901 research outputs found
Robust Multilingual Part-of-Speech Tagging via Adversarial Training
Adversarial training (AT) is a powerful regularization method for neural
networks, aiming to achieve robustness to input perturbations. Yet, the
specific effects of the robustness obtained from AT are still unclear in the
context of natural language processing. In this paper, we propose and analyze a
neural POS tagging model that exploits AT. In our experiments on the Penn
Treebank WSJ corpus and the Universal Dependencies (UD) dataset (27 languages),
we find that AT not only improves the overall tagging accuracy, but also 1)
prevents over-fitting well in low resource languages and 2) boosts tagging
accuracy for rare / unseen words. We also demonstrate that 3) the improved
tagging performance by AT contributes to the downstream task of dependency
parsing, and that 4) AT helps the model to learn cleaner word representations.
5) The proposed AT model is generally effective in different sequence labeling
tasks. These positive results motivate further use of AT for natural language
tasks.Comment: NAACL 201
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging
Fine-tuning neural networks is widely used to transfer valuable knowledge
from high-resource to low-resource domains. In a standard fine-tuning scheme,
source and target problems are trained using the same architecture. Although
capable of adapting to new domains, pre-trained units struggle with learning
uncommon target-specific patterns. In this paper, we propose to augment the
target-network with normalised, weighted and randomly initialised units that
beget a better adaptation while maintaining the valuable source knowledge. Our
experiments on POS tagging of social media texts (Tweets domain) demonstrate
that our method achieves state-of-the-art performances on 3 commonly used
datasets
Adversarial Learning for Chinese NER from Crowd Annotations
To quickly obtain new labeled data, we can choose crowdsourcing as an
alternative way at lower cost in a short time. But as an exchange, crowd
annotations from non-experts may be of lower quality than those from experts.
In this paper, we propose an approach to performing crowd annotation learning
for Chinese Named Entity Recognition (NER) to make full use of the noisy
sequence labels from multiple annotators. Inspired by adversarial learning, our
approach uses a common Bi-LSTM and a private Bi-LSTM for representing
annotator-generic and -specific information. The annotator-generic information
is the common knowledge for entities easily mastered by the crowd. Finally, we
build our Chinese NE tagger based on the LSTM-CRF model. In our experiments, we
create two data sets for Chinese NER tasks from two domains. The experimental
results show that our system achieves better scores than strong baseline
systems.Comment: 8 pages, AAAI-201
- …