Sale!

CS 224n: Assignment #4

$30.00

Category:

CS 224n: Assignment #4
This assignment is split into two sections: Neural Machine Translation with RNNs and Analyzing NMT
Systems. The first is primarily coding and implementation focused, whereas the second entirely consists
of written, analysis questions. If you get stuck on the first section, you can always work on the second.
That being said, the NMT system is more complicated than the neural networks we have previously constructed within this class and takes about 4 hours to train on a GPU. Thus, we strongly recommend you
get started early with this assignment. Finally, the notation and implementation of the NMT system is a
bit tricky, so if you ever get stuck along the way, please come to Office Hours so that the TAs can support you.
1. Neural Machine Translation with RNNs (45 points)
In Machine Translation, our goal is to convert a sentence from the source language (e.g. Spanish) to the
target language (e.g. English). In this assignment, we will implement a sequence-to-sequence (Seq2Seq)
network with attention, to build a Neural Machine Translation (NMT) system. In this section, we
describe the training procedure for the proposed NMT system, which uses a Bidirectional LSTM
Encoder and a Unidirectional LSTM Decoder.
Figure 1: Seq2Seq Model with Multiplicative Attention, shown on the third step of the decoder. Note that
for readability, we do not picture the concatenation of the previous combined-output with the decoder input.
Given a sentence in the source language, we look up the word embeddings from an embeddings matrix,
yielding x1, . . . , xm | xi ∈ R
e×1
, where m is the length of the source sentence and e is the embedding size.
We feed these embeddings to the bidirectional Encoder, yielding hidden states and cell states for both
the forwards (→) and backwards (←) LSTMs. The forwards and backwards versions are concatenated
to give hidden states h
enc
i
and cell states c
enc
i
:
1
CS 224n Assignment 4 Page 2 of 7
h
enc
i = [←−−
h
enc
i
;
−−→
h
enc
i
] where h
enc
i ∈ R
2h×1
,
←−−
h
enc
i
,
−−→
h
enc
i ∈ R
h×1
1 ≤ i ≤ m (1)
c
enc
i = [←−−
c
enc
i
;
−−→
c
enc
i
] where c
enc
i ∈ R
2h×1
,
←−−
c
enc
i
,
−−→
c
enc
i ∈ R
h×1
1 ≤ i ≤ m (2)
We then initialize the Decoder’s first hidden state h
dec
0 and cell state c
dec
0 with a linear projection of the
Encoder’s final hidden state and final cell state.1
h
dec
0 = Wh[
←−−
h
enc
1
;
−−→
h
enc
m ] where h
dec
0 ∈ R
h×1
,Wh ∈ R
h×2h
(3)
c
dec
0 = Wc[
←−−
c
enc
1
;
−−→
c
enc
m ] where c
dec
0 ∈ R
h×1
,Wc ∈ R
h×2h
(4)
With the Decoder initialized, we must now feed it a matching sentence in the target language. On the
t
th step, we look up the embedding for the t
th word, yt ∈ R
e×1
. We then concatenate yt with the
combined-output vector ot−1 ∈ R
h×1
from the previous timestep (we will explain what this is later down
this page!) to produce yt ∈ R
(e+h)×1
. Note that for the first target word (i.e. the start token) o0 is a
zero-vector. We then feed yt as input to the Decoder LSTM.
h
dec
t
, c
dec
t = Decoder(yt, h
dec
t−1
, c
dec
t−1
) where h
dec
t ∈ R
h×1
, c
dec
t ∈ R
h×1
(5)
(6)
We then use h
dec
t
to compute multiplicative attention over h
enc
0
, . . . , h
enc
m :
et,i = (h
dec
t
)
TWattProjh
enc
i where et ∈ R
m×1
,WattProj ∈ R
h×2h
1 ≤ i ≤ m (7)
αt = Softmax(et) where αt ∈ R
m×1
(8)
at =
Xm
i
αt,ih
enc
i where at ∈ R
2h×1
(9)
We now concatenate the attention output at with the decoder hidden state h
dec
t and pass this through
a linear layer, Tanh, and Dropout to attain the combined-output vector ot.
ut = [at; h
dec
t
] where ut ∈ R
3h×1
(10)
vt = Wuut where vt ∈ R
h×1
,Wu ∈ R
h×3h
(11)
ot = Dropout(Tanh(vt)) where ot ∈ R
h×1
(12)
Then, we produce a probability distribution Pt over target words at the t
th timestep:
Pt = Softmax(Wvocabot) where Pt ∈ R
Vt×1
,Wvocab ∈ R
Vt×h
(13)
Here, Vt is the size of the target vocabulary. Finally, to train the network we then compute the softmax
cross entropy loss between Pt and gt, where gt is the 1-hot vector of the target word at timestep t:
1
If it’s not obvious, think about why we regard [←−−
h
enc
1
,
−−→h
enc m ] as the ‘final hidden state’ of the Encoder.
CS 224n Assignment 4 Page 3 of 7
Jt(θ) = CE(Pt, gt) (14)
Here, θ represents all the parameters of the model and Jt(θ) is the loss on step t of the decoder. Now
that we have described the model, let’s try implementing it for Spanish to English translation!
Follow the instructions in the CS224n Azure Guide (link also provided on website and Piazza) in order
to create your VM instance. This should take you approximately 45 minutes. Though you will need
the GPU to train your model, we strongly advise that you first develop the code locally and ensure
that it runs, before attempting to train it on your VM. GPU time is expensive and limited. It takes
