Papers with classification model
EmoNoBa: A Dataset for Analyzing Fine-Grained Emotions on Noisy Bangla Texts (2022.aacl-short)
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| Challenge: | EmoNoBa is a dataset for fine-grained emotion detection on Bangla text . it is based on 22698 comments from social media sites on 12 domains . |
| Approach: | They propose a manually annotated dataset of 22,698 Bangla comments from social media sites on 12 different domains to use for fine-grained emotion detection. |
| Outcome: | The proposed dataset of 22,698 public comments on 12 domains shows that hand-crafted features perform better than neural networks and pre-trained language models. |
SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content (2022.emnlp-industry)
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Apurva Gandhi, Ryan Serrao, Biyi Fang, Gilbert Antonius, Jenna Hong, Tra My Nguyen, Sheng Yi, Ehi Nosakhare, Irene Shaffer, Soundararajan Srinivasan
| Challenge: | SLATE is a sequence labeling approach for extracting tasks from free-form content . past approaches for task extraction from typed content focus on building separate sentence-level task classification models. |
| Approach: | They propose a sequence labeling approach for extracting tasks from free-form content . they use a single, low-latency sequence labelling approach to perform sentence segmentation and classification . |
| Outcome: | The proposed model outperforms a baseline model and achieves 84.4% task F1 score and 88.4% boundary similarity score. |
Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation (2021.acl-short)
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| Challenge: | Early fusion models with cross-attention have shown better-than-human performance on some question answer benchmarks, while it is a poor fit for retrieval since it prevents pre-computation of the answer representations. |
| Approach: | They propose a supervised data mining method to train an efficient late fusion retrieval model by using cross-attention models with cross-references. |
| Outcome: | The proposed model outperforms retrieval models trained with gold annotations on Precision at N (P@N) and Mean Reciprocal Rank (MRR). |
Incorporating Question Answering-Based Signals into Abstractive Summarization via Salient Span Selection (2023.eacl-main)
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| Challenge: | Existing methods for generating summarizations using QA-based supervision produce higher quality summaries than baseline methods. |
| Approach: | They propose a method for incorporating question-answering signals into a summarization model by automatically marking document NPs as salient based on whether they are answered in the gold summaries. |
| Outcome: | The proposed method generates higher-quality summaries than baseline methods on benchmark summarization datasets. |
BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)
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| Challenge: | a new study shows that fake news spreads faster than mainstream articles on the same topic . however, there is no dataset containing compelling fake and questionable news articles . |
| Approach: | They introduce manually verified corpus of compelling fake and questionable news articles on the USA politics . they plan to extend the corpus in the future and use it for automated fake news detection. |
| Outcome: | The proposed model is based on linguistic features and will be extended in the future . it will be used to improve the existing model and improve the tools in the field of fake news detection . |
Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification (2022.emnlp-industry)
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Jai Gupta, Yi Tay, Chaitanya Kamath, Vinh Tran, Donald Metzler, Shailesh Bavadekar, Mimi Sun, Evgeniy Gabrilovich
| Challenge: | a new method for classification of COVID-19 vaccination related search queries is proposed . the proposed method uses pretrained Transformers and dense features to generate search insights . |
| Approach: | They propose a machine learning model that detects COVID-19 vaccination related search queries . they use pretrained Transformers to consider dense features as memory tokens that the model can attend to . |
| Outcome: | The proposed model improves the Vaccine Search Insights task by +15% . the proposed model uses pretrained Transformers and traditional dense features . |
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification (2024.naacl-short)
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Michail Mitsios, Georgios Vamvoukakis, Georgia Maniati, Nikolaos Ellinas, Georgios Dimitriou, Konstantinos Markopoulos, Panos Kakoulidis, Alexandra Vioni, Myrsini Christidou, Junkwang Oh, Gunu Jho, Inchul Hwang, Georgios Vardaxoglou, Aimilios Chalamandaris, Pirros Tsiakoulis, Spyros Raptis
| Challenge: | Emotion detection in textual data is pivotal for developing empathetic human-computer interaction systems. |
| Approach: | They propose a method for categorizing emotions from textual data that acknowledges similarities and distinctions of various emotions. |
| Outcome: | The proposed method preserves high accuracy in emotion prediction and significantly reduces errors in misclassification cases. |
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. |
Memory-Based Invariance Learning for Out-of-Domain Text Classification (2023.emnlp-main)
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| Challenge: | Existing approaches to learning invariant representations rely on the assumption that training and test sets come from the same domain. |
| Approach: | They propose to extend a classification model trained on multiple source domains to an unseen target domain by using key-value memory. |
| Outcome: | The proposed method improves on sentiment analysis and natural language inference tasks. |
Classifying the Informative Behaviour of Emoji in Microblogs (L18-1)
