Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens (N18-1)
Copied to clipboard
| 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. |
Similar Papers
Hierarchically-Refined Label Attention Network for Sequence Labeling (D19-1)
Copied to clipboard
| Challenge: | Conditional random fields (CRF) is a powerful model for statistical sequence labeling, but it does not give much information gain over strong neural encoding. |
| Approach: | They propose a hierarchically-refined label attention network which captures potential long-term label dependency by giving each word incrementally refined label distributions with hierarchical attention. |
| Outcome: | The proposed model improves POS tagging accuracy and speeds up training and testing compared to the current model. |
Bringing Emerging Architectures to Sequence Labeling in NLP (2026.eacl-long)
Copied to clipboard
| 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. |
Generating Token-Level Explanations for Natural Language Inference (N19-1)
Copied to clipboard
| Challenge: | Existing methods to generate token-level explanations for NLI on single sentences have not been tested. |
| Approach: | They propose to generate token-level explanations for NLI without explicitly annotating training data. |
| Outcome: | The proposed approach is faster and more accurate than the black-box methods. |
Generating Structured Pseudo Labels for Noise-resistant Zero-shot Video Sentence Localization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing zero-shot pipelines generate event proposals and then generate a pseudo query for each event proposal. |
| Approach: | They propose a Structure-based Pseudo Label generation (SPL) that generates free-form interpretable pseudo queries before constructing query-dependent event proposals. |
| Outcome: | The proposed method learns with only video data without any annotation . it generates free-form interpretable pseudo queries before constructing query-dependent event proposals . |
Token-Level Self-Evolution Training for Sequence-to-Sequence Learning (2023.acl-short)
Copied to clipboard
| Challenge: | Adaptive training approaches do not consider the variation of learning difficulty in different training steps, making the learning deterministic and sub-optimal. |
| Approach: | They propose a dynamic token-level self-evolution training method that reweighs the training losses of different target tokens based on priors. |
| Outcome: | Empirically, the proposed method yields significant improvements on three translation tasks. |
Trainable, Multiword-aware Linguistic Tokenization Using Modern Neural Networks (2026.eacl-srw)
Copied to clipboard
| Challenge: | Tokenization is a fundamental task in natural language processing that forms the first step of many pipelines. |
| Approach: | They propose to use a standard tokenizer trained without MWE-awareness as a baseline and a character-level SRN+CRF model to train token-level models. |
| Outcome: | The proposed tokenizers are based on a character-level and token-level sequence labeling problem and are consistent with the proposed pipelines. |
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks (2020.emnlp-main)
Copied to clipboard
| 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. |
Token-level and sequence-level loss smoothing for RNN language models (P18-1)
Copied to clipboard
| Challenge: | Maximum likelihood estimation treats all sentences that do not match the ground truth as equally poor, ignoring the structure of the output space. |
| Approach: | They propose to extend the reward augmented maximum likelihood approach to token-level loss smoothing by using token-based approaches to improve the model's performance. |
| Outcome: | The proposed model improves on image captioning and machine translation tasks and treats all sentences that do not match the ground truth as poor . |
NAT: Noise-Aware Training for Robust Neural Sequence Labeling (2020.acl-main)
Copied to clipboard
| Challenge: | Sequence labeling systems should perform reliably under ideal conditions and with corrupted inputs. |
| Approach: | They propose two noise-aware training objectives that improve robustness of sequence labeling performed on perturbed inputs. |
| Outcome: | The proposed methods improve robustness on English and German named entity recognition benchmarks. |
Pre-trained Language Models Can be Fully Zero-Shot Learners (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to pre-trained language models require fine-tuning on labeled datasets or manually constructing proper prompts. |
| Approach: | They propose a nonparametric prompting PLM for fully zero-shot language understanding . they compare it to previous methods for text classification and text entailment . |
| Outcome: | The proposed method outperforms previous methods on diverse tasks. |