File size: 4,048 Bytes
94011a1 |
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 |
# get_constituency_parses.py
# Author: Julie Kallini
# For importing utils
import sys
sys.path.append("..")
import os
import argparse
import stanza
import json
import tqdm
import numpy as np
from utils import PERTURBATIONS, write_file, merge_part_tokens, \
BOS_TOKEN, MARKER_REV, BABYLM_DATA_PATH
from glob import glob
def __get_constituency_parse(sent, nlp, perturbation_class):
try:
parse_doc = nlp(sent)
parsed_sent = parse_doc.sentences[0]
if perturbation_class == "reverse":
new_sent = sent
elif perturbation_class == "hop":
words = [w.text for w in parsed_sent.words]
new_sent = " ".join(merge_part_tokens(words))
else:
raise Exception("Perturbation class is not implemented")
return str(parsed_sent.constituency), new_sent
except:
return None, None
if __name__ == "__main__":
parser = argparse.ArgumentParser(
prog='Parse BabyLM test data',
description='Get constituency parses of BabyLM test data for probing experiments')
parser.add_argument('perturbation_type',
default='all',
const='all',
nargs='?',
choices=PERTURBATIONS.keys(),
help='Perturbation function used to transform BabyLM dataset')
# Get args
args = parser.parse_args()
# Get class of perturbations
perturbation_class = None
if "reverse" in args.perturbation_type:
perturbation_class = "reverse"
elif "hop" in args.perturbation_type:
perturbation_class = "hop"
else:
raise Exception("Perturbation class not implemented")
# Get all relevant test sentences
test_sentences = []
print("Getting sentences to parse...")
if perturbation_class == "reverse":
# For reversal, load original test sentences
babylm_data = glob(f"{BABYLM_DATA_PATH}/babylm_data/babylm_test/*.json")
for file in babylm_data:
if "_parsed" in file:
continue
print(file)
f = open(file)
data = json.load(f)
f.close()
# Get untagged test sentences
for line in tqdm.tqdm(data):
for sent in line["sent_annotations"]:
test_sentences.append(sent["sent_text"])
else:
# For other perturbations, get unaffected test sentences
babylm_data = glob(
f"{BABYLM_DATA_PATH}/babylm_data_perturbed/babylm_{args.perturbation_type}/babylm_test_unaffected_sents/*")
for file in babylm_data:
print(file)
f = open(file)
data = f.readlines()
f.close()
test_sentences.extend([line.strip() for line in data])
# Remove short sentences
MIN_SENTENCE_LEN = 50
test_sentences = [sent for sent in test_sentences if len(
sent) >= MIN_SENTENCE_LEN]
# Init rng for sampling
rng = np.random.default_rng(seed=15)
N = len(test_sentences) if len(test_sentences) < 50000 else 50000
test_sentences = rng.choice(test_sentences, size=N, replace=False)
# Init Stanza NLP tools
nlp = stanza.Pipeline(lang='en',
processors='tokenize,pos,constituency',
package="default_accurate",
use_gpu=True)
# Get constituency parses
parse_data = []
for sent in tqdm.tqdm(test_sentences):
constituency_parse, new_sent = __get_constituency_parse(
sent, nlp, perturbation_class)
if constituency_parse is not None:
parse_data.append(new_sent + "\n")
parse_data.append(constituency_parse + "\n")
# Create directory
parses_directory = f"test_constituency_parses/"
if not os.path.exists(parses_directory):
os.makedirs(parses_directory)
parses_file = f"{perturbation_class}_parses.test"
# Write files
write_file(parses_directory, parses_file, parse_data)
|