Machine Reading Comprehension (MRC) Processor
import pdb
import json
import logging
from tqdm import tqdm
from .base_processor import (
EAEDataProcessor,
EAEInputExample,
EAEInputFeatures
)
from .mrc_converter import read_query_templates
from .input_utils import get_words, get_left_and_right_pos
from collections import defaultdict
logger = logging.getLogger(__name__)
EAEMRCProcessor
Data processor for Machine Reading Comprehension (MRC) for event argument extraction. The class is inherited from
the EAEDataProcessor class, in which the undefined functions, including read_examples() and
convert_examples_to_features() are implemented; a new function entitled remove_sub_word() is defined to remove
the annotations whose word is a sub-word, the rest of the attributes and functions are multiplexed from the
EAEDataProcessor class.
class EAEMRCProcessor(EAEDataProcessor):
"""Data processor for Machine Reading Comprehension (MRC) for event argument extraction.
Data processor for Machine Reading Comprehension (MRC) for event argument extraction. The class is inherited from
the `EAEDataProcessor` class, in which the undefined functions, including `read_examples()` and
`convert_examples_to_features()` are implemented; a new function entitled `remove_sub_word()` is defined to remove
the annotations whose word is a sub-word, the rest of the attributes and functions are multiplexed from the
`EAEDataProcessor` class.
"""
def __init__(self,
config,
tokenizer,
input_file: str,
pred_file: str,
is_training: bool = False) -> None:
"""Constructs a `EAEMRCProcessor`."""
super().__init__(config, tokenizer, pred_file, is_training)
self.read_examples(input_file)
self.convert_examples_to_features()
def read_examples(self,
input_file: str) -> None:
"""Obtains a collection of `EAEInputExample`s for the dataset."""
self.examples = []
self.data_for_evaluation["golden_arguments"] = []
trigger_idx = 0
query_templates = read_query_templates(self.config.prompt_file,
translate=self.config.dataset_name == "ACE2005-ZH")
template_id = self.config.mrc_template_id
with open(input_file, "r", encoding="utf-8") as f:
for idx, line in enumerate(tqdm(f.readlines(), desc="Reading from %s" % input_file)):
item = json.loads(line.strip())
if "events" in item:
words = get_words(text=item["text"], language=self.config.language)
for event in item["events"]:
for trigger in event["triggers"]:
if self.is_training or self.config.golden_trigger or self.event_preds is None:
pred_event_type = event["type"]
else:
pred_event_type = self.event_preds[trigger_idx]
trigger_idx += 1
# Evaluation mode for EAE
# If predicted event type is NA:
# in [default] and [loose] modes, we don't consider the trigger
# in [strict] mode, we consider the trigger
if self.config.eae_eval_mode in ["default", "loose"] and pred_event_type == "NA":
continue
# golden label for the trigger
arguments_per_trigger = dict(id=trigger_idx-1,
arguments=[],
pred_type=pred_event_type,
true_type=event["type"])
for argument in trigger["arguments"]:
arguments_per_role = dict(role=argument["role"], mentions=[])
for mention in argument["mentions"]:
left_pos, right_pos = get_left_and_right_pos(text=item["text"],
trigger=mention,
language=self.config.language)
arguments_per_role["mentions"].append({
"position": [left_pos, right_pos - 1]
})
arguments_per_trigger["arguments"].append(arguments_per_role)
self.data_for_evaluation["golden_arguments"].append(arguments_per_trigger)
if pred_event_type == "NA":
assert self.config.eae_eval_mode == "strict"
# in strict mode, we add the gold args for the trigger but do not make predictions
continue
trigger_left, trigger_right = get_left_and_right_pos(text=item["text"],
trigger=trigger,
language=self.config.language)
for role in query_templates[pred_event_type].keys():
query = query_templates[pred_event_type][role][template_id]
query = query.replace("[trigger]", self.tokenizer.tokenize(trigger["trigger_word"])[0])
query = get_words(text=query, language=self.config.language)
if self.is_training:
no_answer = True
for argument in trigger["arguments"]:
if argument["role"] not in query_templates[pred_event_type]:
# raise ValueError(
# "No template for %s in %s" % (argument["role"], pred_event_type)
# )
logger.warning(
"No template for %s in %s" % (argument["role"], pred_event_type))
pass
if argument["role"] != role:
continue
no_answer = False
for mention in argument["mentions"]:
left_pos, right_pos = get_left_and_right_pos(text=item["text"],
trigger=mention,
language=self.config.language)
example = EAEInputExample(
example_id=trigger_idx-1,
text=words,
pred_type=pred_event_type,
true_type=event["type"],
input_template=query,
trigger_left=trigger_left,
trigger_right=trigger_right,
argument_left=left_pos,
argument_right=right_pos - 1,
argument_role=role
)
self.examples.append(example)
