-be-generated paraphrases. |
| Outcome: | The proposed algorithm can generate high-quality paraphrases without sacrificing quality. |
LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit exceptional translation capabilities in high-resource language tasks, yet their effectiveness in low-resourced languages is suboptimal. |
| Approach: | They conduct extensive multilingual continual pre-training on the LLaMA series models and develop LLiMAX for translation support across more than 100 languages. |
| Outcome: | The proposed model achieves higher translation performance than existing open-source models and performs on-par with specialized translation model on the Flores-101 benchmark. |
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation (2020.acl-main)
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| Challenge: | Aspect term extraction is a task to extract aspect terms from review texts as opinion targets for sentiment analysis. |
| Approach: | They propose a conditional generation task for augmentation of aspect term extraction . they use a sequence-to-sequence method that generates a new sentence . results confirm that their method alleviates the data scarcity problem significantly . |
| Outcome: | The proposed method reduces the data scarcity problem significantly and boosts current models. |
Together We Can: Multilingual Automatic Post-Editing for Low-Resource Languages (2024.findings-emnlp)
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| Challenge: | Existing studies on multilingual automatic post-editing systems for low-resource Indo-Aryan languages have focused on different models for different language pairs. |
| Approach: | They propose to use a multilingual automatic post-editing system to improve machine translations for low-resource Indo-Aryan languages. |
| Outcome: | The proposed model outperforms English-Hindi and English-Marathi models by 2.5 and 2.39 TER points. |
AS-ES Learning: Towards efficient CoT learning in small models (2024.findings-acl)
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| Challenge: | Existing methods to induce Chain-of-Thought (CoT) in LLMs are limited and do not consider the importance of efficiently utilizing existing CoT data. |
| Approach: | They propose a new training paradigm which exploits the inherent information in CoT for iterative generation. |
| Outcome: | The proposed training paradigm surpasses direct seq2seq training on CoT-extensive tasks without data augmentation or altering the model itself. |
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy Interpolation (2024.findings-emnlp)
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| Challenge: | Despite the success of fine-tuning, it still displays model performance instability, especially with limited data. |
| Approach: | They propose a new mitigation strategy that leverages the strengths of ensembling, noise regularisation and model interpolation while retaining computational efficiency. |
| Outcome: | The proposed mitigation strategy outperforms the best performing mitigation strategy (Ensemble) while using only a fraction of its cost. |
Improving Grammatical Error Correction via Contextual Data Augmentation (2024.findings-acl)
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| Challenge: | Increasing use of synthetic data due to inconsistent error distribution and noisy labels is limiting the use of these data. |
| Approach: | They propose a method for augmentation of synthetic data with a more consistent error distribution. |
| Outcome: | The proposed method outperforms strong baselines and achieves state-of-the-art with only a few synthetic data. |
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation (2020.emnlp-main)
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| Challenge: | Social biases present in data are often directly reflected in the predictions of models trained on that data. |
| Approach: | They analyze gender bias in dialogue data and propose techniques to mitigate it . they use counterfactual data augmentation, targeted data collection, and bias controlled training . |
| Outcome: | The proposed techniques mitigate gender bias by balancing genderedness of generated dialogue utterances. |
MAST: A Multi-View Alignment Strategy for Optimal Transport-Based Contrastive Clustering of Short Text (2026.findings-acl)
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| Challenge: | Short text clustering has gained significant prominence due to its ubiquity in real-world applications. |
| Approach: | They propose a multi-view alignment strategy with transport-based clustering that integrates structural views to capture multi-granularity semantic features. |
| Outcome: | Experiments show that MAST outperforms state-of-the-art methods on benchmark datasets. |
An Efficient Retrieval-Based Method for Tabular Prediction with LLM (2025.coling-main)
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| Challenge: | Existing methods for tabular prediction rely on extensive pre-training or fine-tuning of LLMs . a retrieval-based approach eliminates the need for training any modules or performing data augmentation . |
| Approach: | They propose a retrieval-based approach that utilizes the powerful capabilities of large language models in representation, comprehension, and inference. |
