Papers with label prediction
Modelling Uncertainty in Collaborative Document Quality Assessment (D19-55)
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| Challenge: | Existing work on document quality assessment relies on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task. |
| Approach: | They propose to use Gaussian processes and random forests to measure the uncertainty in document quality predictions. |
| Outcome: | The proposed methods can predict the quality of Wikipedia articles while providing an estimate of uncertainty when there is inconsistency in the quality labels from the contributors. |
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition (2020.acl-srw)
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| Challenge: | In general, the labels used in sequence labeling consist of different types of elements. |
| Approach: | They propose to integrate label component information as embeddings into sequence labeling models. |
| Outcome: | The proposed method improves on English and Japanese fine-grained named entity recognition on low-frequency labels. |
In-context Contrastive Learning for Event Causality Identification (2024.emnlp-main)
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| Challenge: | Recent prompt learning-based approaches have shown promising improvements on the ECI task . however, they are subject to the delicate design of multiple prompts and positive correlations between the main task and derivate tasks. |
| Approach: | They propose an event causality identification model that uses contrastive learning to enhance both positive and negative demonstrations. |
| Outcome: | The proposed model improves on the event-related causality identification task . it uses contrastive learning to enhance both positive and negative demonstrations . |
Exploring Listwise Evidence Reasoning with T5 for Fact Verification (2021.acl-short)
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| Challenge: | Existing methods for fact verification use pretrained sequence-to-sequence transformers for sentence selection and label prediction. |
| Approach: | They propose a framework for fact verification that leverages pretrained sequence-to-sequence transformer models for sentence selection and label prediction. |
| Outcome: | The proposed framework scores higher than the second place approach on the blind test set . the proposed framework can be useful for a broader range of NLP tasks, the authors say . |
Compositional Zero-Shot Domain Transfer with Text-to-Text Models (2023.tacl-1)
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Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland
| Challenge: | Existing approaches to zero-shot domain transfer are limited by domain gap and lack of in-domain labels. |
| Approach: | They propose a compositional transfer learning framework (DoT51) that learns domain knowledge and task knowledge in a multi-task manner without access to in-domain labels. |
| Outcome: | The proposed framework outperforms the current state-of-the-art in zero-shot domain transfer by over 7 absolute points in accuracy on RadNLI. |
Long-tailed Extreme Multi-label Text Classification by the Retrieval of Generated Pseudo Label Descriptions (2023.findings-eacl)
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| Challenge: | Extreme Multi-label text classification (XMTC) is a tough challenge due to the sheer size of the label spaces and the severe data scarcity problem associated with the long tail of rare labels in highly skewed distributions. |
| Approach: | They propose to use a trained bag-of-words classifier to generate pseudo label descriptions from a training bag- of-word classifier. |
| Outcome: | The proposed approach outperforms the existing models in the tail label prediction problem and achieves state-of-the-art (SOTA) performance on XMTC benchmark datasets. |
NormNet: Normalize Noun Phrases for More Robust NLP (2023.findings-acl)
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| Challenge: | Existing approaches to debunk false features in deep NLP models are inadequate . previous work suggests that models learn spurious features instead of the true signals of the task . |
| Approach: | They propose a normalization strategy to eliminate false features caused by the textual surfaces of noun phrases. |
| Outcome: | The proposed normalization strategy eliminates false features caused by the textual surfaces of noun phrases. |
Learning to Maximize Mutual Information for Chain-of-Thought Distillation (2024.findings-acl)
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| Challenge: | Knowledge distillation is a technique of transferring knowledge from large, complex models to smaller ones. |
| Approach: | They propose a method utilizing chain-of-thought distillation to transfer knowledge from large, complex models to smaller ones by maximizing mutual information of the representation features of the two tasks. |
| Outcome: | The proposed method outperforms the state-of-the-art knowledge distillation method on four datasets. |
What Part of the Neural Network Does This? Understanding LSTMs by Measuring and Dissecting Neurons (D19-1)
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| Challenge: | Biological neural systems consist of a huge number of neurons, and can react to the environment in complicated ways. |
| Approach: | They propose a metric to quantify the sensitivity of neurons to each label and conduct experiments to prove it. |
| Outcome: | The proposed metric is based on a set of experiments that show that dropping an arbitrary neuron significantly degrades the accuracy of the model. |
Joint Alignment of Multi-Task Feature and Label Spaces for Emotion Cause Pair Extraction (2022.coling-1)
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| Challenge: | Existing methods for ECPE fail to model specific features and interactive features in between, or suffer from inconsistency of label prediction. |
| Approach: | They propose to align ECPE with a feature-task alignment mechanism to model emotion-&cause-specific features and the shared interactive feature. |
| Outcome: | The proposed model outperforms existing systems on all ECA subtasks. |
BoundRL: Efficient Token-level Structured Text Segmentation through Reinforced Boundary Generation (2026.findings-acl)
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Haoyuan Li, Zhengyuan Shen, Sullam Jeoung, Yueyan Chen, Jiayu Li, Qi Zhu, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Structured texts often contain elements beyond plain language, such as code snippets, which conventional sentence-level segmentation methods cannot handle effectively. |
| Approach: | They propose a token-level approach that performs efficient token-based text segmentation and label prediction for long structured texts. |
| Outcome: | The proposed approach outperforms existing models on short-shot prompts and SFT and standard RLVR models on complex LLM prompts. |