Papers with deep learning model
Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networks (2021.naacl-main)
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| Challenge: | Existing work on information extraction (IE) has solved the four main tasks separately, thus failing to benefit from inter-dependencies between tasks. |
| Approach: | They propose a model to solve four IE tasks in a single model that captures inter-dependencies between tasks. |
| Outcome: | The proposed model achieves state-of-the-art performance on monolingual and multilingual learning settings with three different languages. |
CMTA: COVID-19 Misinformation Multilingual Analysis on Twitter (2021.acl-srw)
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| Challenge: | myths, sensationalism, rumours and misinformation, generated intentionally or unintentionally, spread rapidly through social networks during the COVID-19 pandemic . evaluation of tweets for recognizing misinformation can create beneficial understanding to review the top quality and also the readability of online information concerning the COV-19. |
| Approach: | They propose a multilingual COVID-19 related tweet analysis method that uses a deep learning model for multilingual tweet misinformation detection and classification. |
| Outcome: | The proposed method outperforms monolingual models in the misinformation detection task and shows that it can be used to improve the quality and readability of online information. |
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing (N18-1)
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| Challenge: | Currently, machine learning is limited in scalability and is limited to specific training data. |
| Approach: | They propose to enhance learning models with world knowledge in the form of Knowledge Graph fact triples for natural language processing tasks. |
| Outcome: | The proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. |
Torch-Struct: Deep Structured Prediction Library (2020.acl-demos)
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| Challenge: | Structured prediction is a key area of machine learning and is difficult to utilize in deep learning frameworks. |
| Approach: | They propose a library for structured prediction that integrates with vectorized, auto-differentiation based frameworks. |
| Outcome: | The library exploits auto-differentiation to produce readable, fast, and testable code. |
Label Correction Model for Aspect-based Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing models for aspect-based sentiment analysis ignore a phenomenon: aspect boundary label and sentiment label can correct each other. |
| Approach: | They propose a model that uses aspect boundary label and sentiment label to correct each other . they evaluate the model on three benchmark datasets and evaluate its performance . |
| Outcome: | The proposed model performs state-of-the-art on three benchmark datasets. |
Explanation-Based Human Debugging of NLP Models: A Survey (2021.tacl-1)
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| Challenge: | In this paper, we review literature that exploits explanations to enable humans to fix bugs in NLP models. |
| Approach: | They review literature that exploits explanations to enable humans to fix bugs in NLP models. |
| Outcome: | The proposed approach is described in detail in this paper and is based on three dimensions of the problem explanation-based human debugging (EBHD). |
Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation (2021.naacl-main)
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| Challenge: | Existing approaches to deep learning for NLP require large amounts of labeled data. |
| Approach: | They propose an approach that iteratively selects a small number of examples for expert annotation based on their estimated utility in training the model. |
| Outcome: | The proposed approach reduces the data requirements of state-of-the-art AL strategies by 3-25% on multiple NLP tasks while achieving the same performance with virtually no additional computation overhead. |
BERTHA: Video Captioning Evaluation Via Transfer-Learned Human Assessment (2022.lrec-1)
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| Challenge: | Existing metrics to evaluate video captioning systems are based on overlap between the caption and the reference sentence, but fail to include the context of the scene. |
| Approach: | They propose a method to evaluate video captioning systems using a deep learning model . the model is based on a language model that has been shown to work well in NLP tasks . |
| Outcome: | The proposed model outperforms the most commonly used metrics in video to text tasks. |
Cross-Domain Classification of Moral Values (2022.findings-naacl)
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| Challenge: | Existing methods to identify moral values in text can be challenging for transferring knowledge between domains. |
| Approach: | They compare a deep learning model with a domain-specific value classifier to find out whether it can transfer knowledge to new domains. |
| Outcome: | The proposed model can generalize and transfer knowledge to novel domains, but introduce catastrophic forgetting. |
Enhancing Air Quality Prediction with Social Media and Natural Language Processing (P19-1)
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| Challenge: | predicting air quality is a major concern for human health, but the changes of air quality conditions are still difficult to monitor. |
| Approach: | They propose to exploit social media and natural language processing techniques to enhance air quality prediction. |
| Outcome: | The proposed approach improves air quality prediction over baseline that does not use social media by 6.9% to 17.7% in macro-F1 scores. |
Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures (2021.naacl-main)
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| Challenge: | Existing models for document-level Event Causality Identification (ECI) are limited to intra-sentence contexts where event mention pairs are presented in the same sentences. |
| Approach: | They propose a deep learning model that accepts inter-sentence event mention pairs . they use interaction graphs to capture relevant connections between important objects . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |
What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation (2020.coling-main)
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| Challenge: | Acronyms are short forms of phrases that facilitate conveying lengthy sentences in documents. |
| Approach: | They propose to annotate a large dataset for scientific domain and a new deep learning model which expands an ambiguous acronym in a sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art models on the new dataset. |
Multi-task Learning to Enable Location Mention Identification in the Early Hours of a Crisis Event (2021.findings-emnlp)
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| Challenge: | Social media is a platform for people to share their concerns and report information as eyewitnesses of events. |
| Approach: | They propose a multi-task learning approach to leverage available annotated data for several related tasks from the crisis domain to improve performance on a main task with limited annotation. |