| Outcome: | The proposed method exhibits strong predictive performance on tabular prediction task, affirming its practicality and effectiveness. |
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach (2021.emnlp-main)
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| Challenge: | Existing approaches to generating additional parallel sentences are aimed at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words. |
| Approach: | They propose to use data augmentation techniques to generate additional parallel sentences by reversing the order of the target sentence to produce unfluent target sentences. |
| Outcome: | The proposed approach improves on six low-resource translation tasks and the baseline and over DA methods. |
Good-Enough Compositional Data Augmentation (2020.acl-main)
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| Challenge: | a proposed data augmentation protocol provides a compositional inductive bias in conditional and unconditional sequence models. |
| Approach: | They propose a data augmentation protocol that provides a compositional inductive bias in conditional and unconditional sequence models by replacing discontinuous fragments with other fragments that appear in at least one similar environment. |
| Outcome: | The proposed protocol reduces error rate by 87% on diagnostic tasks and 16% on semantic parsing tasks. |
ControlMath: Controllable Data Generation Promotes Math Generalist Models (2024.emnlp-main)
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| Challenge: | Currently, mathematical reasoning is one of the most challenging areas for closed-source LLMs. |
| Approach: | They propose an iterative method involving an equation-generator module and two LLM-based agents that generate diverse equations and transform them into math word problems. |
| Outcome: | The proposed method enables the generation of diverse math problems, not limited to specific domains or distributions. |
MixEdit: Revisiting Data Augmentation and Beyond for Grammatical Error Correction (2023.findings-emnlp)
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| Challenge: | Various data augmentation strategies have been proposed to improve GEC models . high-quality parallel data for GEC is not as widely available . |
| Approach: | They propose a data augmentation approach that strategically augments real data by generating pseudo data. |
| Outcome: | The proposed approach significantly improves GEC models on English and Chinese datasets. |
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis (2023.acl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a task of analyzing people's sentiments at the aspect level. |
| Approach: | They propose a unified bidirectional generative framework to tackle cross-domain ABSA tasks . the framework trains a model in both text-to-label and label-totext directions . |
| Outcome: | The proposed framework trains a model in both label-to-label and label- to-text directions to learn domain-agnostic features. |
Improving Low-resource Question Answering by Augmenting Question Information (2023.findings-emnlp)
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Andong Chen, Yuan Sun, Xiaobing Zhao, Rosella Galindo Esparza, Kehai Chen, Yang Xiang, Tiejun Zhao, Min Zhang
| Challenge: | Low-resource questions pose a significant challenge within the field of Question-Answering (QA) tasks. |
| Approach: | They propose a method that leverages large models' internal knowledge to enhance the quality of augmented data by Prompt Answer, Question Generation, and Question Filter. |
| Outcome: | The proposed method outperforms existing augmentation strategies on high-resource QA tasks like SQUAD1.1 and TriviaQA. |
Neural Machine Translation Methods for Translating Text to Sign Language Glosses (2023.acl-long)
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| Challenge: | State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language glosses. |
| Approach: | They propose to use data augmentation, semi-supervised Neural Machine Translation, transfer learning and multilingual NMT to improve MT of spoken language to Sign Language glosses. |
| Outcome: | The proposed models outperform previous work on two German SL corpora and are confirmed by human evaluation. |
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation (2022.emnlp-main)
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| Challenge: | Existing models for task-specific natural language generation do not contain any labeled examples. |
| Approach: | They propose a variational autoencoder with disentanglement priors for task-specific natural language generation with none or a handful of task-related labeled examples. |
| Outcome: | The proposed model outperforms baseline models in terms of data augmentation and text style transfer in the few-shot setting. |
An Inflectional Database for Gitksan (2022.lrec-1)
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Bruce Oliver, Clarissa Forbes, Changbing Yang, Farhan Samir, Edith Coates, Garrett Nicolai, Miikka Silfverberg
| Challenge: | In this paper, we build a database of partial inflection tables for Gitksan, a low-resource Indigenous language of Canada. |
