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Change context in QA formatted preds to not split words #138

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Nov 11, 2019
Merged

Change context in QA formatted preds to not split words #138

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65 changes: 40 additions & 25 deletions farm/modeling/prediction_head.py
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Original file line number Diff line number Diff line change
Expand Up @@ -714,7 +714,8 @@ def __init__(self,
task_name="question_answering",
no_answer_shift=0,
top_n_predictions=3,
context_size=100,
context_size=20,
max_ans_len=30,
**kwargs):
"""
:param layer_dims: dimensions of Feed Forward block, e.g. [768,2], for adjusting to BERT embedding. Output should be always 2
Expand All @@ -727,8 +728,8 @@ def __init__(self,
:type no_answer_shift: int
:param top_n_predictions: When we split a document into multiple passages we can return top n passage answers
:type top_n_predictions: int
:param context_size: When we format predictions back to string space we also return surrounding context
of size context_size
:param context_size: When we format predictions back to string space we also return "context_size" surrounding
number of subword tokens (it starts and ends on normal word boundaries though)
:type context_size: int
:param kwargs: placeholder for passing generic parameters
:type kwargs: object
Expand All @@ -743,10 +744,11 @@ def __init__(self,
) # predicts start and end token of answer
self.task_name = task_name
self.no_answer_shift = no_answer_shift # how much we want to upweight no answer logit scores compared to text answer ones
self.top_n_predictions = top_n_predictions #for how many passages we want to get predictions
self.context_size = context_size
self.max_ans_len = 1000 # disabling max ans len. Impact on squad performance seems minor
self.top_n_predictions = top_n_predictions
# each answer is returned with surrounding context. In # characters surrounding the answer
self.context_size = context_size
self.max_ans_len = max_ans_len

self.generate_config()


Expand Down Expand Up @@ -857,7 +859,7 @@ def logits_to_preds(self, logits, **kwargs):
best_answer_sum = np.zeros(num_per_batch)
# check if start or end point to the context. Context starts at segment id == 1 (question comes before at segment ids == 0)
context_start = np.argmax(segment_ids,axis=1)
context_end = segment_ids.shape[1] - np.argmax(segment_ids[::-1],axis=1)
context_end = segment_ids.shape[1] - np.argmax(segment_ids[:,::-1],axis=1)
start_proposals = self._get_best_textanswer_indices(start_logits, 3)
end_proposals = self._get_best_textanswer_indices(end_logits, 3)
best_indices = np.zeros((num_per_batch,2),dtype=int)
Expand Down Expand Up @@ -959,38 +961,51 @@ def formatted_preds(self, logits, preds, samples):
passage_pred["probability"] = -1 # TODO add probabilities that make sense : )
try:
#default to returning no answer
start = 0
end = 0
context_start = 0
start_char = 0
end_char = 0
context_start_char = 0
answer = ""
context = ""
if(s_i + e_i > 0):
current_start = int(s_i + passage_shift_i - question_shift_i)
current_end = int(e_i + passage_shift_i - question_shift_i) + 1
start_token = int(s_i + passage_shift_i - question_shift_i)
end_token = int(e_i + passage_shift_i - question_shift_i) + 1
temptext = " ".join(current_sample.clear_text["doc_tokens"])
start = current_sample.tokenized["offsets"][current_start]
start_char = current_sample.tokenized["offsets"][start_token]
# if the last end token is predicted we cannot take the char offset of the following word
if current_end >= len(current_sample.tokenized["offsets"]):
end = len(temptext)
if end_token >= len(current_sample.tokenized["offsets"]):
end_char = len(temptext)
else:
end = current_sample.tokenized["offsets"][current_end]
end_char = current_sample.tokenized["offsets"][end_token]
# we want the answer in original string space (containing newline, tab or multiple
# whitespace. So we need to join doc tokens and work with character offsets
answer = temptext[start:end]
answer = temptext[start_char:end_char]
answer = answer.strip()
# sometimes we strip trailing whitespaces, so we need to adjust end
end = start + len(answer)
context_start = int(np.clip((start-self.context_size),a_min=0,a_max=None))
context_end = int(np.clip(end +self.context_size,a_max=len(temptext),a_min=None))
context = temptext[context_start:context_end]
end_char = start_char + len(answer)
context_start_token = int(np.clip((start_token - self.context_size),a_min=0,a_max=None))
while not current_sample.tokenized["start_of_word"][context_start_token]:
context_start_token -= 1
context_start_char = current_sample.tokenized["offsets"][context_start_token]
context_end_token = int(np.clip(end_token + self.context_size,
a_min=None,
a_max=len(current_sample.tokenized["offsets"]) -1))
while not current_sample.tokenized["start_of_word"][context_end_token]:
context_end_token += 1
if context_end_token >= len(current_sample.tokenized["offsets"]):
break
if context_end_token >= len(current_sample.tokenized["offsets"]):
context_end_char = len(temptext)
else:
context_end_char = current_sample.tokenized["offsets"][context_end_token]
context = temptext[context_start_char:context_end_char]
except IndexError as e:
logger.info(e)
passage_pred["answer"] = answer
passage_pred["offset_start"] = start
passage_pred["offset_end"] = end
passage_pred["offset_start"] = start_char
passage_pred["offset_end"] = end_char
passage_pred["context"] = context
passage_pred["offset_context_start"] = start - context_start
passage_pred["offset_context_end"] = end - context_start
passage_pred["offset_context_start"] = context_start_char
passage_pred["offset_context_end"] = context_end_char
passage_pred["document_id"] = current_sample.clear_text.get("document_id", None)
passage_predictions.append(passage_pred)

Expand Down