16.4. Natural Language Inference and the Dataset¶ Open the notebook in SageMaker Studio Lab
In Section 16.1, we discussed the problem of sentiment analysis. This task aims to classify a single text sequence into predefined categories, such as a set of sentiment polarities. However, when there is a need to decide whether one sentence can be inferred form another, or eliminate redundancy by identifying sentences that are semantically equivalent, knowing how to classify one text sequence is insufficient. Instead, we need to be able to reason over pairs of text sequences.
16.4.1. Natural Language Inference¶
Natural language inference studies whether a hypothesis can be inferred from a premise, where both are a text sequence. In other words, natural language inference determines the logical relationship between a pair of text sequences. Such relationships usually fall into three types:
Entailment: the hypothesis can be inferred from the premise.
Contradiction: the negation of the hypothesis can be inferred from the premise.
Neutral: all the other cases.
Natural language inference is also known as the recognizing textual entailment task. For example, the following pair will be labeled as entailment because “showing affection” in the hypothesis can be inferred from “hugging one another” in the premise.
Premise: Two women are hugging each other.
Hypothesis: Two women are showing affection.
The following is an example of contradiction as “running the coding example” indicates “not sleeping” rather than “sleeping”.
Premise: A man is running the coding example from Dive into Deep Learning.
Hypothesis: The man is sleeping.
The third example shows a neutrality relationship because neither “famous” nor “not famous” can be inferred from the fact that “are performing for us”.
Premise: The musicians are performing for us.
Hypothesis: The musicians are famous.
Natural language inference has been a central topic for understanding natural language. It enjoys wide applications ranging from information retrieval to open-domain question answering. To study this problem, we will begin by investigating a popular natural language inference benchmark dataset.
16.4.2. The Stanford Natural Language Inference (SNLI) Dataset¶
Stanford Natural Language Inference (SNLI) Corpus is a collection of
over 500000 labeled English sentence pairs
(Bowman et al., 2015). We download and store the
extracted SNLI dataset in the path ../data/snli_1.0.
import os
import re
import torch
from torch import nn
from d2l import torch as d2l
#@save
d2l.DATA_HUB['SNLI'] = (
'https://nlp.stanford.edu/projects/snli/snli_1.0.zip',
'9fcde07509c7e87ec61c640c1b2753d9041758e4')
data_dir = d2l.download_extract('SNLI')
import os
import re
from mxnet import gluon, np, npx
from d2l import mxnet as d2l
npx.set_np()
#@save
d2l.DATA_HUB['SNLI'] = (
'https://nlp.stanford.edu/projects/snli/snli_1.0.zip',
'9fcde07509c7e87ec61c640c1b2753d9041758e4')
data_dir = d2l.download_extract('SNLI')
16.4.2.1. Reading the Dataset¶
The original SNLI dataset contains much richer information than what we
really need in our experiments. Thus, we define a function read_snli
to only extract part of the dataset, then return lists of premises,
hypotheses, and their labels.
#@save
def read_snli(data_dir, is_train):
"""Read the SNLI dataset into premises, hypotheses, and labels."""
def extract_text(s):
# Remove information that will not be used by us
s = re.sub('\\(', '', s)
s = re.sub('\\)', '', s)
# Substitute two or more consecutive whitespace with space
s = re.sub('\\s{2,}', ' ', s)
return s.strip()
label_set = {'entailment': 0, 'contradiction': 1, 'neutral': 2}
file_name = os.path.join(data_dir, 'snli_1.0_train.txt'
if is_train else 'snli_1.0_test.txt')
with open(file_name, 'r') as f:
rows = [row.split('\t') for row in f.readlines()[1:]]
premises = [extract_text(row[1]) for row in rows if row[0] in label_set]
hypotheses = [extract_text(row[2]) for row in rows if row[0] in label_set]
labels = [label_set[row[0]] for row in rows if row[0] in label_set]
return premises, hypotheses, labels
#@save
def read_snli(data_dir, is_train):
"""Read the SNLI dataset into premises, hypotheses, and labels."""