| Approach: | They use Gitksan data in interlinear glossed format to build a database of partial inflection tables and enrich it with neural transformer reinflection models. |
| Outcome: | The proposed model improves the performance of the experimental data hallucination and back-translation techniques. |
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (2021.emnlp-main)
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| Challenge: | Data augmentation aims to alleviate the overfitting issue in low-resource or class-imbalanced situations. |
| Approach: | They propose a framework called Text AutoAugment to enhance training samples . they use a Bayesian optimization algorithm to search for the best policy . |
| Outcome: | The proposed framework outperforms baseline methods on six benchmark datasets. |
Image and Text: Fighting the same Battle? Super Resolution Learning for Imbalanced Text Classification (2023.findings-emnlp)
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| Challenge: | Using high-resolution images to overcome the problem of low resolution has never been used in NLP. |
| Approach: | They propose a super-resolution learning method that uses high-res images to overcome the problem of low resolution images. |
| Outcome: | The proposed method is efficient when compared to state-of-the-art methods on several benchmarks datasets in two languages. |
HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid (2024.findings-emnlp)
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Hemank Lamba, Anton Abilov, Ke Zhang, Elizabeth Olson, Henry Dambanemuya, João Bárcia, David Batista, Christina Wille, Aoife Cahill, Joel Tetreault, Alejandro Jaimes
| Challenge: | Humanitarian organizations can analyze data to discover trends, gather aggregated insights, manage security risks, and inform advocacy and funding proposals. |
| Approach: | They present a dataset comprising news articles in three languages containing instances of different types of violent incidents categorized by the humanitarian sector they impact. |
| Outcome: | The proposed framework can be used to identify violent incidents and identify their impact on humanitarian operations. |
On Collaborating Small and Large Models For Few-shot Intent Detection (2025.findings-emnlp)
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| Challenge: | Existing methods for few-shot intent detection face high inference cost and label interference. |
| Approach: | They propose a framework that integrates a small prediction model with a large language model for FSID. |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
End-to-end Adversarial Sample Generation for Data Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods for generating adversarial samples have deceived many neural inference models, such as text classification and machine translation. |
| Approach: | They propose an adversarial sample generator that consists of a conditioned paraphrasing model and a condition generator and introduce a pretrained discriminator to help the adversarial sample generator adapt to the data characteristics. |
| Outcome: | The proposed approach improves the performance of the trained model on several tasks and is robust for various attacking techniques. |
Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP (2022.emnlp-main)
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| Challenge: | Textual adversarial samples are often misrepresented in research on security, evaluation, explainability, and data augmentation. |
| Approach: | They propose to use adversarial samples to evaluate their methods on security tasks to demonstrate the real-world concerns rather than developing impractical methods. |
| Outcome: | The proposed method has higher practical value than the current benchmark. |
Towards Robustifying NLI Models Against Lexical Dataset Biases (2020.acl-main)
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| Challenge: | Recent studies show that deep learning models exploit dataset biases without deep understanding of the language semantics. |
| Approach: | They propose two methods to debiase models against lexical dataset biases . they use contradiction-word bias and word-overlapping bias as examples . |
| Outcome: | The proposed method removes label bias at embedding level, while the other uses a bag-of-words sub-model to capture features likely to exploit the bias. |
Identifying Physical Object Use in Sentences (2022.emnlp-main)
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| Challenge: | Prior research has focused on learning the prototypical functions of physical objects . but many sentences refer to objects even when they are not used . |
| Approach: | They propose a task that determines whether a physical object mentioned in a sentence was used or likely will be used. |
| Outcome: | The proposed model exploits data augmentation methods and FrameNet to fine-tune a pre-trainedmodel. |
Paraphrasing as Zero-shot Translation with Feature-guided Diversity Enhancement (2026.acl-long)
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| Challenge: | Existing studies use parallel corpora for training, which results in less diverse paraphrases. |
| Approach: | They train a bidirectional multilingual neural machine translation model on a bilingual parallel corpus and use it as a paraphrasing model. |
