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: We use OpenNMT BIBREF24 as the implementation of the NMT system for all experiments BIBREF5 . PBMT-R is a phrase-based method with a reranking post-processing step BIBREF18 . Hybrid performs sentence splitting and deletion operations based on discourse representation structures, and then simplifies sentences with PBMT-R BIBREF25 . SBMT-SARI BIBREF19 is syntax-based translation model using PPDB paraphrase database BIBREF26 and modifies tuning function (using SARI). Dress is an encoder-decoder model coupled with a deep reinforcement learning framework, and the parameters are chosen according to the original paper BIBREF20 . 
 Question: what state of the art methods did they compare with?

SOLUTION: OpenNMT PBMT-R Hybrid SBMT-SARI Dress

PROBLEM: While manually analyzing the raw dataset, we noticed that looking at the tweet one has replied to or has quoted, provides significant contextual information. We call these, “context tweets". As humans can better understand a tweet with the reference of its context, our assumption is that computers also benefit from taking context tweets into account in detecting abusive language.

As shown in the examples below, (2) is labeled abusive due to the use of vulgar language. However, the intention of the user can be better understood with its context tweet (1).

(1) I hate when I'm sitting in front of the bus and somebody with a wheelchair get on.

INLINEFORM0 (2) I hate it when I'm trying to board a bus and there's already an as**ole on it.

Similarly, context tweet (3) is important in understanding the abusive tweet (4), especially in identifying the target of the malice.

(3) Survivors of #Syria Gas Attack Recount `a Cruel Scene'.

INLINEFORM0 (4) Who the HELL is “LIKE" ING this post? Sick people....

Huang et al. huang2016modeling used several attributes of context tweets for sentiment analysis in order to improve the baseline LSTM model. However, their approach was limited because the meta-information they focused on—author information, conversation type, use of the same hashtags or emojis—are all highly dependent on data.

In order to avoid data dependency, text sequences of context tweets are directly used as an additional feature of neural network models. We use the same baseline model to convert context tweets to vectors, then concatenate these vectors with outputs of their corresponding labeled tweets. More specifically, we concatenate max-pooled layers of context and labeled tweets for the CNN baseline model. As for RNN, the last hidden states of context and labeled tweets are concatenated. 
 Question: What additional features and context are proposed?

SOLUTION: using tweets that one has replied or quoted to as contextual information

PROBLEM: We evaluate the proposed architecture on two publicly available datasets: the Adverse Drug Events (ADE) dataset BIBREF6 and the CoNLL04 dataset BIBREF7. We show that our architecture is able to outperform the current state-of-the-art (SOTA) results on both the NER and RE tasks in the case of ADE. In the case of CoNLL04, our proposed architecture achieves SOTA performance on the NER task and achieves near SOTA performance on the RE task. On both datasets, our results are SOTA when averaging performance across both tasks. 
 Question: Do they repot results only on English data?

SOLUTION:
Yes