1 |
The (un)suitability of automatic evaluation metrics for text simplification
|
|
|
|
BASE
|
|
Show details
|
|
4 |
AStitchInLanguageModels : dataset and methods for the exploration of idiomaticity in pre-trained language models
|
|
|
|
BASE
|
|
Show details
|
|
5 |
ASSET : a dataset for tuning and evaluation of sentence simplification models with multiple rewriting transformations
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Probing the need for visual context in multimodal machine translation
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Taking MT evaluation metrics to extremes : beyond correlation with human judgments
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Assessing multilingual multimodal image description: Studies of native speaker preferences and translator choices
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Exploring the use of Acoustic Embeddings in Neural Machine Translation
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Exploring gap filling as a cheaper alternative to reading comprehension questionnaires when evaluating machine translation for gisting
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Object counts! Bringing explicit detections back into image captioning
|
|
|
|
BASE
|
|
Show details
|
|
16 |
End-to-end image captioning exploits multimodal distributional similarity
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Semi-supervised adaptation of RNNLMs by fine-tuning with domain-specific auxiliary features
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Sheffield MultiMT: Using Object Posterior Predictions for Multimodal Machine Translation
|
|
|
|
BASE
|
|
Show details
|
|
20 |
Unraveling the Contribution of Image Captioning and Neural Machine Translation for Multimodal Machine Translation
|
|
|
|
BASE
|
|
Show details
|
|
|
|