| Outcome: | The proposed method generates paraphrases with higher semantic consistency, literal fluency and sentential diversity than existing parabanks and LLMs. |
To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering (2023.acl-long)
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| Challenge: | Recent advances in open-domain question answering have demonstrated impressive accuracy on general-purpose domains like Wikipedia. |
| Approach: | They propose a more realistic end-to-end domain shift evaluation setting covering five diverse domains to assess model adaption. |
| Outcome: | The proposed model improves by 24 points when adapted to unsupervised datasets. |
Do Slides Help? Multi-modal Context for Automatic Transcription of Conference Talks (2025.emnlp-main)
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| Challenge: | Current automatic speech recognition systems rely on only audio information, ignoring multi-modal context. |
| Approach: | They propose to integrate visual context into existing automatic speech recognition systems to integrate presentation slides with multi-modal information. |
| Outcome: | The proposed model reduces word error rate by approximately 34% across all words and 35% for domain-specific terms compared to baseline model. |
CARMA: Enhanced Compositionality in LLMs via Advanced Regularisation and Mutual Information Alignment (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) struggle with compositional generalisation, limiting their ability to systematically combine learned components to interpret novel inputs. |
| Approach: | They propose a method that enhances stability and robustness of compositional reasoning in large language models (LLMs) by regularising and preserving layer-wise stability constraints. |
| Outcome: | The proposed method reduces variability, stabilizes token representations, and improves compositional reasoning. |
EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification (2023.emnlp-main)
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| Challenge: | Existing methods to augment training data with counterfactuals fail to handle multi-hop fact verification due to their incapability to preserve complex logical relationships. |
| Approach: | They propose to augment training data with counterfactuals that alter causal features of the original data by preserving logical relationships. |
| Outcome: | The proposed method outperforms the baselines and can generate linguistically diverse counterfactuals without disrupting their logical relationships. |
CREST: A Joint Framework for Rationalization and Counterfactual Text Generation (2023.acl-long)
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| Challenge: | Existing methods for analyzing and training NLP models have not been integrated to combine their complementary advantages. |
| Approach: | They introduce a framework for selective rationalization and counterfactual text generation that leverages CREST to regularize selective rationales and a loss function that regularizes selective rationals. |
| Outcome: | The proposed framework generates valid counterfactuals that are more natural than those produced by previous methods and can be used for data augmentation at scale. |
An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Models (2023.acl-long)
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| Challenge: | Pre-trained language models inherit more human-like biases from the training corpora, causing computationally expensive problems. |
| Approach: | They propose parameter-efficient methods in combination with counterfactual data augmentation for bias mitigation. |
| Outcome: | The proposed methods are effective in mitigating gender bias, prompt tuning is more suitable for GPT-2 than BERT, and less effective when it comes to racial and religious bias. |
In the LLM era, Word Sense Induction remains unsolved (2025.findings-acl)
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| Challenge: | Existing word sense induction methods rely on predefined sense inventories of arbitrary granularity and require labor intensive sense-annotated data. |
| Approach: | They propose to evaluate an LLM-based word sense induction method for English . they use data augmentation sources, corpus and lexicon, and semi-supervised scenarios to augment the data. |
| Outcome: | The proposed method surpasses the previous SOTA system on a English set by 3.3%. |
LA-UCL: LLM-Augmented Unsupervised Contrastive Learning Framework for Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | Experimental results show that our model exceeds the baseline models due to the lack of cognitive ability. |
| Approach: | They propose a LLM-Augmented Unsupervised Contrastive Learning Framework which introduces a cognition-enabled Large Language Model (LLM) for efficient data augmentation and presents corresponding contrastive learning strategies. |
| Outcome: | The proposed model exceeds baseline models on six datasets. |
An Empirical Comparison of LM-based Question and Answer Generation Methods (2023.findings-acl)
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| Challenge: | Question and answer generation (QAG) is a task of generating question-answer pairs given a context. |
