class_docstr: A class to read TED talk dataset
      
class_docstr_tok: ["A","class","to","read","TED","talk","dataset"]
code: def filter_text(self, dict_):
      if self.target_token:
          field_index = 1
      else:
          field_index = 0
      data_dict = defaultdict(list)
      list1 = dict_["source"]
      list2 = dict_["target"]
      for sent1, sent2 in zip(list1, list2):
          try:
              src_sent = " ".join(sent1.split()[field_index:])
          except IndexError:
              src_sent = "NULL"
  
          if (
              src_sent.find(self.empty_line_flag) != -1
              or len(src_sent) == 0
          ):
              continue
  
          elif (
              sent2.find(self.empty_line_flag) != -1 or len(sent2) == 0
          ):
              continue
  
          else:
              data_dict["source"].append(sent1)
              data_dict["target"].append(sent2)
      return data_dict
  
imports: import itertools
  import os
  import csv
  from collections import defaultdict
  from six.moves import zip
  import io
  import wget
  import sys
  from subprocess import check_call, check_output
label_desc: Use image node_id 3 for calling the MultiLingualAlignedCorpusReader obj's underlying member method code with example usage: obj.filter_text(dict_) and returns: data_dict