Question: what are the state-of-the-art models?

Evidence:
raw_evidence: For comparison, we adopt and re-implement three kinds of baselines as follows:
We implement an SVM with lexical features in accordance with previous works BIBREF16 , BIBREF17 , BIBREF1 , BIBREF15 , BIBREF4 and select the best feature set on the development set.
We implement and fine-tune a series of neural text classifiers, including attention-based method BIBREF3 and other methods we deem important. CNN BIBREF18 and GRU BIBREF27 , BIBREF21 take as input the concatenation of fact description and plea. Similarly, CNN/GRU+law refers to using the concatenation of fact description, plea and law articles as inputs.
We implement and train some off-the-shelf RC models, including r-net BIBREF5 and AoA BIBREF6 , which are the leading models on SQuAD leaderboard. In our initial experiments, these models take fact description as passage and plea as query. Further, Law articles are added to the fact description as a part of the reading materials, which is a simple way to consider them as well.
FLOAT SELECTED: Table 1: Experimental results(%). P/R/F1 are reported for positive samples and calculated as the mean score over 10-time experiments. Acc is defined as the proportion of test samples classified correctly, equal to micro-precision. MaxFreq refers to always predicting the most frequent label, i.e. support in our dataset. * indicates methods proposed in previous works.
raw_evidence: For comparison, we adopt and re-implement three kinds of baselines as follows:
We implement an SVM with lexical features in accordance with previous works BIBREF16 , BIBREF17 , BIBREF1 , BIBREF15 , BIBREF4 and select the best feature set on the development set.
We implement and fine-tune a series of neural text classifiers, including attention-based method BIBREF3 and other methods we deem important. CNN BIBREF18 and GRU BIBREF27 , BIBREF21 take as input the concatenation of fact description and plea. Similarly, CNN/GRU+law refers to using the concatenation of fact description, plea and law articles as inputs.
We implement and train some off-the-shelf RC models, including r-net BIBREF5 and AoA BIBREF6 , which are the leading models on SQuAD leaderboard. In our initial experiments, these models take fact description as passage and plea as query. Further, Law articles are added to the fact description as a part of the reading materials, which is a simple way to consider them as well.
In this paper, we explore the task of predicting judgments of civil cases. Comparing with conventional text classification framework, we propose Legal Reading Comprehension framework to handle multiple and complex textual inputs. Moreover, we present a novel neural model, AutoJudge, to incorporate law articles for judgment prediction. In experiments, we compare our model on divorce proceedings with various state-of-the-art baselines of various frameworks. Experimental results show that our model achieves considerable improvement than all the baselines. Besides, visualization results also demonstrate the effectiveness and interpretability of our proposed model.

Answers:
SVM , CNN , GRU , CNN/GRU+law, r-net , AoA 
SVM with lexical features in accordance with previous works BIBREF16 , BIBREF17 , BIBREF1 , BIBREF15 , BIBREF4, attention-based method BIBREF3 and other methods we deem important, some off-the-shelf RC models, including r-net BIBREF5 and AoA BIBREF6 , which are the leading models on SQuAD leaderboard