In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

[Q]: Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese.  The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical). 
 Question: What languages do they experiment on?
[A]: English, German, Spanish, Italian, Japanese and Portuguese  English, Arabic and Chinese


[Q]: We compared our model with MLE, RL and GAN baselines. Since COCO and EMNLP2017 WMT don't have input while WeiboDial regards posts as input, we chose the following baselines respectively:

MLE: a RNN model trained with MLE objective BIBREF4 . Its extension, Seq2Seq, can work on the dialogue dataset BIBREF2 .

SeqGAN: The first text GAN model that updates the generator with policy gradient based on the rewards from the discriminator BIBREF7 .

LeakGAN: A variant of SeqGAN that provides rewards based on the leaked information of the discriminator for the generator BIBREF11 .

MaliGAN: A variant of SeqGAN that optimizes the generator with a normalized maximum likelihood objective BIBREF8 .

IRL: This inverse reinforcement learning method replaces the discriminator with a reward approximator to provide dense rewards BIBREF12 .

RAML: A RL approach to incorporate MLE objective into RL training framework, which regards BLEU as rewards BIBREF17 .

DialogGAN: An extension of SeqGAN tuned to dialogue generation task with MLE objective added to the adversarial objective BIBREF16 .

DPGAN: A variant of DialogGAN which uses a language model based discriminator and regards cross-entropy as rewards BIBREF13 . 
 Question: What GAN models were used as baselines to compare against?
[A]: MLE SeqGAN LeakGAN MaliGAN IRL RAML DialogGAN DPGAN


[Q]: In this section we describe how to evaluate and compare the outcomes of algorithms which assign relevance scores to words (such as LRP or SA) through intrinsic validation. Furthermore, we propose a measure of model explanatory power based on an extrinsic validation procedure. 
 Question: Are the document vectors that the authors introduce evaluated in any way other than the new way the authors propose?
[A]:
Yes