"""Script used to filter malformed examples from the original SCAT corpus. To run, copy original SCAT files from https://github.com/neulab/contextual-mt/tree/master/data/scat under the same path of the script. Filtered files will be created in the filtered_scat folder. Uncomment lines to save dropped malformed sentences into separate files for inspection. """ import re from pathlib import Path def drop_malformed_tags( split: str, save_folder: str = "filtered_scat", ): find_tag_pattern = r"(|<\/?p>|)" nested_uninformative_pattern = r"(\W*(

[^<]*

)\W*)" with open(f"highlighted.{split}.context.en") as f: orig_ctx_en = f.readlines() with open(f"highlighted.{split}.context.fr") as f: orig_ctx_fr = f.readlines() with open(f"highlighted.{split}.en") as f: orig_tgt_en = f.readlines() with open(f"highlighted.{split}.fr") as f: orig_tgt_fr = f.readlines() print("# of context examples: EN -", len(orig_ctx_en), "FR -", len(orig_ctx_fr)) print("# of target examples: EN -", len(orig_tgt_en), "FR -", len(orig_tgt_fr)) ctx_en = [] ctx_fr = [] tgt_en = [] tgt_fr = [] #drop_ctx_en = [] #drop_ctx_fr = [] #drop_tgt_en = [] #drop_tgt_fr = [] for ex_idx in range(len(orig_ctx_en)): drop = False txt_list = [orig_ctx_en[ex_idx], orig_tgt_en[ex_idx], orig_ctx_fr[ex_idx], orig_tgt_fr[ex_idx]] if not ( "

" in txt_list[1] and "

" in txt_list[1] and "

" in txt_list[3] and "

" in txt_list[3] and "

" not in txt_list[0] and "

" not in txt_list[0] and "

" not in txt_list[2] and "

" not in txt_list[2] ): drop = True # Nested tags like

it

are uninformative and simply mean the supporting context wasn't found # in the source. We replace them with the inner tag

it

so that the tag is dropped for the next step. for i in range(len(txt_list)): for uninformative_match, nested_tag in re.findall(nested_uninformative_pattern, txt_list[i]): txt_list[i] = txt_list[i].replace(uninformative_match, nested_tag) txt = " ".join(txt_list) matches = [(m.group(0),) + m.span() for m in re.finditer(find_tag_pattern, txt)] if not drop: if len(matches) > 0 and len(matches) % 2 == 0: for match_idx in range(0, len(matches), 2): # The last condition is added to drop malformed examples in which all spans matching # the target pronoun have been tagged with

...

# e.g. "Well, you're certainly not talking about the algor

it

hm project, because

it

would # be unthinkable for you to derail

it

at this point." if not ( (matches[match_idx][0] == "" and matches[match_idx+1][0] == "") or (matches[match_idx][0] == "

" and matches[match_idx+1][0] == "

") or (matches[match_idx][2] < matches[match_idx+1][1]) ): drop = True break else: drop = True if not drop: ctx_en.append(txt_list[0]) ctx_fr.append(txt_list[2]) tgt_en.append(txt_list[1]) tgt_fr.append(txt_list[3]) #else: # drop_ctx_en.append(txt_list[0]) # drop_ctx_fr.append(txt_list[2]) # drop_tgt_en.append(txt_list[1]) # drop_tgt_fr.append(txt_list[3]) # print("Dropped example:", txt) print("# of dropped examples:", len(orig_ctx_en) - len(ctx_en)) print("# of filtered examples:", len(ctx_en)) save_folder = Path(save_folder) save_folder.mkdir(parents=True, exist_ok=True) with open(save_folder / f"filtered.{split}.context.en", "w") as f: f.writelines(ctx_en) with open(save_folder / f"filtered.{split}.context.fr", "w") as f: f.writelines(ctx_fr) with open(save_folder / f"filtered.{split}.en", "w") as f: f.writelines(tgt_en) with open(save_folder / f"filtered.{split}.fr", "w") as f: f.writelines(tgt_fr) #with open(save_folder / f"dropped.{split}.context.en", "w") as f: # f.writelines(drop_ctx_en) #with open(save_folder / f"dropped.{split}.context.fr", "w") as f: # f.writelines(drop_ctx_fr) #with open(save_folder / f"dropped.{split}.en", "w") as f: # f.writelines(drop_tgt_en) #with open(save_folder / f"dropped.{split}.fr", "w") as f: # f.writelines(drop_tgt_fr) print("Files written to the filtered_scat folder") if __name__ == "__main__": drop_malformed_tags("train") drop_malformed_tags("valid") drop_malformed_tags("test")