Pytorch Exercise: Augmenting the LSTM part-of-speech tagger with character-level features
最近在学习pytorch，尝试了一个LSTM来进行词性标注的demo，看到文末有可以练习的Exercise，于是尝试解决。最终代码如下。
官网上给出的问题是：
In the example above, each word had an embedding, which served as the inputs to our sequence model. Let’s augment the word embeddings with a representation derived from the characters of the word. We expect that this should help significantly, since character-level information like affixes have a large bearing on part-of-speech. For example, words with the affix -ly are almost always tagged as adverbs in English.
To do this, let cwcw be the character-level representation of word ww. Let xwxw be the word embedding as before. Then the input to our sequence model is the concatenation of xwxw and cwcw. So if xwxw has dimension 5, and cwcw dimension 3, then our LSTM should accept an input of dimension 8.
To get the character level representation, do an LSTM over the characters of a word, and let cwcw be the final hidden state of this LSTM. Hints:
- There are going to be two LSTM’s in your new model. The original one that outputs POS tag scores, and the new one that outputs a character-level representation of each word.
- To do a sequence model over characters, you will have to embed characters. The character embeddings will be the input to the character LSTM.
在网上搜索答案无果后，自己写了程序。下面的程序中，使用character embedding的方法，设定单词最长长度为MAX_WORD_LEN，采用LSTM将每个单词字母的最终输出作为对应单词的char emb。
# -*- coding:utf8 -*-
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import torch.optim as optim
torch.manual_seed(1)
def prepare_sequence(seq, to_ix):
idxs = [to_ix[w] for w in seq]
tensor = torch.LongTensor(idxs)
return Variable(tensor)
training_data = [
("The dog ate the apple".split(), ["DET", "NN", "V", "DET", "NN"]),
("Everybody read that book".split(), ["NN", "V", "DET", "NN"])
]
word_to_ix = {}
for sent, tags in training_data:
for word in sent:
if word not in word_to_ix:
word_to_ix[word] = len(word_to_ix)
char_to_ix = {}
char_to_ix[' '] = len(char_to_ix)
for sent, _ in training_data:
for word in sent:
for char in word:
if char not in char_to_ix:
char_to_ix[char] = len(char_to_ix)
# print(char_to_ix)
# print('len(char_to_ix):',len(char_to_ix))
# print(word_to_ix)
tag_to_ix = {"DET": 0, "NN": 1, "V": 2}
class LSTMTagger(nn.Module):
def __init__(self, word_emb_dim, char_emb_dim, hidden_dim, vocab_size, tagset_size, char_size):
super(LSTMTagger,self).__init__()
self.hidden_dim = hidden_dim
self.char_emb_dim = char_emb_dim
self.word_embedding = nn.Embedding(vocab_size, word_emb_dim)
self.char_embedding = nn.Embedding(char_size, char_emb_dim)
self.char_lstm = nn.LSTM(char_emb_dim, char_emb_dim)
self.lstm = nn.LSTM(word_emb_dim + char_emb_dim, hidden_dim)
self.hidden2tag = nn.Linear(hidden_dim, tagset_size)
def forward(self, sentence_word, sentence_char, MAX_WORD_LEN):
# char emb
sentence_size = sentence_word.size()[0]
char_emb = self.char_embedding(sentence_char) # [sentence_size * MAX_WORD_LEN, char_emb_dim]
try :
char_emb = char_emb.view(len(sentence_word), MAX_WORD_LEN, -1).permute(1,0,2) # [MAX_WORD_LEN, sentence_size, char_emb_dim]
except :
print("char_emb.size():",char_emb.size())
self.hidden_char = self.initHidden_char(sentence_size)
char_lstm_out, self.hidden = self.char_lstm(char_emb, self.hidden_char)
char_embeded = char_lstm_out[-1,:,:].view(sentence_size,-1)
# word emb
word_embeded = self.word_embedding(sentence_word)
embeded = torch.cat((word_embeded, char_embeded),dim=1)
# print('embeded size:\n', embeded.size())
self.hidden = self.initHidden()
lstm_out, self.hidden = self.lstm(embeded.view(sentence_size,1,-1), self.hidden)
tag_space = self.hidden2tag(lstm_out.view(sentence_size,-1))
tag_scores = F.log_softmax(tag_space)
return tag_scores
def initHidden(self):
result = (Variable(torch.zeros(1,1,self.hidden_dim)),
Variable(torch.zeros(1, 1, self.hidden_dim)))
return result
def initHidden_char(self, sentence_size):
result = (Variable(torch.zeros(1, sentence_size, self.char_emb_dim)),
Variable(torch.zeros(1, sentence_size, self.char_emb_dim)))
return result
# These will usually be more like 32 or 64 dimensional.
# We will keep them small, so we can see how the weights change as we train.
WORD_EMB_DIM = 6
CHAR_EMB_DIM = 3
HIDDEN_DIM = 6
MAX_WORD_LEN = 8
model = LSTMTagger(WORD_EMB_DIM, CHAR_EMB_DIM, HIDDEN_DIM, len(word_to_ix), len(tag_to_ix), len(char_to_ix))
loss_function = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)
# before training
print('before training')
sentence_word = prepare_sequence(training_data[0][0], word_to_ix)
sent_chars = []
for w in training_data[0][0]:
sps = ' ' * (MAX_WORD_LEN - len(w))
sent_chars.extend(list(sps + w) if len(w) < MAX_WORD_LEN else list(w[:MAX_WORD_LEN]))
sentence_char = prepare_sequence(sent_chars, char_to_ix)
tag_scores = model(sentence_word, sentence_char, MAX_WORD_LEN)
targets = prepare_sequence(training_data[0][1], tag_to_ix)
print(tag_scores)
print('targets:\n',targets)
for epoch in range(300):
for sentence, tags in training_data:
model.zero_grad()
model.hidden = model.initHidden()
sentence_word = prepare_sequence(sentence, word_to_ix)
sent_chars = []
for w in sentence:
sps = ' ' * (MAX_WORD_LEN - len(w))
sent_chars.extend(list(sps + w) if len(w)