def test_textual_entailment(self):
predictor = pretrained.decomposable_attention_with_elmo_parikh_2017()
result = predictor.predict_json({
"premise":
"An interplanetary spacecraft is in orbit around a gas giant's icy moon.",
"hypothesis":
"The spacecraft has the ability to travel between planets."
})
assert result["label_probs"][0] > 0.7 # entailment
result = predictor.predict_json({
"premise":
"Two women are wandering along the shore drinking iced tea.",
"hypothesis":
"Two women are sitting on a blanket near some rocks talking about politics."
})
assert result["label_probs"][1] > 0.8 # contradiction
result = predictor.predict_json({
"premise":
"A large, gray elephant walked beside a herd of zebras.",
"hypothesis": "The elephant was lost."
})
assert result["label_probs"][2] > 0.6 # neutral
def test_textual_entailment(self):
predictor = pretrained.decomposable_attention_with_elmo_parikh_2017()
result = predictor.predict_json({
"premise": "An interplanetary spacecraft is in orbit around a gas giant's icy moon.",
"hypothesis": "The spacecraft has the ability to travel between planets."
})
assert result["label_probs"][0] > 0.7 # entailment
result = predictor.predict_json({
"premise": "Two women are wandering along the shore drinking iced tea.",
"hypothesis": "Two women are sitting on a blanket near some rocks talking about politics."
})
assert result["label_probs"][1] > 0.8 # contradiction
result = predictor.predict_json({
"premise": "A large, gray elephant walked beside a herd of zebras.",
"hypothesis": "The elephant was lost."
})
assert result["label_probs"][2] > 0.6 # neutral
def IR(sent_select_method):
if sent_select_method == 'esim':
esim = pretrained.esim_nli_with_elmo_chen_2017()
elif sent_select_method == 'entail':
attention = pretrained.decomposable_attention_with_elmo_parikh_2017()
else:
print("loading embedding...")
nlp = spacy.load('en_vectors_web_lg') # 300-dim GloVe vectors
# TODO: change num_vector?
print("finished loading embedding...")
titles_dict = xdb_query.load_xapian_titles(Args.OBJECTS, Args.TITLES)
predictor = get_constituency_parser()
with open(DATA_SET, 'r') as data_set_f:
data_set = json.load(data_set_f)
num_sents = 1
print("num sents selected", num_sents)
output_content = {}
for id_, record in tqdm(data_set.items()):
parse_result = predictor.predict_json({"sentence": record['claim']})
NPs = get_constituency_parsing_NPs(parse_result, NPs=set())
NPs = get_customised_NPs(parse_result, NPs=NPs)
# doc_ids = NPs2titles(NPs, titles_dict)
if Args.LOG_MISSING_DOCS:
evidence = list(map(lambda x: x[0], record['evidence']))
matched_titles, missing = result_stat(evidence, NPs)
log_missing(missing, record, NPs, parse_result)
# Sentence selection
# if sent_select_method == 'esim':
# sents = sent_selection_esim(esim,
# record['claim'],
# doc_ids,
# Args.DB_PATH,
# num_sents)
# elif sent_select_method == 'entail': # texual entailment
# # not very well
# sents = sent_selection_entail(attention,
# record['claim'],
# doc_ids,
# Args.DB_PATH,
# num_sents)
# else: # similarity
# sents = sent_selection_sim(nlp,
# record['claim'],
# doc_ids,
# Args.DB_PATH,
# num_sents) # 1 sent is the best
# record['evidence'] = sents
t = [NP for NP in NPs if NP in titles_dict]
# randomly select 'sentence 0' from each doc to do doc retrieval
record['evidence'] = [[title, 0] for title in t]
output_content[id_] = record
return output_content