| Outcome: | The proposed approach improves performance on a task with limited annotated data. |
Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution (2021.acl-long)
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| Challenge: | Existing deep learning models for event coreference resolution are limited in that they cannot exploit important interactions between relevant objects for ECR. |
| Approach: | They propose a deep learning model that groups coreferent event mentions into the same clusters . they use document structures to capture relevant objects for ECR . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |
Quantifying Intimacy in Language (2020.emnlp-main)
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| Challenge: | Intimacy is a fundamental aspect of how we relate to others in social settings. |
| Approach: | They propose a computational framework for studying the intimacy in language with a dataset and a deep learning model for accurately predicting the intimacy level of questions. |
| Outcome: | The proposed framework enables the analysis of 80.5M questions across social media, books, and films to quantify the intimacy expressed in language and to predict the intimacy level. |
A Unified Neural Network Model for Readability Assessment with Feature Projection and Length-Balanced Loss (2022.emnlp-main)
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| Challenge: | Traditional readability assessment models employ hundreds of linguistic features, but it is less explored for readability assessments. |
| Approach: | They propose a BERT-based model with feature projection and length-balanced loss to determine the difficulty level of a given text. |
| Outcome: | The proposed model achieves significant improvements over baseline models on three English benchmark datasets and one Chinese dataset. |
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework (2020.coling-main)
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| Challenge: | 65% of job descriptions miss describing a significant number of relevant skills, a problem we address with a deep learning model based on a multi-label classification problem . |
| Approach: | They propose a deep learning model to learn the set of enumerated job skills associated with a job description. |
| Outcome: | The proposed model improves on an existing baseline solution by over 9% and 7% absolute improvements in terms of recall and normalized discounted cumulative gain. |
Sanskrit Sandhi Splitting using seq2(seq)2 (D18-1)
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| Challenge: | Existing methods for word splitting in Sanskrit have low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. |
| Approach: | They propose a deep learning architecture called Double Decoder RNN which predicts the location of the splits with 95% accuracy and 79.5% accuracy. |
| Outcome: | The proposed model outperforms the state-of-the-art in the problem of Chinese word segmentation with 79.5% accuracy and the existing model's generalization capability. |
Reconstructing NER Corpora: a Case Study on Bulgarian (2020.lrec-1)
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| Challenge: | Named Entity Recognition (NER) and Named Enel Linking (NEL) are two related tasks that are under-resourced for the Slavic languages. |
| Approach: | They propose to use deep learning methods to improve a Named Entity Recognition corpus and to predict and annotate new types in a test corpus. |
| Outcome: | The proposed model improves a type-based Named Entity Recognition (NER) training corpus and predicts and annotates new types in a test corpus. |
NwQM: A neural quality assessment framework for Wikipedia (2020.emnlp-main)
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| Challenge: | Wikipedia articles are classified into several quality classes, which indicate their reliability as encyclopedic content. |
| Approach: | They propose a deep learning model which accumulates signals from key information sources to obtain improved Wikipedia article representation. |
| Outcome: | The proposed model improves Wikipedia article representation by 8% over state-of-the-art approaches with detailed ablation studies. |
Unsupervised Non-transferable Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods to train a good deep learning model require labeled data for the target domain which can be difficult to obtain. |
| Approach: | They propose an unsupervised non-transferable learning method that does not require annotated target domain data and introduce a secret key component for recovering the model’s access to the target domain. |
| Outcome: | The proposed method reduces model generalization ability in specific target domains while still recovering access to the target domain. |
Revisit Few-shot Intent Classification with PLMs: Direct Fine-tuning vs. Continual Pre-training (2023.findings-acl)
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| Challenge: | Recent progress in intent detection relies on deep models and datasets with well-crafted annotations. |
| Approach: | They propose a continual pre-training approach to train deep learning models . they propose augmentation method and sequential self-distillation to boost performance . |
| Outcome: | The proposed method outperforms methods that employ continual pre-training on labeled datasets on few-shot intent detection tasks. |
Exploiting the Syntax-Model Consistency for Neural Relation Extraction (2020.acl-main)
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| Challenge: | Existing deep learning models for Relation Extraction (RE) have limited generalization beyond the syntactic structures of the input sentences. |
| Approach: | They propose a deep learning model that uses dependency trees to extract syntactic importance of words for Relation Extraction. |
| Outcome: | The proposed model outperforms existing models on three RE benchmark datasets. |
Unsupervised Binary Code Translation with Application to Code Clone Detection and Vulnerability Discovery (2023.findings-emnlp)
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| Challenge: | a recent study shows that binary code analysis is a key problem in software security research. |
| Approach: | They propose to apply Neural Machine Translation to binary code analysis . they translate a binary in a low-resource ISA and train a model on the high-resourced ISA . |
| Outcome: | The proposed model can be used to analyze binary code across ISAs using a high-resource ISA. |
Using Pre-Trained Language Models in an End-to-End Pipeline for Antithesis Detection (2024.lrec-main)
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| Challenge: | Rhetorical figures are a "departure from the normal usage" of language . features of metaphors, irony and sarcasm enhance performance of several NLP tasks. |
| Approach: | They propose a pipeline approach to detect rhetorical figures using large language models by splitting text into phrases and identifying parallel phrases with a syntactically parallel structure. |
| Outcome: | The proposed approach outperforms state-of-the-art methods by an F1 score of 65.11 %. |