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| Challenge: | Emoji are pictographs used in microblogs as emotion markers, but can also represent a wider range of concepts. |
| Approach: | They analyze a corpus of tweets pairs and classify emoji with respect to redundancy . they propose to further investigate the informative behaviour of e-mails using eoji . |
| Outcome: | The proposed model achieved an F-score of 0.7 for emoji use in 2475 tweets pairs. |
Open-Set Semi-Supervised Text Classification via Adversarial Disagreement Maximization (2024.acl-long)
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| Challenge: | Open-Set Semi-Supervised Text Classification (OSTC) aims to train a classification model on a limited set of labeled texts along with plenty of unlabeled examples. |
| Approach: | They propose to train a classification model on a limited set of labeled texts alongside plenty of unlabeled examples that include both in-distribution and out-of-difference examples. |
| Outcome: | The proposed model improves on outlier detection and abnormal example detection and calibration. |
Modeling Dialogue in Conversational Cognitive Health Screening Interviews (2020.lrec-1)
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| Challenge: | Dementia is one of the most pressing healthcare concerns as median age rises . a conversational agent capable of conducting cognitive health screening interviews could be an inexpensive, flexible, low-stress alternative . |
| Approach: | They propose an annotation schema for assigning dialogue act labels to utterances in patient-interviewer conversations collected as part of a clinically-validated cognitive health screening task. |
| Outcome: | The proposed system is characterized by high inter-annotator agreement and is able to perform clinically-validated cognitive health screening tasks. |
Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues (2020.lrec-1)
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| Challenge: | Using the MPAA rating, movie content can negatively affect children’s behaviour, for example, watching specific programs may encourage irresponsible sexual behavior and alcohol usage in teenagers. |
| Approach: | They propose an RNN-based architecture that jointly models the genre and the emotions in the script to predict the MPAA rating. |
| Outcome: | The proposed model outperforms the traditional machine learning method by 7% and achieves an 81% weighted F1 score. |
A Hybrid Model of Classification and Generation for Spatial Relation Extraction (2022.coling-1)
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| Challenge: | Existing studies only focus on spatial relations extraction as a classification task . spatial information is one kind of critical information for natural language understanding . |
| Approach: | They propose a hybrid model that generates null-role relations and extracts non-null-rol . they propose varying kinds of schemes to represent spatial relation . |
| Outcome: | The proposed model outperforms the baselines on the spatial relation extraction task on SpaceEval. |
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers (2022.findings-acl)
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| Challenge: | a recent study has investigated how transformer-based language models can be combined with active learning. |
| Approach: | They propose to combine transformer-based language models with active learning to reduce labeling costs . transformers are expensive, but they can be fine-tuned using a query strategy . they compare transformers to experiments from previous research to evaluate their performance . |
| Outcome: | The proposed model outperforms the well-known prediction entropy query strategy on five widely used text classification benchmarks. |
Speaker Turn Modeling for Dialogue Act Classification (2021.findings-emnlp)
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| Challenge: | Existing approaches to DA classification model utterances without incorporating the turn changes among speakers throughout the dialogue, thus treating it no different than non-interactive written text. |
| Approach: | They propose to integrate the turn changes in conversations among speakers when modeling DAs by learning conversation-invariant speaker turn embeddings to represent speaker turns in a conversation. |
| Outcome: | The proposed model captures semantics from the dialogue content while accounting for different speaker turns in a conversation. |
My Heart Skipped a Beat! Recognizing Expressions of Embodied Emotion in Natural Language (2024.naacl-long)
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| Challenge: | a new task is needed to recognize physical manifestations of emotions in natural language . physical manifestation of emotions affects not only our mental state but also our physical state . |
| Approach: | They propose a task to recognize expressions of embodied emotion in natural language . they use body part mentions with human annotations to extract emotional manner expressions . |
| Outcome: | The proposed model can train without gold data and improve performance with gold data. |
At the Intersection of NLP and Sustainable Development: Exploring the Impact of Demographic-Aware Text Representations in Modeling Value on a Corpus of Interviews (2022.lrec-1)
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| Challenge: | In order to preserve the privacy of speakers, we investigate encoding demographic information using autoencoders. |
| Approach: | They introduce a dataset of qualitative interviews from rural communities in India and Uganda and use it to enhance text representations with demographic information. |
| Outcome: | The proposed model extends the UPV classification model with demographic information to preserve the privacy of speakers. |
Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction (2025.coling-main)
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| Challenge: | Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis. |