def extract_text(s):
# Remove information that will not be used by us
s = re.sub('\\(', '', s)
s = re.sub('\\)', '', s)
# Substitute two or more consecutive whitespace with space
s = re.sub('\\s{2,}', ' ', s)
return s.strip()
label_set = {'entailment': 0, 'contradiction': 1, 'neutral': 2}
file_name = os.path.join(data_dir, 'snli_1.0_train.txt'
if is_train else 'snli_1.0_test.txt')
with open(file_name, 'r') as f:
rows = [row.split('\t') for row in f.readlines()[1:]]
premises = [extract_text(row[1]) for row in rows if row[0] in label_set]
hypotheses = [extract_text(row[2]) for row in rows if row[0] in label_set]
labels = [label_set[row[0]] for row in rows if row[0] in label_set]
return premises, hypotheses, labels
Now let’s print the first 3 pairs of premise and hypothesis, as well as their labels (“0”, “1”, and “2” correspond to “entailment”, “contradiction”, and “neutral”, respectively ).
train_data = read_snli(data_dir, is_train=True)
for x0, x1, y in zip(train_data[0][:3], train_data[1][:3], train_data[2][:3]):
print('premise:', x0)
print('hypothesis:', x1)
print('label:', y)
premise: A person on a horse jumps over a broken down airplane .
hypothesis: A person is training his horse for a competition .
label: 2
premise: A person on a horse jumps over a broken down airplane .
hypothesis: A person is at a diner , ordering an omelette .
label: 1
premise: A person on a horse jumps over a broken down airplane .
hypothesis: A person is outdoors , on a horse .
label: 0
train_data = read_snli(data_dir, is_train=True)
for x0, x1, y in zip(train_data[0][:3], train_data[1][:3], train_data[2][:3]):
print('premise:', x0)
print('hypothesis:', x1)
print('label:', y)
premise: A person on a horse jumps over a broken down airplane .
hypothesis: A person is training his horse for a competition .
label: 2
premise: A person on a horse jumps over a broken down airplane .
hypothesis: A person is at a diner , ordering an omelette .
label: 1
premise: A person on a horse jumps over a broken down airplane .
hypothesis: A person is outdoors , on a horse .
label: 0
The training set has about 550000 pairs, and the testing set has about 10000 pairs. The following shows that the three labels “entailment”, “contradiction”, and “neutral” are balanced in both the training set and the testing set.
test_data = read_snli(data_dir, is_train=False)
for data in [train_data, test_data]:
print([[row for row in data[2]].count(i) for i in range(3)])
[183416, 183187, 182764]
[3368, 3237, 3219]
test_data = read_snli(data_dir, is_train=False)
for data in [train_data, test_data]:
print([[row for row in data[2]].count(i) for i in range(3)])
[183416, 183187, 182764]
[3368, 3237, 3219]
16.4.2.2. Defining a Class for Loading the Dataset¶
Below we define a class for loading the SNLI dataset by inheriting from
the Dataset class in Gluon. The argument num_steps in the class
constructor specifies the length of a text sequence so that each
minibatch of sequences will have the same shape. In other words, tokens
after the first num_steps ones in longer sequence are trimmed, while
special tokens “<pad>” will be appended to shorter sequences until their
length becomes num_steps. By implementing the __getitem__
function, we can arbitrarily access the premise, hypothesis, and label
with the index idx.
#@save
class SNLIDataset(torch.utils.data.Dataset):
"""A customized dataset to load the SNLI dataset."""
def __init__(self, dataset, num_steps, vocab=None):
self.num_steps = num_steps
all_premise_tokens = d2l.tokenize(dataset[0])
all_hypothesis_tokens = d2l.tokenize(dataset[1])
if vocab is None:
self.vocab = d2l.Vocab(all_premise_tokens + all_hypothesis_tokens,
min_freq=5, reserved_tokens=['<pad>'])
else:
self.vocab = vocab
self.premises = self._pad(all_premise_tokens)
self.hypotheses = self._pad(all_hypothesis_tokens)
self.labels = torch.tensor(dataset[2])
print('read ' + str(len(self.premises)) + ' examples')
def _pad(self, lines):
return torch.tensor([d2l.truncate_pad(
self.vocab[line], self.num_steps, self.vocab['<pad>'])
for line in lines])
def __getitem__(self, idx):
return (self.premises[idx], self.hypotheses[idx]), self.labels[idx]
def __len__(self):
return len(self.premises)
#@save
class SNLIDataset(gluon.data.Dataset):
"""A customized dataset to load the SNLI dataset."""
