babi_rnn.RmdTrains two recurrent neural networks based upon a story and a question. The resulting merged vector is then queried to answer a range of bAbI tasks.
The results are comparable to those for an LSTM model provided in Weston et al.: “Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks” http://arxiv.org/abs/1502.05698
| Task Number | FB LSTM Baseline | Keras QA |
|---|---|---|
| QA1 - Single Supporting Fact | 50 | 100.0 |
| QA2 - Two Supporting Facts | 20 | 50.0 |
| QA3 - Three Supporting Facts | 20 | 20.5 |
| QA4 - Two Arg. Relations | 61 | 62.9 |
| QA5 - Three Arg. Relations | 70 | 61.9 |
| QA6 - yes/No Questions | 48 | 50.7 |
| QA7 - Counting | 49 | 78.9 |
| QA8 - Lists/Sets | 45 | 77.2 |
| QA9 - Simple Negation | 64 | 64.0 |
| QA10 - Indefinite Knowledge | 44 | 47.7 |
| QA11 - Basic Coreference | 72 | 74.9 |
| QA12 - Conjunction | 74 | 76.4 |
| QA13 - Compound Coreference | 94 | 94.4 |
| QA14 - Time Reasoning | 27 | 34.8 |
| QA15 - Basic Deduction | 21 | 32.4 |
| QA16 - Basic Induction | 23 | 50.6 |
| QA17 - Positional Reasoning | 51 | 49.1 |
| QA18 - Size Reasoning | 52 | 90.8 |
| QA19 - Path Finding | 8 | 9.0 |
| QA20 - Agent’s Motivations | 91 | 90.7 |
For the resources related to the bAbI project, refer to: https://research.facebook.com/researchers/1543934539189348
Notes:
50% test accuracy on QA2 in 20 epochs (16 seconds per epoch on CPU) In comparison, the Facebook paper achieves 50% and 20% for the LSTM baseline.
The task does not traditionally parse the question separately. This likely improves accuracy and is a good example of merging two RNNs.
The word vector embeddings are not shared between the story and question RNNs.
See how the accuracy changes given 10,000 training samples (en-10k) instead of only 1000. 1000 was used in order to be comparable to the original paper.
Experiment with GRU, LSTM, and JZS1-3 as they give subtly different results.
The length and noise (i.e. ‘useless’ story components) impact the ability for LSTMs / GRUs to provide the correct answer. Given only the supporting facts, these RNNs can achieve 100% accuracy on many tasks. Memory networks and neural networks that use attentional processes can efficiently search through this noise to find the relevant statements, improving performance substantially. This becomes especially obvious on QA2 and QA3, both far longer than QA1.
library(keras)
library(readr)
library(stringr)
library(purrr)
library(tibble)
library(dplyr)
# Function definition -----------------------------------------------------
tokenize_words <- function(x){
x <- x %>%
str_replace_all('([[:punct:]]+)', ' \\1') %>%
str_split(' ') %>%
unlist()
x[x != ""]
}
parse_stories <- function(lines, only_supporting = FALSE){
lines <- lines %>%
str_split(" ", n = 2) %>%
map_df(~tibble(nid = as.integer(.x[[1]]), line = .x[[2]]))
lines <- lines %>%
mutate(
split = map(line, ~str_split(.x, "\t")[[1]]),
q = map_chr(split, ~.x[1]),
a = map_chr(split, ~.x[2]),
supporting = map(split, ~.x[3] %>% str_split(" ") %>% unlist() %>% as.integer()),
story_id = c(0, cumsum(nid[-nrow(.)] > nid[-1]))
) %>%
select(-split)
stories <- lines %>%
filter(is.na(a)) %>%
select(nid_story = nid, story_id, story = q)
questions <- lines %>%
filter(!is.na(a)) %>%
select(-line) %>%
left_join(stories, by = "story_id") %>%
filter(nid_story < nid)
if(only_supporting){
questions <- questions %>%
filter(map2_lgl(nid_story, supporting, ~.x %in% .y))
}
questions %>%
group_by(story_id, nid, question = q, answer = a) %>%
summarise(story = paste(story, collapse = " ")) %>%
ungroup() %>%
mutate(
question = map(question, ~tokenize_words(.x)),