| Approach: | They propose a novel approach to document-level event argument extraction that integrates predefined templates and generative language models into a foundational embedding derived from a classification model. |
| Outcome: | The proposed approach is more effective than baseline models and data-efficient in low-resource scenarios. |
Word-Level Loss Extensions for Neural Temporal Relation Classification (C18-1)
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| Challenge: | Unsupervised pre-trained word embeddings are used for many tasks in natural language processing to leverage unlabeled textual data. |
| Approach: | They extend the model's task loss with an unsupervised auxiliary loss on the word-embedding level of the model to ensure that the learned word representations contain both task-specific features and more general features. |
| Outcome: | The proposed model improves on the task of extracting narrative containment relations from clinical records using a general-domain part-of-speech tagger as linguistic resource. |
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder (2020.emnlp-main)
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| Challenge: | Existing methods for detecting public sentiment drift are not designed for sentiment drift detection. |
| Approach: | They propose a Hierarchical Variational Auto-Encoder model to learn better distribution representation and a new drift measure to directly evaluate distribution changes between historical and new data. |
| Outcome: | The proposed model performs better than three existing state-of-the-art methods. |
APPReddit: a Corpus of Reddit Posts Annotated for Appraisal (2022.lrec-1)
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Marco Antonio Stranisci, Simona Frenda, Eleonora Ceccaldi, Valerio Basile, Rossana Damiano, Viviana Patti
| Challenge: | Existing resources for emotion recognition are lacking for appraisal models. |
| Approach: | They propose to use APPReddit to annotate non-experimental data according to Appraisal theories . they compare it with enISEAR, a corpus of events created in an experimental setting and annotated according to this theory. |
| Outcome: | The proposed model predicts four appraisal dimensions without significant loss . the proposed model is compared with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. |
Exploring Distantly-Labeled Rationales in Neural Network Models (2021.acl-long)
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| Challenge: | Existing methods focus on distantly-labeled rationales, ignoring the potential important non-rationale words and not distinguishing the importance of different rationale words. |
| Approach: | They propose two novel auxiliary loss functions to make better use of distantly-labeled rationales, which encourage models to maintain their focus on important words beyond labeled rationals (PINs) and alleviate redundant training on non-helpful rationale (NoIRs). |
| Outcome: | The proposed methods outperform existing methods on two representative classification tasks while maintaining the ability to spread focus to other unlabeled important words. |
Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System (2020.coling-main)
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| Challenge: | In smart speakers and conversational robots, the demand for expressive speech synthesis has increased. |
| Approach: | They propose to annotate a news dataset with emotion labels for each sentence and to evaluate its effectiveness using the constructed dataset. |
| Outcome: | The proposed method improves the performance of the proposed model by preferentially annotating news articles with low confidence in the human-in-the-loop machine learning framework. |
Text Counterfactuals via Latent Optimization and Shapley-Guided Search (2021.emnlp-main)
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| Challenge: | Using latent optimization and Shapley values, we generate a set of minimal modifications to the text to change the classifier's prediction. |
| Approach: | They propose to generate a counterfactual by making minimal modifications to the text to change the model's prediction. |
| Outcome: | The proposed approach achieves favorable performance compared to white-box and black-box baselines using human and automatic evaluations. |
Cold-start Active Learning through Self-supervised Language Modeling (2020.emnlp-main)
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| Challenge: | Labeling data is a fundamental bottleneck in machine learning due to annotation cost and time. |
| Approach: | They propose a strategy that uses the pre-training loss to find examples that surprise the model and minimize labeling costs. |
| Outcome: | The proposed approach reduces labeling costs and costs by using pre-trained language models. |
Integrating Personalized PageRank into Neural Word Sense Disambiguation (2021.emnlp-main)
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| Challenge: | Neural Word Sense Disambiguation (WSD) uses pre-existing knowledge, but only close neighbors influence prediction. |
| Approach: | They propose to exploit WordNet graphs to improve a classification model by recomputing logits . they incorporate an online neural approximated PageRank to refine edge weights . |
| Outcome: | The proposed method improves the current state of the art in the field of Neural Word Sense Disambiguation (WSD) the proposed method exploits the global graph structure while keeping space requirements linear in the number of edges. |
GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification (2025.coling-main)
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| Challenge: | Existing models generate text on demand, but in real-life situations, individuals do not continuously generate text or voice opinions. |
| Approach: | They propose a novel task to identify news-triggered opinion expressing timing by using a dataset generated by professional stock analysts. |
| Outcome: | The proposed model can generate opinion on stock analysts' actions and improves performance in various opinion understanding tasks. |