Papers with sentence understanding
Linking artificial and human neural representations of language (D19-1)
Copied to clipboard
| Challenge: | a pre-trained BERT architecture is used to fine-tune sentence encoding models on a variety of natural language understanding (NLU) tasks. |
| Approach: | They compare sentence encoding models with fMRI-based fMR predictions of the sentence . they use a pre-trained BERT architecture as a baseline and fine-tune it on a variety of natural language understanding (NLU) tasks. |
| Outcome: | The proposed model does not yield significant improvements in brain decoding performance on the natural language understanding (NLU) tasks. |
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference (N18-1)
Copied to clipboard
| Challenge: | et al., 1996, show that many of the most actively studied problems in NLP depend in large part on natural language understanding (NLU). |
| Approach: | They propose a dataset for machine learning that uses ten different genres of English to evaluate sentences for their meanings. |
| Outcome: | The multi-genre natural language inference corpus is one of the largest available for natural language understanding. |
Constructing High Quality Sense-specific Corpus and Word Embedding via Unsupervised Elimination of Pseudo Multi-sense (L18-1)
Copied to clipboard
| Challenge: | Existing word embedding frameworks distinguish different senses of words by their contexts. |
| Approach: | They propose a framework for unsupervised corpus sense tagging which trains multi-sense word embeddings on a given corpus. |
| Outcome: | The proposed framework detects pseudo multi-senses without extra language resources without additional language resources. |
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing uncertainty sampling methods are time-consuming and can't be executed frequently. |
| Approach: | They propose adversarial uncertainty sampling in discrete space to find informative unlabeled text samples for annotation using adversarials. |
| Outcome: | The proposed approach outperforms baselines on effectiveness on five datasets. |
Identifying Physical Object Use in Sentences (2022.emnlp-main)
Copied to clipboard
| Challenge: | Prior research has focused on learning the prototypical functions of physical objects . but many sentences refer to objects even when they are not used . |
| Approach: | They propose a task that determines whether a physical object mentioned in a sentence was used or likely will be used. |
| Outcome: | The proposed model exploits data augmentation methods and FrameNet to fine-tune a pre-trainedmodel. |