[PDF] Top 20 Adversarial Training for Relation Extraction

... of relation ex- traction, where the goal is to predict the relation that exists between a particular entity pair given several text ...setting, relation extraction is much harder than the ... See full document

... the relation extraction ...that relation labels are not mutually ...one relation at a time, but increase the complexity of the NER ...simultaneous extraction of multi- ple relations ... See full document

... generative adversarial training method for robust distant supervision relation ...With adversarial train- ing, our goal is to gradually decrease the perfor- mance of the discriminator, while ... See full document

... incorporate adversarial training, ...in training, to improve the ro- bustness of relation ...versarial training can improve its performance by a good ... See full document

... no-self- training in which no silver instances were used, all-Merge in which all silver instances were used, sub-Merge in which a subset of silver samples were used, and Posi-Merge in which only the positive ... See full document

... the training data of implicit dis- course relations, prior works have used weak su- pervision from sentences with discourse connec- tives (Marcu and Echihabi, 2002; Sporleder and Lascarides, 2008; Braud and Denis, ... See full document

... the training corpus and tested whether each extrac- tion made was in fact a negative ...the training corpus contains one million documents, this method only yielded a few thou- sand new negative instances ... See full document

... tion network. Our model trained within the pro- posed framework provides significant improve- ment, showing the benefits of utilizing implicit connectives at training time. 3) “Ensemble” has the same neural ... See full document

... and training the classifiers using ACE relation sub-types (rather than on types), they achieved an impressive ...lation extraction task and might not be appropriate to compare against the automatic ... See full document

... (2014), adversarial training has been explored for several NLP applications recently to resist noise, such as text classification (Miyato et ...2018). Adversarial training has also been ... See full document

... during training and never get punished for: (1) a model can learn that it is unlikely for the last sentence to contain the real answer; (2) a model can learn that the fixed set of fake answers should not be ... See full document

... We validate our adversarial model for MRQA Shared Task with 6 different out-of-domain datasets, which are BioASQ (BA) (Tsatsaronis et al., 2012), DROP (DP) (Dua et al., 2019), DuoRC (DR) (Saha et al., 2018), RACE ... See full document

... Temporal relation extraction is important for understanding the ordering of events in narrative ...high-quality training instances available to a temporal relation extraction task, with ... See full document

... Some other methods also do not have such re- quirement. Plank and Moschitti (2013) designed the semantic syntactic tree kernel (SSTK) to learn cross-domain patterns. Nguyen et al. (2015b) con- structed a case study ... See full document

... Moreover, we propose an iterative human- machine co-training framework for the task of RE. The main idea is (i) to automatically select a subset of less-noisy examples applying an automatic clas- sifier, (ii) ... See full document

... labelled relation-specific training ...ates training data automatically by aligning a KB with plain ...a relation in KB, then all sentences that contain these two entities will express this ... See full document

... BC-V training corpus in a sentence-level fashion, making it difficult to directly apply conventional machine learning ...event extraction/semantic role labeling models induced from other training ... See full document

... A goal of natural language processing is to de- velop techniques that enable machines to process naturally occurring language. However, not all language is clear and, as humans, we may not always understand each other ... See full document

... Table 6 shows the results. It’s obvious that ran- dom sampling hurts the model performance ex- cept Self-BLEU-1, because it indeed allows low- quality samples available to the generator. Explor- ing these samples ... See full document

... In the CoNLL 2017 shared task (Zeman et al., 2017), some language data is available in more than one treebanks typically from different anno- tation projects. While the treebanks differ in many respects such as the genre ... See full document