| Approach: | They propose to leverage sequence-to-sequence language model fine-tuning to generate question-answer pairs given a context. |
| Outcome: | The proposed model outperforms other more convoluted approaches in the end-to-end model and is computationally light at both training and inference times. |
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples (2024.findings-acl)
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| Challenge: | Using high-performing text and image generation models, we can improve compositional reasoning capabilities for large multimodal models. |
| Approach: | They propose a framework to improve visio-linguistic compositional reasoning capability for multimodal models by leveraging physical grounding and using image-based models for semantic counterfactual fine-tuning. |
| Outcome: | The proposed framework outperforms GPT-4V and DALLE-3 on a new dataset and checkpoints. |
Is Robustness Transferable across Languages in Multilingual Neural Machine Translation? (2023.findings-emnlp)
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| Challenge: | Existing studies have focused on bilingual machine translation with a single translation direction. |
| Approach: | They propose a robustness transfer analysis protocol to analyze the transferability of robustness across different languages in multilingual neural machine translation. |
| Outcome: | The proposed protocol shows that the robustness gained in one translation direction can transfer to other translation directions. |
Gloss2Text: Sign Language Gloss translation using LLMs and Semantically Aware Label Smoothing (2024.findings-emnlp)
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| Challenge: | Existing approaches to sign language translation use gloss annotations as an intermediary . a new approach to use large language models and word embeddings to improve Gloss2Text translation is needed. |
| Approach: | They propose to leverage large language models pre-trained on expansive and diverse corpora to improve Gloss2Text translation stage by using data augmentation and label-smoothing loss function. |
| Outcome: | The proposed approach surpasses state-of-the-art methods on the PHOENIX Weather 2014T dataset . it shows that gloss annotations can be used to guide the translation process . |
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning (2024.findings-acl)
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| Challenge: | Instruction tuning is critical to large language models but its success heavily relies on the training data quality. |
| Approach: | They propose a paradigm that synergizes a teacher LLM’s reflection and introspection with the data selection capability of the student LLM to automatically refine existing instruction-tuning data. |
| Outcome: | The proposed method achieves much stronger and top-tier 7B and 13B LLMs without collecting brand-new data. |
Mixture of Soft Prompts for Controllable Data Generation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) generate fluent text when the target output follows natural language patterns. |
| Approach: | They propose a method that uses large language models to generate fluent text from a limited ontology rather than direct prediction by using soft prompts. |
| Outcome: | The proposed method produces diverse and natural text while preserving label semantics. |
Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models (2024.emnlp-main)
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| Challenge: | Existing studies have revealed that Large Vision-Language Models suffer from hallucinations in practice, including object hallucines, spatial hallucinos, attribute hallucinications, etc. |
| Approach: | They propose to use CLIP model to mitigate object hallucinations by using a data augmentation method to create negative samples with a variety of hallucinian issues. |
| Outcome: | The proposed method mitigates object hallucinations and can be used as a visual encoder, effectively alleviating the object halluination issue in LVLMs. |
PK-ICR: Persona-Knowledge Interactive Multi-Context Retrieval for Grounded Dialogue (2023.emnlp-main)
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| Challenge: | Identifying relevant persona or knowledge for conversational systems is difficult, but recent work has shown that it is more realistic to optimize for concrete persona. |
| Approach: | They propose a persona-knowledge dual context retrieval method that utilizes all dialogue contexts simultaneously. |
| Outcome: | The proposed method performs zero-shot top-1 knowledge retrieval and precise persona scoring. |
Fairness in Automatic Speech Recognition Isn’t a One-Size-Fits-All (2025.findings-emnlp)
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| Challenge: | Pre-trained speech models like Whisper exhibit inconsistent group-level performance that varies across domains. |
| Approach: | They fine-tune a Whisper model on the Fair-Speech corpus using basic fine- tuning, demographic rebalancing, gender-swapped data augmentation and a novel contrastive learning objective. |
| Outcome: | The proposed method achieves stable, cross-domain fairness improvements without changes to the training data distribution and with minimal accuracy trade-offs. |
