| Challenge: | Existing methods to ground entities depend on inference or non-differentiable losses. |
| Approach: | They propose a phrase grounding task that grounds entities to corresponding regions in an image . they use neural chain Conditional Random Fields to model dependencies among regions . |
| Outcome: | The proposed method is based on a dataset of the Flickr30k Entities dataset. |
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Improving Pre-trained Vision-and-Language Embeddings for Phrase Grounding (2021.emnlp-main)
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| Challenge: | Existing studies have focused on the phrase grounding ability of pretrained vision-and-language models, but it is unclear how they can be used for phrase ground. |
| Approach: | They propose to extract phrase-region pairs from pre-trained vision-and-language embeddings and propose four fine-tuning objectives to improve model phrase grounding ability using image-caption data without any supervised grounding signals. |
| Outcome: | The proposed model outperforms baseline models in weakly-supervised and supervised phrase grounding settings on two representative datasets and shows that it is possible to achieve better phrase groundability without sacrificing representation generality. |
Hybrid semi-Markov CRF for Neural Sequence Labeling (P18-2)
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| Challenge: | Existing conditional random fields (CRFs) use hand-crafted features to perform sequence labeling tasks. |
| Approach: | They propose to use semi-Markov conditional random fields for neural sequence labeling in natural language processing to extract features from segments instead of words. |
| Outcome: | The proposed model achieves state-of-the-art when no external knowledge is used. |
Contrastive Learning with Expectation-Maximization for Weakly Supervised Phrase Grounding (2022.emnlp-main)
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| Challenge: | Weakly supervised phrase grounding aims to learn an alignment between phrases in a caption and objects in an image using only caption-image annotations. |
| Approach: | They propose a novel contrastive learning framework that adaptively refines the target prediction by using only caption-image annotations. |
| Outcome: | The proposed framework outperforms existing methods on two widely used benchmarks, Flickr30K Entities and RefCOCO+. |
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. |
Uncertainty-Aware Label Refinement for Sequence Labeling (2020.emnlp-main)
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| Challenge: | Conditional random fields (CRF) for label decoding have been a problem for many tasks. |
| Approach: | They propose a two-stage label decoding framework that model long-term label dependencies while being much more computationally efficient. |
| Outcome: | The proposed method outperforms the CRF-based methods and greatly accelerates the inference process. |
Bregman Conditional Random Fields: Sequence Labeling with Parallelizable Inference Algorithms (2025.acl-long)
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| Challenge: | Existing methods for sequence labeling are hidden Markov models and conditional random fields (CRF). |
| Approach: | They propose a new discriminative model for sequence labeling called Bregman conditional random fields (BCRF) they propose to use Fenchel-Young losses to learn from partial labels. |
| Outcome: | The proposed model performs better in highly constrained settings than the existing model, which is slower and faster. |
AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network (2020.emnlp-main)
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| Challenge: | Existing approaches to sequence labeling require sequential computation that makes parallelization impossible. |
| Approach: | They propose to employ a parallelizable approximate variational inference algorithm for the CRF model. |
| Outcome: | The proposed approach improves decoding speed and accuracy with long sentences and is parallelizable for faster training and prediction. |
Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents (2020.emnlp-main)
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| Challenge: | Existing image-text grounding approaches require detailed annotations, authors say . existing methods are difficult to adapt to unlabeled multi-image, multi-sentence documents, they say . |
| Approach: | They propose a method that can learn contextual meanings from unlabeled documents . they demonstrate that a simple unsupervised clustering-based method can be useful . |
| Outcome: | The proposed method is particularly effective for local contextual meanings of a word . existing image-text grounding methods are difficult to adapt to unlabeled multi-image, multi-sentence documents . |
Hierarchically-Refined Label Attention Network for Sequence Labeling (D19-1)
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| 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. |
Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers (2020.findings-emnlp)
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| Challenge: | Named entity recognition models use a conditional random field as the final layer . current work eschews prior knowledge of how the span encoding scheme works . |
| Approach: | They propose to constrain the output to suppress illegal transitions to train a tagger with a cross-entropy loss twice as fast as a CRF. |
| Outcome: | The proposed model trains twice as fast as a CRF with statistically insignificant differences in F1 . the proposed model is open source and can be used in PyTorch and TensorFlow. |