if no_answer:
example = EAEInputExample(
example_id=trigger_idx-1,
text=words,
pred_type=pred_event_type,
true_type=event["type"],
input_template=query,
trigger_left=trigger_left,
trigger_right=trigger_right,
argument_left=-1,
argument_right=-1,
argument_role=role
)
self.examples.append(example)
else:
# one instance per query
example = EAEInputExample(
example_id=trigger_idx-1,
text=words,
pred_type=pred_event_type,
true_type=event["type"],
input_template=query,
trigger_left=trigger_left,
trigger_right=trigger_right,
argument_left=-1,
argument_right=-1,
argument_role=role
)
self.examples.append(example)
# negative triggers
for neg_trigger in item["negative_triggers"]:
if self.is_training or self.config.golden_trigger or self.event_preds is None:
pred_event_type = "NA"
else:
pred_event_type = self.event_preds[trigger_idx]
trigger_idx += 1
if self.config.eae_eval_mode == "loose":
continue
elif self.config.eae_eval_mode in ["default", "strict"]:
if pred_event_type == "NA":
continue
trigger_left, trigger_right = get_left_and_right_pos(text=item["text"],
trigger=neg_trigger,
language=self.config.language)
for role in query_templates[pred_event_type].keys():
query = query_templates[pred_event_type][role][template_id]
query = query.replace("[trigger]",
self.tokenizer.tokenize(neg_trigger["trigger_word"])[0])
query = get_words(text=query, language=self.config.language)
# one instance per query
example = EAEInputExample(
example_id=trigger_idx-1,
text=words,
pred_type=pred_event_type,
true_type="NA",
input_template=query,
trigger_left=trigger_left,
trigger_right=trigger_right,
argument_left=-1,
argument_right=-1,
argument_role=role
)
self.examples.append(example)
else:
raise ValueError("Invalid eae_eval_mode: %s" % self.config.eae_eval_mode)
else:
for candi in item["candidates"]:
trigger_left, trigger_right = get_left_and_right_pos(text=item["text"],
trigger=candi,
language=self.config.language)
pred_event_type = self.event_preds[trigger_idx]
trigger_idx += 1
if pred_event_type != "NA":
for role in query_templates[pred_event_type].keys():
query = query_templates[pred_event_type][role][template_id]
query = query.replace("[trigger]", self.tokenizer.tokenize(candi["trigger_word"])[0])
query = get_words(text=query, language=self.config.language)
# one instance per query
example = EAEInputExample(
example_id=trigger_idx-1,
text=words,
pred_type=pred_event_type,
true_type="NA",
input_template=query,
trigger_left=trigger_left,
trigger_right=trigger_right,
argument_left=-1,
argument_right=-1,
argument_role=role
)
self.examples.append(example)
if self.event_preds is not None:
assert trigger_idx == len(self.event_preds)
def convert_examples_to_features(self) -> None:
"""Converts the `EAEInputExample`s into `EAEInputFeatures`s."""
self.input_features = []
self.data_for_evaluation["text_range"] = []
self.data_for_evaluation["text"] = []
for example in tqdm(self.examples, desc="Processing features for MRC"):
# context
input_context = self.tokenizer(example.text,
truncation=True,
max_length=self.config.max_seq_length,
is_split_into_words=True)
# template
input_template = self.tokenizer(example.input_template,
truncation=True,
padding="max_length",
max_length=self.config.max_seq_length,
is_split_into_words=True)
input_context = self.remove_sub_word(input_context)
# concatenate
input_ids = input_context["input_ids"] + input_template["input_ids"]
attention_mask = input_context["attention_mask"] + input_template["attention_mask"]
token_type_ids = [0] * len(input_context["input_ids"]) + [1] * len(input_template["input_ids"])
# truncation
input_ids = input_ids[:self.config.max_seq_length]
attention_mask = attention_mask[:self.config.max_seq_length]
token_type_ids = token_type_ids[:self.config.max_seq_length]
# output labels
start_position = 0 if example.argument_left == -1 else example.argument_left + 1
end_position = 0 if example.argument_right == -1 else example.argument_right + 1
# data for evaluation
text_range = dict()
text_range["start"] = 1
text_range["end"] = text_range["start"] + sum(input_context["attention_mask"][1:])
self.data_for_evaluation["text_range"].append(text_range)
self.data_for_evaluation["text"].append(example.text)
# features
features = EAEInputFeatures(
example_id=example.example_id,
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
argument_left=start_position,
argument_right=end_position
)
self.input_features.append(features)
@staticmethod
def remove_sub_word(inputs):
"""Removes the annotations whose word is a sub-word."""
outputs = defaultdict(list)
pre_word_id = -1
for token_id, word_id in enumerate(inputs.word_ids()):
if token_id == 0 or (word_id != pre_word_id and word_id is not None):
for key in inputs:
outputs[key].append(inputs[key][token_id])
pre_word_id = word_id
return outputs