Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations (N19-1)
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| Challenge: | Several methods have been proposed for automatic music style classification, but they are limited in two aspects. |
| Approach: | They propose a deep learning approach to automatically learn and exploit style correlations by reviewing music reviews on websites. |
| Outcome: | The proposed approach performs well in capturing style correlations. |
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SGM: Sequence Generation Model for Multi-label Classification (C18-1)
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| Challenge: | Existing methods ignore the correlations between labels and different parts of the text can contribute differently for predicting different labels. |
| Approach: | They propose to view the multi-label classification task as a sequence generation problem and apply a decoder-based sequence generation model to solve it. |
| Outcome: | The proposed methods outperform previous work by a substantial margin. |
A Large Multilingual and Multi-domain Dataset for Recommender Systems (L18-1)
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| Challenge: | Existing algorithms for recommending items are limited and focused on specific domains. |
| Approach: | They propose a multi-domain interests dataset to train and test Recommender Systems . the english dataset includes an average of 90 preferences per user on music, books, movies, celebrities, sport, politics . |
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Style is NOT a single variable: Case Studies for Cross-Stylistic Language Understanding (2021.acl-long)
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| Challenge: | a benchmark corpus of text in 15 different styles is used to study stylistic language . a similar benchmark is used for cross-style language understanding . |
| Approach: | They propose a benchmark corpus that combines existing datasets and collects a new one for cross-style language understanding. |
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Enhancing Extreme Multi-Label Text Classification: Addressing Challenges in Model, Data, and Evaluation (2023.emnlp-industry)
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Dan Li, Zi Long Zhu, Janneke van de Loo, Agnes Masip Gomez, Vikrant Yadav, Georgios Tsatsaronis, Zubair Afzal
| Challenge: | Existing approaches to extreme multi-label text classification face inherent challenges in terms of model, data, and evaluation. |
| Approach: | They propose a label ranking model as an alternative to the conventional SciBERT-based classification model and an active learning-based pipeline that addresses the data scarcity of new labels during the update of a classification system. |
| Outcome: | The proposed model enables efficient handling of large-scale labels and accommodates new labels. |
Evaluating Extreme Hierarchical Multi-label Classification (2022.acl-long)
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| Challenge: | Several natural language processing tasks are defined as a classification problem in its most complex form: Multi-label Hierarchical Extreme classification. |
| Approach: | They propose a classification metric inspired by the Information Contrast Model (ICM) they use a set of formal properties to analyze the evaluation metrics. |
| Outcome: | The proposed evaluation metrics are suitable for multi-label hierarchical extreme classification scenarios. |
Even the Simplest Baseline Needs Careful Re-investigation: A Case Study on XML-CNN (2022.naacl-main)
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| Challenge: | XML-CNN has been a popular research topic in NLP due to its superior performance . however, the increasing complexity brings difficulties to ensure the true architectural progress . |
| Approach: | They propose to re-examine an influential multi-label text classification method . they propose suitable baselines for multi-level text classification tasks . |
| Outcome: | The proposed method performs better than the original model, the authors show . they show that the re-implementation reveals contradictory results to the original work . |
MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation) (2022.findings-naacl)
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| Challenge: | Named Entity Recognition (NER) is a process of identifying named entities in unstructured texts and classifying them through specific semantic categories. |
| Approach: | They propose a method for automatically producing NER annotations and introduce a manually-annotated test set. |
| Outcome: | The proposed method covers 10 languages, 15 NER categories and 2 textual genres and a manually-annotated test set. |
Hierarchical Label Generation for Text Classification (2023.findings-eacl)
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| Challenge: | None Hierarchical text classification (HTC) aims to assign the most relevant labels with their structure for a given document. |
| Approach: | They propose a method that captures the label hierarchy for real-world classification applications by using a taxonomic hierarchy. |
| Outcome: | The proposed method can generate unseen labels in subword level. |
Multi-modal Multi-label Emotion Detection with Modality and Label Dependence (2020.emnlp-main)
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| Challenge: | Existing studies on multi-label emotion detection focus on one modality . current studies focus on label dependence, but there is no consensus on the model . |
| Approach: | They propose a multi-modal sequence-to-set approach to model label dependence and modality dependence in a multiple-modal scenario. |
| Outcome: | The proposed approach is able to model the label dependence and the modality dependence in a multi-modal scenario. |
Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation (D19-55)
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| Challenge: | elucidates the dangerous current state of style transfer auto-evaluation research. |
| Approach: | They propose ways to aggregate the three metrics into one evaluator. |
| Outcome: | The proposed method could be used to aggregate the three metrics into one evaluator. |