Papers with ensemble model
Synthetic Propaganda Embeddings To Train A Linear Projection (D19-50)
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| Challenge: | Using contextualized token embeddings, we can extract features of propaganda from contextualized embeddnings without fine-tuning the large parameters of the base model. |
| Approach: | They propose a method for detecting fine-grained categories of propaganda in text by generating synthetically generated embeddings from pre-trained language models. |
| Outcome: | The proposed method is used in the first shared task in fine-grained propaganda detection at NLP4IF as Team Stalin. |
Heterogeneous Graph Neural Networks to Predict What Happen Next (2020.coling-main)
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| Challenge: | Existing work on event representation cannot capture discontinuous event segments . Existing models cannot represent heterogeneous relations and discontinuous events . |
| Approach: | They propose a heterogeneous-event graph network to model missing events . they employ each unique word and individual event as nodes in the graph . |
| Outcome: | The proposed model outperforms baseline models on one-step and multi-step inference tasks. |
Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP Dataset for Early Detection of Alzheimer’s Disease (2020.aacl-main)
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| Challenge: | Existing studies on the detection of Alzheimer's disease focus on the diagnosis of dementia instead. |
| Approach: | They propose to use a dataset to develop NLP models for the detection of Mild Cognitive Impairment (MCI) MCI is a progressive neurodegenerative disorder associated with memory loss and declines in major brain functions including semantic and pragmatic levels of language processing. |
| Outcome: | The proposed model performs best on 74.1% of the 3 topics studied. |
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. |
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property Prediction (2022.findings-emnlp)
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| Challenge: | Neural language models encode rich knowledge about entities and their relationships but common properties of nouns are difficult to extract because they are rarely explicitly stated in texts. |
| Approach: | They propose to extract perceptual properties from images and use them in an ensemble model to complement the information extracted from language models. |
| Outcome: | The proposed model improves noun property prediction compared to powerful text-based language models. |
SchAman: Spell-Checking Resources and Benchmark for Endangered Languages from Amazonia (2022.aacl-short)
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Arturo Oncevay, Gerardo Cardoso, Carlo Alva, César Lara Ávila, Jovita Vásquez Balarezo, Saúl Escobar Rodríguez, Delio Siticonatzi Camaiteri, Esaú Zumaeta Rojas, Didier López Francis, Juan López Bautista, Nimia Acho Rios, Remigio Zapata Cesareo, Héctor Erasmo Gómez Montoya, Roberto Zariquiey
| Challenge: | Spell-checking as a generation task requires large amount of data, which is not feasible for endangered languages such as the languages spoken in Peru. |
| Approach: | They propose to use augmented misspelling data to train neural spell-checking models for four endangered languages of Peru: Shipibo-Koniba, Asháninka, Yánesha, yine . |
| Outcome: | The proposed model achieves better scores in most of the errors and languages in the four indigenous languages of Peru: Shipibo-Koniba, Asháninka, Yánesha, yine. |
LICHEE: Improving Language Model Pre-training with Multi-grained Tokenization (2021.findings-acl)
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| Challenge: | Pre-trained language models are trained based on single-grained tokenization, making it hard to learn the precise meaning of coarse-grain words and phrases. |
| Approach: | They propose a language model pretraining method that incorporates multi-grained information of input text into pre-trained language models. |
| Outcome: | The proposed method improves performance on CLUE and SuperGLUE in Chinese and English with little extra inference cost. |
AutoMeTS: The Autocomplete for Medical Text Simplification (2020.coling-main)
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| Challenge: | Semi-automated text simplification approaches can be used to simplify text faster and at a higher quality. |
| Approach: | They propose to use autocomplete to simplify medical texts using aligned English Wikipedia sentences and pretrained neural language models to analyze the additional context. |
| Outcome: | The proposed model outperforms the best individual model by 2.1% and achieves a word prediction accuracy of 64.52%. |
Translating a Math Word Problem to a Expression Tree (D18-1)
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| Challenge: | Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. |
| Approach: | They propose an equation normalization method to normalize duplicated equations and propose an ensemble model to combine their advantages. |
| Outcome: | The proposed model outperforms the previous state-of-the-art models on the math word problem solving. |
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference (N18-1)
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Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Fern, Oladimeji Farri
| Challenge: | Existing approaches to natural language inference rely on simple reading mechanisms for independent encoding of the premise and hypothesis. |
| Approach: | They propose a novel bidirectional dependent reading network to efficiently model the relationship between a premise and a hypothesis during encoding and inference. |
| Outcome: | The proposed model outperforms existing methods by a considerable margin on the Stanford Natural Language Inference (SNLI) dataset. |
Devil’s Advocate: Novel Boosting Ensemble Method from Psychological Findings for Text Classification (2021.findings-emnlp)
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| Challenge: | Existing ensemble methods that combine submodels to create a composite model can improve model performance by diminishing model bias and variance. |
| Approach: | They propose a method which uses a deliberately dissenting model to force other submodels within the ensemble to better collaborate. |
| Outcome: | The proposed method shows comparable or improved performance on 5 text classification tasks when compared to conventional methods. |
A Span Selection Model for Semantic Role Labeling (D18-1)
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| Challenge: | Existing models for semantic role labeling use BIO tags to predict argument spans . but performance of these approaches is weak . |
| Approach: | They propose a span-based model that takes into account all possible argument spans and scores them for each label. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2005 and 2012 datasets. |
Attention-Guided Answer Distillation for Machine Reading Comprehension (D18-1)
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| Challenge: | Existing approaches to reading comprehension systems are vulnerable to adversarial attacks. |
| Approach: | They propose to use knowledge distillation to transfer knowledge from an ensemble to a single model. |
| Outcome: | The proposed methods outperform the teacher on adversarial datasets and NarrativeQA benchmarks. |
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs (P19-1)
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| Challenge: | Existing models to tackle multi-hop reading comprehension (RC) are focusing on a single document or paragraph, but they lack the ability to do reasoning across multiple documents. |
| Approach: | They propose a heterogeneous document-entity graph with different types of nodes and edges to solve multi-hop RC problem. |
| Outcome: | The proposed model can do reasoning over the proposed graph with nodes representation initialized with co-attention and self-attention based context encoders. |
BERT-based Classical Arabic Poetry Authorship Attribution (2025.coling-main)
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| Challenge: | AA in Arabic poetry has been a significant issue since the 9th century due to the loss of pre-Islamic poetry and the misattribution of post-Islamical works to earlier poets. |
| Approach: | They propose a computational approach to authorship attribution in Arabic poetry using the entire Classical Arabic Poetry corpus for the first time. |
| Outcome: | The proposed model achieves F1 scores ranging from 0.97 to 1.0 and was applied to four pre-Islamic misattribution cases. |
CAMERO: Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing (2022.acl-long)
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| Challenge: | Existing work has resorted to sharing weights among models, but results are not affordable for real-world deployment. |
| Approach: | They propose a consistency-regularized ensemble learning approach based on perturbed models to retain ensemble benefits while maintaining a low memory cost. |
| Outcome: | The proposed approach outperforms the standard ensemble of 8 BERT-base models on the GLUE benchmark by 0.7 with a significantly smaller model size. |