| Challenge: | Existing approaches to multi-label classification are based on pre-specifying the label order, or relating the sequence probability to the set probability in ad hoc ways. |
| Approach: | They propose a new training objective that maximizes this set probability and a prediction objective that finds the most probable set on a test document. |
| Outcome: | The proposed model outperforms existing methods on a set of labels for multi-label classification . the proposed model is based on 'set probability' and 'prediction objective' |
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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 Deep Reinforced Sequence-to-Set Model for Multi-Label Classification (P19-1)
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| Challenge: | Multi-label classification (MLC) aims to assign multiple labels to each sample. |
| Approach: | They propose a sequence-to-set model that is trained via reinforcement learning and rewards feedback independent of the label order. |
| Outcome: | The proposed model outperforms baseline models and reduces sensitivity to label order. |
Bringing Emerging Architectures to Sequence Labeling in NLP (2026.eacl-long)
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| Challenge: | Pretrained Transformer encoders are the dominant approach to sequence labeling . however, few have been applied to sequence labels on flat or simplified tasks . |
| Approach: | They propose to use pretrained Transformer encoders to model relations across words . they find that the architectures adapt well across tagging tasks that vary in complexity . |
| Outcome: | The proposed architectures perform well across tagging tasks across languages and datasets. |
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks (2020.emnlp-main)
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| Challenge: | Recent work shows that conditional random fields (CRFs) perform well in sequence labeling tasks. |
| Approach: | They propose several high-order energy terms to capture dependencies among labels in sequence labeling . they use convolutional, recurrent, and self-attention networks to construct these energy terms . |
| Outcome: | The proposed approach improves on four sequence labeling tasks while having the same decoding speed as simple classifiers. |
Design Challenges and Misconceptions in Neural Sequence Labeling (C18-1)
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| Challenge: | Existing neural sequence labeling models have been used for many tasks such as POS tagging, chunking and named entity recognition (NER). |
| Approach: | They propose to replicate twelve neural sequence labeling models and compare them to three benchmarks to find out which models are effective and which are inconsistent. |
| Outcome: | The proposed models are compared on NER, Chunking, and POS tagging benchmarks. |
Variational Sequential Labelers for Semi-Supervised Learning (D18-1)
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| Challenge: | a family of multitask variational methods for semi-supervised sequence labeling is currently unclear how to use them in the context of sequence labelling. |
| Approach: | They propose a family of multitask variational methods for semi-supervised sequence labeling using latent variables and a discriminative labeler. |
| Outcome: | The proposed models outperform standard sequential baselines on 8 sequence labeling datasets and improve further with unlabeled data. |
Document Ranking with a Pretrained Sequence-to-Sequence Model (2020.findings-emnlp)
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| Challenge: | Experimental results on the MS MARCO passage ranking task show that our ranking approach is superior to strong encoder-only models. |
| Approach: | They propose to use a pretrained sequence-to-sequence model to generate relevance labels as "target tokens" they also show how the underlying logits of these target tokens can be interpreted as relevance probabilities for ranking. |
| Outcome: | The proposed model outperforms existing models in a data-poor setting and significantly outperformed an encoder-only model on the MS MARCO passage ranking task. |
Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens (N18-1)
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| Challenge: | Recent work has used attention weights to visualize the focus of neural models in input data. |
| Approach: | They propose to use attention-based visualization techniques to infer token-level labels from a network trained only on sentence-level labeling. |
| Outcome: | The proposed approach outperforms gradient-based methods on four datasets and is expected to outperfect supervised methods. |
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces (D18-1)
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| Challenge: | Large multi-label datasets contain labels that occur thousands of times (frequent group), those that occur only a few times (few-shot group) and labels that never appear in the training dataset (zero-shot groups). |
| Approach: | They perform a fine-grained evaluation to understand how state-of-the-art methods perform on infrequent labels. |
| Outcome: | The proposed methods improve on two publicly available datasets for multi-label text classification. |
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN (2023.findings-acl)
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| Challenge: | Existing sequence labeling algorithms can be decomposed into two parts . |
| Approach: | They propose a graph neural networks sequence labeling (GNN-SL) that augments the vanilla SL model output with similar tagging examples retrieved from the whole training set. |
| Outcome: | The proposed model performs well on three sequence labeling tasks. |