TASK DEFINITION: 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.
PROBLEM: The baseline is a hierarchical phrase-based system BIBREF29 with a 4-gram language model, with feature weights tuned using MIRA BIBREF30 . Therefore, an appropriate additional baseline would be to mark translation rules with these indicator functions but without the scores, akin to identifying rules with phrases in them (Baseline + SegOn). 
 Question: Which translation systems do they compare against?

SOLUTION: hierarchical phrase-based system BIBREF29 appropriate additional baseline would be to mark translation rules with these indicator functions but without the scores, akin to identifying rules with phrases in them (Baseline + SegOn)

PROBLEM: 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?

SOLUTION: MLE SeqGAN LeakGAN MaliGAN IRL RAML DialogGAN DPGAN

PROBLEM: We apply attention probes to the task of identifying the existence and type of dependency relation between two words. Our first experiment is an exploratory visualization of how word sense affects context embeddings.  
 Question: What linguistic features were probed for?

SOLUTION:
dependency relation between two words word sense