approximately 4 hours to train the NMT system. We don’t want you to accidentally use all your GPU
time for the assignment, debugging your model rather than training and evaluating it. Finally, make
sure that your VM is turned off whenever you are not using it.
If your Azure subscription runs out of money your VM will be locked and all code and
data on the VM will be lost. Turn off your VM and request more Azure credits before
your subscription runs out. See Piazza for instructions on requesting more credits if you
are about to run out.
In order to run the model code on your local machine, please run the following command to create the
proper virtual environment:
conda env create –file local env.yml
Note that this virtual environment will not be needed on the VM.
(a) (2 points) In order to apply tensor operations, we must ensure that the sentences in a given batch
are of the same length. Thus, we must identify the longest sentence in a batch and pad others to
be the same length. Implement the pad sents function in utils.py, which shall produce these
padded sentences.
(b) (3 points) Implement the init function in model embeddings.py to initialize the necessary
source and target embeddings.
(c) (4 points) Implement the init function in nmt model.py to initialize the necessary model embeddings (using the ModelEmbeddings class from model embeddings.py) and layers (LSTM,
projection, and dropout) for the NMT system.
(d) (8 points) Implement the encode function in nmt model.py. This function converts the padded
source sentences into the tensor X, generates h
enc
1
, . . . , h
enc
m , and computes the initial state h
dec
0 and
initial cell c
dec
0
for the Decoder. You can run a non-comprehensive sanity check by executing:
python sanity_check.py 1d
(e) (8 points) Implement the decode function in nmt model.py. This function constructs y¯ and
runs the step function over every timestep for the input. You can run a non-comprehensive sanity
check by executing:
python sanity_check.py 1e
(f) (10 points) Implement the step function in nmt model.py. This function applies the Decoder’s
LSTM cell for a single timestep, computing the encoding of the target word h
dec
t
, the attention
scores et, attention distribution αt, the attention output at, and finally the combined output ot.
You can run a non-comprehensive sanity check by executing:
python sanity_check.py 1f
CS 224n Assignment 4 Page 4 of 7
(g) (3 points) (written) The generate sent masks() function in nmt model.py produces a tensor
called enc masks. It has shape (batch size, max source sentence length) and contains 1s in positions
corresponding to ‘pad’ tokens in the input, and 0s for non-pad tokens. Look at how the masks are
used during the attention computation in the step() function (lines 295-296).
First explain (in around three sentences) what effect the masks have on the entire attention computation. Then explain (in one or two sentences) why it is necessary to use the masks in this
way.
(h) Now it’s time to get things running! Execute the following to generate the necessary vocab file:
sh run.sh vocab
As noted earlier, we recommend that you develop the code on your personal computer. Confirm
that you are running in the proper conda environment and then execute the following command to
train the model on your local machine:
sh run.sh train_local
Once you have ensured that your code does not crash (i.e. let it run till iter 10 or iter 20),
power on your VM from the Azure Web Portal. Then read the Managing Code Deployment to a
VM section of our Practical Guide to VMs (link also given on website and Piazza) for instructions
on how to upload your code to the VM.
Next, install necessary packages to your VM by running:
pip install -r gpu_requirements.txt
Finally, turn to the Managing Processes on a VM section of the Practical Guide and follow the
instructions to create a new tmux session. Concretely, run the following command to create tmux
session called nmt.
tmux new -s nmt
Once your VM is configured and you are in a tmux session, execute:
sh run.sh train
Once you know your code is running properly, you can detach from session and close your ssh
connection to the server. To detach from the session, run:
tmux detach
You can return to your training model by ssh-ing back into the server and attaching to the tmux
session by running:
tmux a -t nmt
(i) (4 points) Once your model is done training (this should take about 4 hours on the VM),
execute the following command to test the model:
sh run.sh test
Please report the model’s corpus BLEU Score. It should be larger than 21.
(j) (3 points) In class, we learned about dot product attention, multiplicative attention, and additive
attention. Please provide one possible advantage and disadvantage of each attention mechanism,
with respect to either of the other two attention mechanisms. As a reminder, dot product attention
