# 15.4. Natural Language Inference and the Dataset¶ Open the notebook in Colab

In Section 15.1, 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. It results in

## 15.4.1. Natural Language Inference¶

Natural language inference (NLI) studies whether a hypothesis can be inferred from a premise, where both are a text sequence. In other words, NLI 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.

NLI 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.

NLI 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 NLI benchmark dataset.

## 15.4.2. The Stanford Natural Language Inference (SNLI) Dataset¶

Stanford Natural Language Inference (SNLI) Corpus is a collection of over $$500,000$$ labeled English sentence pairs [Bowman et al., 2015]. We download and store the extracted SNLI dataset in the path ../data/snli_1.0.

import collections
import d2l
from mxnet import gluon, np, npx
import os
import re
import zipfile

npx.set_np()

# Saved in the d2l package for later use
d2l.DATA_HUB['SNLI'] = (
'https://nlp.stanford.edu/projects/snli/snli_1.0.zip',
'9fcde07509c7e87ec61c640c1b2753d9041758e4')


Downloading ../data/snli_1.0.zip from https://nlp.stanford.edu/projects/snli/snli_1.0.zip...


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.

# Saved in the d2l package for later use
"""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 = (data_dir + 'snli_1.0_'+ ('train' if is_train else '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


The training set has about $$550,000$$ pairs, and the testing set has about $$10,000$$ 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]


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.

# Saved in the d2l package for later use
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
p_tokens = d2l.tokenize(dataset[0])
h_tokens = d2l.tokenize(dataset[1])
if vocab is None:
self.vocab = d2l.Vocab(p_tokens + h_tokens, min_freq=5,
else:
self.vocab = vocab
self.labels = np.array(dataset[2])
print('read ' + str(len(self.premises)) + ' examples')

def __getitem__(self, idx):
return (self.premises[idx], self.hypotheses[idx]), self.labels[idx]

def __len__(self):
return len(self.premises)


### 15.4.2.3. Putting All Things 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.

# Saved in the d2l package for later use
train_set = SNLIDataset(train_data, num_steps)
test_set = SNLIDataset(test_data, num_steps, train_set.vocab)
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

18678


Now we print the shape of the first minibatch. Contrary to sentiment analysis, we have $$2$$ 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

(128, 50)
(128, 50)
(128,)


## 15.4.3. Summary¶

• Natural language inference (NLI) studies whether a hypothesis can be inferred from a premise, where both are a text sequence.

• In NLI, relationships between premises and hypotheses include entailment, contradiction, and neutral.

• Stanford Natural Language Inference (SNLI) Corpus is a popular benchmark dataset of NLI.

## 15.4.4. Exercises¶

1. 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 NLI?

2. How can we change hyperparameters to reduce the vocabulary size?