def __init__(self, dataset, num_steps, vocab=None):
self.num_steps = num_steps
all_premise_tokens = d2l.tokenize(dataset[0])
all_hypothesis_tokens = d2l.tokenize(dataset[1])
if vocab is None:
self.vocab = d2l.Vocab(all_premise_tokens + all_hypothesis_tokens,
min_freq=5, reserved_tokens=['<pad>'])
else:
self.vocab = vocab
self.premises = self._pad(all_premise_tokens)
self.hypotheses = self._pad(all_hypothesis_tokens)
self.labels = np.array(dataset[2])
print('read ' + str(len(self.premises)) + ' examples')
def _pad(self, lines):
return np.array([d2l.truncate_pad(
self.vocab[line], self.num_steps, self.vocab['<pad>'])
for line in lines])
def __getitem__(self, idx):
return (self.premises[idx], self.hypotheses[idx]), self.labels[idx]
def __len__(self):
return len(self.premises)
16.4.2.3. Putting It All Together¶
Now we can invoke the read_snli function and the SNLIDataset
class to download the SNLI dataset and return DataLoader instances
for both training and testing sets, together with the vocabulary of the
training set. It is noteworthy that we must use the vocabulary
constructed from the training set as that of the testing set. As a
result, any new token from the testing set will be unknown to the model
trained on the training set.
#@save
def load_data_snli(batch_size, num_steps=50):
"""Download the SNLI dataset and return data iterators and vocabulary."""
num_workers = d2l.get_dataloader_workers()
data_dir = d2l.download_extract('SNLI')
train_data = read_snli(data_dir, True)
test_data = read_snli(data_dir, False)
train_set = SNLIDataset(train_data, num_steps)
test_set = SNLIDataset(test_data, num_steps, train_set.vocab)
train_iter = torch.utils.data.DataLoader(train_set, batch_size,
shuffle=True,
num_workers=num_workers)
test_iter = torch.utils.data.DataLoader(test_set, batch_size,
shuffle=False,
num_workers=num_workers)
return train_iter, test_iter, train_set.vocab
#@save
def load_data_snli(batch_size, num_steps=50):
"""Download the SNLI dataset and return data iterators and vocabulary."""
num_workers = d2l.get_dataloader_workers()
data_dir = d2l.download_extract('SNLI')
train_data = read_snli(data_dir, True)
test_data = read_snli(data_dir, False)
train_set = SNLIDataset(train_data, num_steps)
test_set = SNLIDataset(test_data, num_steps, train_set.vocab)
train_iter = gluon.data.DataLoader(train_set, batch_size, shuffle=True,
num_workers=num_workers)
test_iter = gluon.data.DataLoader(test_set, batch_size, shuffle=False,
num_workers=num_workers)
return train_iter, test_iter, train_set.vocab
Here we set the batch size to 128 and sequence length to 50, and invoke
the load_data_snli function to get the data iterators and
vocabulary. Then we print the vocabulary size.
train_iter, test_iter, vocab = load_data_snli(128, 50)
len(vocab)
read 549367 examples
read 9824 examples
18678
train_iter, test_iter, vocab = load_data_snli(128, 50)
len(vocab)
[22:09:03] ../src/storage/storage.cc:196: Using Pooled (Naive) StorageManager for CPU
read 549367 examples
read 9824 examples
18678
Now we print the shape of the first minibatch. Contrary to sentiment
analysis, we have two inputs X[0] and X[1] representing pairs of
premises and hypotheses.
for X, Y in train_iter:
print(X[0].shape)
print(X[1].shape)
print(Y.shape)
break
torch.Size([128, 50])
torch.Size([128, 50])
torch.Size([128])
for X, Y in train_iter:
print(X[0].shape)
print(X[1].shape)
print(Y.shape)
break
(128, 50)
(128, 50)
(128,)
16.4.3. Summary¶
Natural language inference studies whether a hypothesis can be inferred from a premise, where both are a text sequence.
In natural language inference, relationships between premises and hypotheses include entailment, contradiction, and neutral.
Stanford Natural Language Inference (SNLI) Corpus is a popular benchmark dataset of natural language inference.
16.4.4. Exercises¶
Machine translation has long been evaluated based on superficial \(n\)-gram matching between an output translation and a ground-truth translation. Can you design a measure for evaluating machine translation results by using natural language inference?
How can we change hyperparameters to reduce the vocabulary size?