Papers with approximate inference
Robust Question Answering Through Sub-part Alignment (2021.naacl-main)
Copied to clipboard
| Challenge: | Current textual question answering models fail to generalize to out-of-domain settings. |
| Approach: | They propose to decompose question and context into smaller units and align them to find the answer. |
| Outcome: | The proposed model is more robust than the standard BERT QA model on adversarial and out-of-domain datasets. |
Probabilistic Transformer: A Probabilistic Dependency Model for Contextual Word Representation (2023.findings-acl)
Copied to clipboard
| Challenge: | Syntactic structures were deemed essential in natural language processing . but since the deep learning revolution, NLP has been dominated by neural models that do not consider syntactical structures in their design. |
| Approach: | They propose a model that models latent representations of words in a sentence . they use a conditional random field to model latent and dependency arcs . |
| Outcome: | The proposed model performs competitively to transformers on small to medium sized datasets. |
Phrase Grounding by Soft-Label Chain Conditional Random Field (D19-1)
Copied to clipboard
| 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. |