story = map(story, ~tokenize_words(.x)),
id = row_number()
) %>%
select(id, question, answer, story)
}
vectorize_stories <- function(data, vocab, story_maxlen, query_maxlen){
questions <- map(data$question, function(x){
map_int(x, ~which(.x == vocab))
})
stories <- map(data$story, function(x){
map_int(x, ~which(.x == vocab))
})
# "" represents padding
answers <- sapply(c("", vocab), function(x){
as.integer(x == data$answer)
})
list(
questions = pad_sequences(questions, maxlen = query_maxlen),
stories = pad_sequences(stories, maxlen = story_maxlen),
answers = answers
)
}
# Parameters --------------------------------------------------------------
max_length <- 99999
embed_hidden_size <- 50
batch_size <- 32
epochs <- 40
# Data Preparation --------------------------------------------------------
path <- get_file(
fname = "babi-tasks-v1-2.tar.gz",
origin = "https://s3.amazonaws.com/text-datasets/babi_tasks_1-20_v1-2.tar.gz"
)
untar(path, exdir = str_replace(path, fixed(".tar.gz"), "/"))
path <- str_replace(path, fixed(".tar.gz"), "/")
# Default QA1 with 1000 samples
# challenge = '%stasks_1-20_v1-2/en/qa1_single-supporting-fact_%s.txt'
# QA1 with 10,000 samples
# challenge = '%stasks_1-20_v1-2/en-10k/qa1_single-supporting-fact_%s.txt'
# QA2 with 1000 samples
challenge <- "%stasks_1-20_v1-2/en/qa2_two-supporting-facts_%s.txt"
# QA2 with 10,000 samples
# challenge = '%stasks_1-20_v1-2/en-10k/qa2_two-supporting-facts_%s.txt'
train <- read_lines(sprintf(challenge, path, "train")) %>%
parse_stories() %>%
filter(map_int(story, ~length(.x)) <= max_length)
test <- read_lines(sprintf(challenge, path, "test")) %>%
parse_stories() %>%
filter(map_int(story, ~length(.x)) <= max_length)
# extract the vocabulary
all_data <- bind_rows(train, test)
vocab <- c(unlist(all_data$question), all_data$answer,
unlist(all_data$story)) %>%
unique() %>%
sort()
# Reserve 0 for masking via pad_sequences
vocab_size <- length(vocab) + 1
story_maxlen <- map_int(all_data$story, ~length(.x)) %>% max()
query_maxlen <- map_int(all_data$question, ~length(.x)) %>% max()
# vectorized versions of training and test sets
train_vec <- vectorize_stories(train, vocab, story_maxlen, query_maxlen)
test_vec <- vectorize_stories(test, vocab, story_maxlen, query_maxlen)
# Defining the model ------------------------------------------------------
sentence <- layer_input(shape = c(story_maxlen), dtype = "int32")
encoded_sentence <- sentence %>%
layer_embedding(input_dim = vocab_size, output_dim = embed_hidden_size) %>%
layer_dropout(rate = 0.3)
question <- layer_input(shape = c(query_maxlen), dtype = "int32")
encoded_question <- question %>%
layer_embedding(input_dim = vocab_size, output_dim = embed_hidden_size) %>%
layer_dropout(rate = 0.3) %>%
layer_lstm(units = embed_hidden_size) %>%
layer_repeat_vector(n = story_maxlen)
merged <- list(encoded_sentence, encoded_question) %>%
layer_add() %>%
layer_lstm(units = embed_hidden_size) %>%
layer_dropout(rate = 0.3)
preds <- merged %>%
layer_dense(units = vocab_size, activation = "softmax")
model <- keras_model(inputs = list(sentence, question), outputs = preds)
model %>% compile(
optimizer = "adam",
loss = "categorical_crossentropy",
metrics = "accuracy"
)
model
# Training ----------------------------------------------------------------
model %>% fit(
x = list(train_vec$stories, train_vec$questions),
y = train_vec$answers,
batch_size = batch_size,
epochs = epochs,
validation_split=0.05
)
evaluation <- model %>% evaluate(
x = list(test_vec$stories, test_vec$questions),
y = test_vec$answers,
batch_size = batch_size
)
evaluation