Is ChatGPT the ultimate Data Augmentation Algorithm? (2023.findings-emnlp)
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| Challenge: | Recent research has examined the use of ChatGPT for data augmentation, but only in limited contexts. |
| Approach: | They use ChatGPT to create new data with paraphrasing and zero-shot generation to compare it to seven other algorithms. |
| Outcome: | The proposed model performs exceptionally well on some simpler data, but it does not perform better than the other algorithms. |
Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation? (2024.emnlp-main)
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| Challenge: | Named Entity Recognition (NER) is a key task in NLP to find mentions of named entities and classify them into predefined categories. |
| Approach: | They investigated the impact of data augmentation on confidence calibration and uncertainty estimation in Named Entity Recognition (NER) tasks. |
| Outcome: | The data augmentation improves calibration and uncertainty in cross-genre and cross-lingual setting, especially in-domain setting. |
Diversity-oriented Data Augmentation with Large Language Models (2025.acl-long)
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| Challenge: | Existing data augmentation methods focus on increasing sample numbers while neglecting sample distribution diversity, which can lead to model overfitting. |
| Approach: | They propose a data augmentation framework that focuses on sample distribution diversity and trains a large language model as a diverse paraphraser. |
| Outcome: | The proposed framework achieves an average performance gain of 10.52% surpassing the runner-up baseline with more than three percentage points. |
Modal Dependency Parsing via Biaffine Attention with Self-Loop (2025.findings-acl)
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| Challenge: | A modal dependency structure is a web of connections between events and sources of information in a document that allows for tracing of who-said-what with what levels of certainty. |
| Approach: | They propose a modal dependency structure that integrates biaffine attention with a large language model to optimize against domain-specific challenges of modal dependence parsing. |
| Outcome: | The proposed approach outperforms the previous state-of-the-art on English and Chinese datasets by 2% and 4% respectively. |
BiasWipe: Mitigating Unintended Bias in Text Classifiers through Model Interpretability (2024.emnlp-main)
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| Challenge: | Existing methods to mitigate unintended bias in social media platforms are re-training and adding extra parameters to the model. |
| Approach: | They propose a technique to mitigate unintended bias in language models by pruning the neuron weights responsible for univ bias. |
| Outcome: | The proposed technique achieves fairness by pruning the neuron weights responsible for unintended bias without loss of original performance. |
KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling (2025.emnlp-main)
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| Challenge: | Existing approaches to multi-hop question answering focus on generating simple questions and neglecting the integration of essential knowledge, such as relevant sentences within documents. |
| Approach: | They propose a framework to expand the diversity of generated multi-hop questions by sampling varied knowledge compositions within a given context. |
| Outcome: | The proposed framework improves the overall accuracy of knowledge composition selection by 3.9% on hotpotQA and 2WikiMultihopQA datasets. |
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training (2025.findings-acl)
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Yihang Yao, Zhepeng Cen, Miao Li, William Han, Yuyou Zhang, Emerson Liu, Zuxin Liu, Chuang Gan, Ding Zhao
| Challenge: | Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. |
| Approach: | They propose a data-centric approach that enhances LLMs’ awareness of symmetry in query variations and propose syMmetry-ENhanceD (MEND) data augmentation. |
| Outcome: | Extensive experiments on logical and arithmetic reasoning tasks show that the proposed approach improves model robustness at the knowledge extraction stage through query augmentation. |
From Noise to Clarity: Filtering Real and LLM-Generated Samples for Enhanced Intent Detection (2025.findings-emnlp)
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| Challenge: | Using large language models for generating synthetic samples for data augmentation can cause problems with the generalization ability of classification models. |
| Approach: | They propose an interpretable Sample Filter by Topic Modeling framework that allows for filtering by topic and a 'sampler by topic' framework. |
| Outcome: | The proposed framework reduces the quantity of real and synthetic samples while improving the performance of the classification models. |
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. |
Saliency-Aware Interpolative Augmentation for Multimodal Financial Prediction (2024.lrec-main)
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Samyak Jain, Parth Chhabra, Atula Tejaswi Neerkaje, Puneet Mathur, Ramit Sawhney, Shivam Agarwal, Preslav Nakov, Sudheer Chava, Dinesh Manocha
