Bug in Sentence Offset

[gunturbudi]

Hello, I want to report a bug. I have a Sentence like this:

As a administrator, I want to refund sponsorship money that was processed via stripe, so that people get their monies back.

When I try to convert it to CoNLL the span is not converted well. I then debug the library and found that the offset is wrong. Here is the output of the offset:

As 0
a 3
administrator, 3
I 20
want 22
to 25
refund 30
sponsorship 37
money 49
that 55
was 60
processed 64
via 74
stripe, 78
so 86
that 89
people 94
get 101
their 103
monies 111
back. 118

As you can see, in the second and third lines, the offset is the same (3 and 3, while It should be 3 and 5). This behavior makes the span undetected in the conversion process.

It seems that the get_offsets function in utils.py checks the equality in the sequence of characters to decide about the offsets.

def get_offsets(
        text: str,
        tokens: List[str],
        start: Optional[int] = 0) -> List[int]:
    """Calculate char offsets of each tokens.

    Args:
        text (str): The string before tokenized.
        tokens (List[str]): The list of the string. Each string corresponds
            token.
        start (Optional[int]): The start position.
    Returns:
        (List[str]): The list of the offset.
    """
    offsets = []
    i = 0
    for token in tokens:
        for j, char in enumerate(token):
            while char != text[i]:
                i += 1

            if j == 0:
                offsets.append(i + start)
    return offsets

It will be a problem if the last character of the previous word is the same as the first character of the next word. I'm still looking for a fix to this problem.

Cheers

[gunturbudi]

I manage to solve the problems with this modification in utils.py. It basically check what I mentioned in the above issues, if the last character of the previous word is the same as the first character of the next word.

def get_offsets(
        text: str,
        tokens: List[str],
        start: Optional[int] = 0) -> List[int]:

    """Calculate char offsets of each tokens.

    Args:
        text (str): The string before tokenized.
        tokens (List[str]): The list of the string. Each string corresponds
            token.
        start (Optional[int]): The start position.
    Returns:
        (List[str]): The list of the offset.
    """

    offsets = []
    i = 0

    same_char = False

    for k, token in enumerate(tokens):
        if token[0] == tokens[k-1][-1]:
            same_char = True
        else:
            same_char = False

        for j, char in enumerate(token):
            while char != text[i] or same_char:
                i += 1
                same_char = False

            if j == 0:
                offsets.append(i + start)

    return offsets

I don't know if it's the best solution. But it works for me, and luckily my NER model improves :)

Regards