806 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
Adversarial training for multi-context joint entity and relation extraction
Adversarial training (AT) is a regularization method that can be used to
improve the robustness of neural network methods by adding small perturbations
in the training data. We show how to use AT for the tasks of entity recognition
and relation extraction. In particular, we demonstrate that applying AT to a
general purpose baseline model for jointly extracting entities and relations,
allows improving the state-of-the-art effectiveness on several datasets in
different contexts (i.e., news, biomedical, and real estate data) and for
different languages (English and Dutch).Comment: EMNLP 2018, code is available at
https://github.com/bekou/multihead_joint_entity_relation_extractio
Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction
Recent neural-based relation extraction approaches, though achieving
promising improvement on benchmark datasets, have reported their vulnerability
towards adversarial attacks. Thus far, efforts mostly focused on generating
adversarial samples or defending adversarial attacks, but little is known about
the difference between normal and adversarial samples. In this work, we take
the first step to leverage the salience-based method to analyze those
adversarial samples. We observe that salience tokens have a direct correlation
with adversarial perturbations. We further find the adversarial perturbations
are either those tokens not existing in the training set or superficial cues
associated with relation labels. To some extent, our approach unveils the
characters against adversarial samples. We release an open-source testbed,
"DiagnoseAdv" in https://github.com/zjunlp/DiagnoseAdv.Comment: IJCKG 202
Neural approaches to sequence labeling for information extraction
Een belangrijk aspect binnen artificiële intelligentie (AI) is het interpreteren van menselijke taal uitgedrukt in tekstuele (geschreven) vorm: natural Language processing (NLP) is belangrijk gezien tekstuele informatie nuttig is voor veel toepassingen. Toch is het verstaan ervan (zogenaamde natural Language understanding, (NLU) een uitdaging, gezien de ongestructureerde vorm van tekst, waarvan de betekenis vaak dubbelzinnig en contextafhankelijk is. In dit proefschrift introduceren we oplossingen voor tekortkomingen van gerelateerd werk bij het behandelen van fundamentele taken in natuurlijke taalverwerking, zoals named entity recognition (i.e. het identificeren van de entiteiten die in een zin voorkomen) en relatie-extractie (het identificeren van relaties tussen entiteiten). Vertrekkend van een specifiek probleem (met name het identificeren van de structuur van een huis aan de hand van een tekstueel zoekertje), bouwen we stapsgewijs een complete (geautomatiseerde) oplossing voor de bovengenoemde taken, op basis van neutrale netwerkarchitecturen. Onze oplossingen zijn algemeen toepasbaar op verschillende toepassingsdomeinen en talen. We beschouwen daarnaast ook de taak van het identificeren van relevante gebeurtenissen tijdens een evenement (bv. een doelpunt tijdens een voetbalwedstrijd), in informatiestromen op Twitter. Meer bepaald formuleren we dit probleem als het labelen van woord sequenties (vergelijkbaar met named entity recognition), waarbij we de chronologische relatie tussen opeenvolgende tweets benutten
On Robustness and Bias Analysis of BERT-based Relation Extraction
Fine-tuning pre-trained models have achieved impressive performance on
standard natural language processing benchmarks. However, the resultant model
generalizability remains poorly understood. We do not know, for example, how
excellent performance can lead to the perfection of generalization models. In
this study, we analyze a fine-tuned BERT model from different perspectives
using relation extraction. We also characterize the differences in
generalization techniques according to our proposed improvements. From
empirical experimentation, we find that BERT suffers a bottleneck in terms of
robustness by way of randomizations, adversarial and counterfactual tests, and
biases (i.e., selection and semantic). These findings highlight opportunities
for future improvements. Our open-sourced testbed DiagnoseRE is available in
\url{https://github.com/zjunlp/DiagnoseRE}.Comment: work in progres
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