is et,i = s
T
t hi
, multiplicative attention is et,i = s
T
t Whi
, and additive attention is et,i = v
T
(W1hi +
W2st).
2. Analyzing NMT Systems (30 points)
(a) (12 points) Here we present a series of errors we found in the outputs of our NMT model (which
is the same as the one you just trained). For each example of a Spanish source sentence, reference
(i.e., ‘gold’) English translation, and NMT (i.e., ‘model’) English translation, please:
CS 224n Assignment 4 Page 5 of 7
1. Identify the error in the NMT translation.
2. Provide a reason why the model may have made the error (either due to a specific linguistic
construct or specific model limitations).
3. Describe one possible way we might alter the NMT system to fix the observed error.
Below are the translations that you should analyze as described above. Note that out-of-vocabulary
words are underlined.
i. (2 points) Source Sentence: Aqu´ı otro de mis favoritos, “La noche estrellada”.
Reference Translation: So another one of my favorites, “The Starry Night”.
NMT Translation: Here’s another favorite of my favorites, “The Starry Night”.
ii. (2 points) Source Sentence: Ustedes saben que lo que yo hago es escribir para los ni˜nos, y,
de hecho, probablemente soy el autor para ni˜nos, ms ledo en los EEUU.
Reference Translation: You know, what I do is write for children, and I’m probably America’s
most widely read children’s author, in fact.
NMT Translation: You know what I do is write for children, and in fact, I’m probably the
author for children, more reading in the U.S.
iii. (2 points) Source Sentence: Un amigo me hizo eso – Richard Bolingbroke.
Reference Translation: A friend of mine did that – Richard Bolingbroke.
NMT Translation: A friend of mine did that – Richard <unk>
iv. (2 points) Source Sentence: Solo tienes que dar vuelta a la manzana para verlo como una
epifan´ıa.
Reference Translation: You’ve just got to go around the block to see it as an epiphany.
NMT Translation: You just have to go back to the apple to see it as a epiphany.
v. (2 points) Source Sentence: Ella salv´o mi vida al permitirme entrar al ba˜no de la sala de
profesores.
Reference Translation: She saved my life by letting me go to the bathroom in the teachers’
lounge.
NMT Translation: She saved my life by letting me go to the bathroom in the women’s room.
vi. (2 points) Source Sentence: Eso es m´as de 100,000 hect´areas.
Reference Translation: That’s more than 250 thousand acres.
NMT Translation: That’s over 100,000 acres.
(b) (4 points) Now it is time to explore the outputs of the model that you have trained! The test-set
translations your model produced in question 1-i should be located in outputs/test outputs.txt.
Please identify 2 examples of errors that your model produced.2 The two examples you find should
be different error types from one another and different error types than the examples provided in
the previous question. For each example you should:
1. Write the source sentence in Spanish. The source sentences are in the en es data/test.es.
2. Write the reference English translation. The reference translations are in the en es data/test.en.
3. Write your NMT model’s English translation. The model-translated sentences are in the
outputs/test outputs.txt.
4. Identify the error in the NMT translation.
5. Provide a reason why the model may have made the error (either due to a specific linguistic
construct or specific model limitations).
6. Describe one possible way we might alter the NMT system to fix the observed error.
2An ‘error’ is not just a NMT translation that doesn’t match the reference translation. There must be something wrong
with the NMT translation, in your opinion.
CS 224n Assignment 4 Page 6 of 7
(c) (14 points) BLEU Score is the most commonly used automatic evaluation metric for NMT systems.
It is usually calculated across the entire test set, but here we will consider BLEU defined for a single
example.3 Suppose we have a source sentence s, a set of k reference translations r1, . . . , rk, and a
candidate translation c. To compute the BLEU score of c, we first compute the modified n-gram
precision pn of c, for each of n = 1, 2, 3, 4:
pn =
X
ngram∈c
min 
max
i=1,…,k
Countri
(ngram), Countc(ngram)
X
ngram∈c
Countc(ngram)
(15)
Here, for each of the n-grams that appear in the candidate translation c, we count the maximum number of times it appears in any one reference translation, capped by the number of times
it appears in c (this is the numerator). We divide this by the number of n-grams in c (denominator).
Next, we compute the brevity penalty BP. Let c be the length of c and let r
∗ be the length of
the reference translation that is closest to c (in the case of two equally-close reference translation
lengths, choose r
∗ as the shorter one).
BP =
(
1 if c ≥ r