| Challenge: | Recent advances in the Financial AI realm have expanded the scope of data and methods they use, such as textual and audio cues from financial earnings calls, but limitations exist. |
| Approach: | They propose a Saliency-guided Hierarchical Mixup augmentation technique for multimodal financial prediction tasks. |
| Outcome: | The proposed technique outperforms state-of-the-art methods by 3-7% on financial earnings and conference call datasets. |
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment (2024.lrec-main)
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| Challenge: | Large language models (LLMs) can reveal toxic or offensive content inadvertently or intentionally. |
| Approach: | They propose to control the diversity of both sides according to the number of samples for fine-tuning, which can directly reflect their impact. |
| Outcome: | The proposed approach improves the performance of large language models after fine-tuning. |
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning (2024.lrec-main)
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| Challenge: | Existing methods for data augmentation have not been well explored. |
| Approach: | They propose to use punctuation insertion, modal verbs, and double negation to produce diverse forms of sentences. |
| Outcome: | The proposed methods perform better on diverse datasets with semantic similarity and standard negation. |
Select High-quality Synthetic QA Pairs to Augment Training Data in MRC under the Reward Guidance of Generative Language Models (2024.lrec-main)
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| Challenge: | Existing approaches focus on downstream metrics to select QA pairs, which lack generalization across different datasets. |
| Approach: | They propose a general selection method that uses a large pre-trained language model as a reward model in a Reinforcement Learning framework for the training of the selection agent. |
| Outcome: | The proposed method improves performance on generative and extractive datasets. |
STAGE: Simple Text Data Augmentation by Graph Exploration (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) are widely used for various tasks, but fine-tuning them requires sufficient data. |
| Approach: | They propose a method for data augmentation that utilizes a word-relation graph to select optimal words for each modification. |
| Outcome: | The proposed method is highly effective across diverse datasets and different PLMs. |
Towards Equitable Natural Language Understanding Systems for Dialectal Cohorts: Debiasing Training Data (2024.lrec-main)
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| Challenge: | Prior research has shown that biases exist in these models against certain languages or dialects. |
| Approach: | They propose to use a dialect identification model to obtain targeted training data augmentation for under-represented dialects to debias NLU model for dialectal cohorts in NLU systems. |
| Outcome: | The proposed framework can provide insights on dialect disparity in real-world NLU systems and targeted data argumentation can help narrow the model’s performance gap between standard language speakers and dialect speakers. |
Towards Robust Evidence-Aware Fake News Detection via Improving Semantic Perception (2024.lrec-main)
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| Challenge: | Existing methods lack sufficient semantic perception and are easily blinded by textual expressions. |
| Approach: | They propose a model-agnostic training framework to improve the semantic perception of evidence-aware fake news detection by combining two kinds of data augmentations with synthetic data. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the extended test set while achieving competitive performance on the original one. |
Data-Efficient Hate Speech Detection via Cross-Lingual Nearest Neighbor Retrieval with Limited Labeled Data (2025.emnlp-main)
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| Challenge: | Existing methods for detecting hate speech data are expensive and time-consuming . labeled data is expensive and difficult to collect, especially for low-resource languages . |
| Approach: | They propose a method that leverages nearest-neighbor retrieval to augment minimal labeled data in target language. |
| Outcome: | The proposed method outperforms existing models on eight languages and is highly data-efficient. |
Mind the Pause: Disfluency-Aware Objective Tuning for Multilingual Speech Correction with LLMs (2026.acl-long)
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| Challenge: | Spontaneous speech is rarely fluent, and disfluencies can degrade readability and reliability . a sequence tagger first marks disfluent tokens, and these signals guide instruction fine-tuning . |
| Approach: | They propose a multilingual correction pipeline where a sequence tagger first marks disfluent tokens . they add a contrastive learning objective that penalizes the reproduction of disfluency tokens. |
| Outcome: | The proposed model improves readability and reliability of ASR transcripts in three languages . disfluencies can cause misinterpretations, incoherent responses, poor user experience . |