🏷️sec_natural-language-inference-and-dataset
In :numref:sec_sentiment, 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.
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.
[Stanford Natural Language Inference (SNLI) Corpus] is a collection of over 500000 labeled English sentence pairs :cite:Bowman.Angeli.Potts.ea.2015.
We download and store the extracted SNLI dataset in the path ../data/snli_1.0.
#@tab mxnet
from d2l import mxnet as d2l
from mxnet import gluon, np, npx
import os
import re
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')
#@tab pytorch
from d2l import torch as d2l
import torch
from torch import nn
import os
import re
#@save
d2l.DATA_HUB['SNLI'] = (
'https://nlp.stanford.edu/projects/snli/snli_1.0.zip',
'9fcde07509c7e87ec61c640c1b2753d9041758e4')
data_dir = d2l.download_extract('SNLI')
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.
#@tab all
#@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 ).
#@tab all
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)
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.
#@tab all
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)])
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.
#@tab mxnet
#@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)
#@tab pytorch
#@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)
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.
#@tab mxnet
#@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
#@tab pytorch
#@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
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.
#@tab all
train_iter, test_iter, vocab = load_data_snli(128, 50)
len(vocab)
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.
#@tab all
for X, Y in train_iter:
print(X[0].shape)
print(X[1].shape)
print(Y.shape)
break
- 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.
- 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?
:begin_tab:mxnet
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:begin_tab:pytorch
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