Task: Question Answering
Input (English): We will compare to the more recent cross-lingual language model XLM BIBREF12, as well as the state-of-the-art CoNLL 2018 shared task results with predicted tokenisation and segmentation in an updated version of the paper. In French, no extensive work has been done due to the limited availability of NER corpora. We compare our model with the strong baselines settled by BIBREF49, who trained both CRF and BiLSTM-CRF architectures on the FTB and enhanced them using heuristics and pretrained word embeddings. In the TRANSLATE-TRAIN setting, we report the best scores from previous literature along with ours. BiLSTM-max is the best model in the original XNLI paper, mBERT which has been reported in French in BIBREF52 and XLM (MLM+TLM) is the best-presented model from BIBREF50. 
 Question: What is the state of the art?
Output (English): POS and DP task: CONLL 2018
NER task: (no extensive work) Strong baselines CRF and BiLSTM-CRF
NLI task: mBERT or XLM (not clear from text)