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This l)al)er describes novel and practical .lal)anesc parsers that uses decision trees, l"irst, we COl> struct a single, decision tree to estimate modifica-- lion probabilities; how one phrase tends t.o …...

Recent work on Conditional Random Fields (CRFs) has demonstrated the need for regularisation to counter the tendency of these models to overfit. The standard approach to regularising CRFs involves a …...

AbstractSeveral corpora annotated for coreference have been made available in the past decade. These resources differ with respect to their size and the underlying structure: the number of domains and …...

The performance of NLP classifiers largely depends on the quality of the features considered for prediction (feature engineering). However, as the number of features increases, the more likely overfit- ting …...

AbstractWe investigate techniques for supervised domain adaptation for neural machine translation where an existing model trained on a large out-of-domain dataset is adapted to a small in-domain dataset. In this …...

AbstractRecurrent neural networks (RNNs) have shown promising performance for language modeling. However, traditional training of RNNs using back-propagation through time often suffers from overfitting. One reason for this is that …...

This paper focuses on supervised hypernymy detection using distributional representations for unknown word pairs. Levy et al. (2015) demonstrated that supervised hypernymy detection suffers from overfitting hypernyms in training data. …...

AbstractThe performance of fine-tuning pre-trained language models largely depends on the hyperparameter configuration. In this paper, we investigate the performance of modern hyperparameter optimization methods (HPO) on fine-tuning pre-trained language …...

AbstractProviding pretrained language models with simple task descriptions in natural language enables them to solve some tasks in a fully unsupervised fashion. Moreover, when combined with regular learning from examples, …...

AbstractThis paper describes Huawei Artificial Intelligence Application Research Center’s neural machine translation systems and submissions to the WMT21 biomedical translation shared task. Four of the submissions achieve state-of-the-art BLEU scores …...

AbstractFew-shot text classification aims to classify the text under the few-shot scenario. Most of the previous methods adopt optimization-based meta learning to obtain task distribution. However, due to the neglect …...

AbstractOverfitting is a notorious problem when there is insufficient data to train deep neural networks in machine learning tasks. Data augmentation regularization methods such as Dropout, Mixup, and their enhanced …...

AbstractData augmentation is an effective approach to tackle over-fitting. Many previous works have proposed different data augmentations strategies for NLP, such as noise injection, word replacement, back-translation etc. Though effective, …...

AbstractKnowledge distillation (KD) is a highly promising method for mitigating the computational problems of pre-trained language models (PLMs). Among various KD approaches, Intermediate Layer Distillation (ILD) has been a de …...