File size: 5,659 Bytes
a687d50
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
89073fb
a687d50
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d50dd92
 
 
 
a687d50
 
 
 
d50dd92
 
 
 
 
 
 
 
 
928a58b
 
 
 
d50dd92
a687d50
 
 
 
 
d50dd92
a687d50
 
 
928a58b
 
 
 
a687d50
928a58b
 
 
 
 
 
 
 
 
a687d50
ad1c8ed
 
 
 
d50dd92
 
ad1c8ed
 
 
 
d50dd92
a687d50
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d50dd92
 
 
 
 
 
 
 
a687d50
 
 
d50dd92
a687d50
 
 
d50dd92
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
"""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"(<hon>|<\/?p>|<hoff>)"
    nested_uninformative_pattern = r"(<hon>\W*(<p>[^<]*</p>)\W*<hoff>)"
    embedded_tag = r"\s(\S+<p>[^<]*</p>\S*|\S*<p>[^<]*</p>\S+)\s"

    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]]

        # Drop malformed <p>...</p> tags in which random mid-word spans are tagged
        # e.g. "I bought a picture frame for my desk, and <p>it</p>'s just s<p>it</p>ting there, wa<p>it</p>ing for his face."
        for i in range(len(txt_list)):
            for embedded_tag_match in re.findall(embedded_tag, txt_list[i]):
                removed_tag = embedded_tag_match.replace("<p>", "").replace("</p>", "")
                txt_list[i] = txt_list[i].replace(embedded_tag_match, removed_tag, 1)    

        # <p>...</p> tags should only be present in the target text
        if not (
            "<p>" in txt_list[1] and "</p>" in txt_list[1] and "<p>" in txt_list[3] and "</p>" in txt_list[3] and
            "<p>" not in txt_list[0] and "</p>" not in txt_list[0] and "<p>" not in txt_list[2] and "</p>" not in txt_list[2]
        ):
            drop = True        

        # Nested tags like <hon><p>it</p><hoff> are uninformative and simply mean the supporting context wasn't found
        # in the source. We replace them with the inner tag <p>it</p> 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, 1)
        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):

                    if not (
                        (matches[match_idx][0] == "<hon>" and matches[match_idx+1][0] == "<hoff>") or
                        (matches[match_idx][0] == "<p>" and matches[match_idx+1][0] == "</p>") or
                        (matches[match_idx][2] < matches[match_idx+1][1])
                    ):
                        drop = True
                        break
            else:
                drop = True
        if not drop:
            ctx_en.append(re.sub(r"\u200e", "", re.sub(r"\u200b", "", txt_list[0].strip())) + "\n")
            ctx_fr.append(re.sub(r"\u200e", "", re.sub(r"\u200b", "", txt_list[2].strip())) + "\n")
            tgt_en.append(re.sub(r"\u200e", "", re.sub(r"\u200b", "", txt_list[1].strip())) + "\n")
            tgt_fr.append(re.sub(r"\u200e", "", re.sub(r"\u200b", "", txt_list[3].strip())) + "\n")
            
        else:
            drop_ctx_en.append(re.sub(r"\u200e", "", re.sub(r"\u200b", "", txt_list[0].strip())) + "\n")
            drop_ctx_fr.append(re.sub(r"\u200e", "", re.sub(r"\u200b", "", txt_list[2].strip())) + "\n")
            drop_tgt_en.append(re.sub(r"\u200e", "", re.sub(r"\u200b", "", txt_list[1].strip())) + "\n")
            drop_tgt_fr.append(re.sub(r"\u200e", "", re.sub(r"\u200b", "", txt_list[3].strip())) + "\n")
            #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")