exp
1 −
r

c

otherwise
(16)
Lastly, the BLEU score for candidate c with respect to r1, . . . , rk is:
BLEU = BP × exp X
4
n=1
λn log pn

(17)
where λ1, λ2, λ3, λ4 are weights that sum to 1.
i. (5 points) Please consider this example:
Source Sentence s: el amor todo lo puede
Reference Translation r1: love can always find a way
Reference Translation r2: love makes anything possible
NMT Translation c1: the love can always do
NMT Translation c2: love can make anything possible
Please compute the BLEU scores for c1 and c2. Let λi = 0.5 for i ∈ {1, 2} and λi = 0 for
i ∈ {3, 4} (this means we ignore 3-grams and 4-grams, i.e., don’t compute p3 or p4).
When computing BLEU scores, show your working (i.e., show your computed values for p1, p2,
c, r
∗ and BP).
Which of the two NMT translations is considered the better translation according to the BLEU
Score? Do you agree that it is the better translation?
ii. (5 points) Our hard drive was corrupted and we lost Reference Translation r2. Please recompute BLEU scores for c1 and c2, this time with respect to r1 only. Which of the two NMT
translations now receives the higher BLEU score? Do you agree that it is the better translation?
3This definition of sentence-level BLEU score matches the sentence bleu() function in the nltk Python package. Note
that the NLTK function is sensitive to capitalization. In this question, all text is lowercased, so capitalization is irrelevant.
http://www.nltk.org/api/nltk.translate.html#nltk.translate.bleu_score.sentence_ble

Reviews

There are no reviews yet.

Be the first to review “CS 224n: Assignment #4”

Your email address will not be published.