[SOLVED] Cse291 project 3- neural crfs for constituency parsing
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In this assignment, you will be building a neural conditional random field (CRF) for the task of constituency parsing. You only need to implement the inside algorithm for the CRF as part of your model’s forward pass. The rest of the system is provided in the starter code.
After introducing the task and modeling approach in Sections 2–3, we provide implementation instructions in Section 4. Finally, we describe deliverables in Section 5 and optional extensions to the project in Section 7.
2 Constituency Parsing
Constituency Parsing [1, 2] is a type of syntactic parsing, which is the task of assigning a syntactic structure to a sentence. For example:
I shot an elephant in my pajamas. can be parsed in two ways as Figure 1 shown.Figure 1: Two parse trees for an ambiguous sentence. The parse on the left corresponds to the humorous reading in which the elephant is in the pajamas, the parse on the right corresponds to the reading in which Captain Spaulding did the shooting in his pajamas.
For this assignment, you will be using data from the English WSJ Penn TreeBank (PTB) to learn a constituency parser. In the dataset itself, trees are represented as flattened strings with brackets and labels. For example,
(TOP (S (NP (NNP Ms.) (NNP Haag)) (VP (VBZ plays) (NP (NNP Elianti))) (. .)))
In the codebase, we provide a Dataloader() module to load the string trees and a draw_tree()
method to visualize the trees (rendered as below).Figure 2: Constituency tree example.
3 LSTM-CRFs for Constituency ParsingWe use Bi-LSTMs, MLPs (multilayer perceptrons), and Biaffine functions to obtain a scoring function φ for the task. Then, similar to the previous project on Name Entity Recognition (NER), we use a Conditional Random Field (CRF) layer to incorporate constituency structure among words. We will describe each component and the decoding and learning algorithm in details in the following parts.
Bi-LSTM features. As we’ve learned from lectures and the previous two assignments, recurrent neural networks (RNNs) are a natural choice for computing learned feature representations of sequences. Given a sentence with POS tags, X = {W,G}, we encode it as sequence of word embedding vectors w1 …wT and tag embedding vectors g1 …gT before passing it to the model. The word embedding and tag embedding and the same position are simply concatenated.
xt = CONCAT(wt,gt), We then apply a Bi-LSTM to this embedding sequence.
−→ (1)
h t = tanh(Wihxt + bih + Whhh(t−1) + bhh). (2)
For bidirectional RNNs, hidden states −→h t,←−h t are computed in forward and backward directions, respectively, and concatenated to obtain ht = h−→h t;←−h ti.
Boundary representation. We apply two separate MLPs, MLPleft and MLPright, to ht, to create feature representations of both left and right span endpoints.
rlefti = MLPleft(ht) (3)
rrighti = MLPright(ht) (4)
Biaffine scoring function. The biaffine scoring layer was first introduce in [3]. Given the left and right span representations, rlefti , rrighti , for each position i, we score each candidate span (i,j) using a biaffine operation over the left boundary representation ri and the right boundary representation rj, using an intermediate parameter matrix, wlabell , that depends on the label l. Thus, the score a span (i,j) with label l is given by:
Biaffine (5)
(6)
where w is a weight matrix for label l. And, accordingly, the vectorized version will be:
(7)
Note that, Wlabel is a tensor defined as .
Factorizing the production rules. In the neural CRF we present here in this project, all labels are independent of one-another given X. This is not true in PCFGs. In this project, the CRF structure is just for enforcing tree-shape, while the BiLSTM will predict labels for spans independently. This means our factorization assumptions are simpler. For a full sentence X, its score is the product of all constituent spans’ scores:
Φ(X,Y ) = Y φ(i,j,l) (8)
(i,j,l)∈Y
And the conditional log likelihood under the model is:
Φ(X,Y )
logP(Y |X) = log P Y 0∈O(X) Φ(X,Y 0) (9)
Here, O(X) denotes the set of valid binary trees over sentence X.
CYKDecoding. Given the log values of the local scoring potentials we computed earlier, logφ(i,j,l), we can use the Cocke–Younger–Kasami (CYK) algorithm to obtain the best scoring constituency tree Yˆ (i.e. whose score is maximum among all the possible parse trees):
Yˆ = argmaxY ∈O(X)P(Y |X) (10)
= argmaxY ∈O(X)Φ(X,Y ) (11)
The original CYK is a dynamic programming algorithm with O(n3 · |R|) complexity, where R is the set of all possible production rules in the underlying PCFG. However, in a neural CRF parser, we actually make the parent label and child label independent in our parametrization (see Eq. 8). Therefore, the CYK decoding algorithm in this assignment is just O(n3) time complexity (if the maxes in the pseudo-code below are pre-computed and cached). We provide an implementation of the algorithm in the CSE291 repository, and the pseudo-code (non-batched version) is as below.
Input:
Score: a scoring matrix of shape [T, T, L]
# stores the logs of the phi scores computed earlier
T: length of sentence
L: the number of labels
Output:
Result: a result matrix of shape [T, T]
# stores the scores of best sub-trees for each span
Algorithm:
# all the data are zero-indexed Initialize matrix Result with -inf
For i = 0 to T – 1:
Result[i, i] = 0
For d = 2 to T: # for each span length
For i = 0 to T – d: # for each start
j := i + d – 1 # end of span
For k = i to j – 1: # for each split point of span
Result[i, j] := max(Result[i, j], max(Score[i, j, *]) + Result[i, k] + Result[k+1, j])
Return Result
Learning. In order to estimate model parameters θ, we minimize the negative log likelihood over entire sequences in the dataset {X,Y∗}:
X,Y∗ X,Y∗
NLL = X −logp(Y ∗ | X;θ) = X −hlogΦ(X,Y ∗) − log X Φ(X,Y 0)i (12)
Y 0∈O(X)
To compute the partition function, logPY ∈O(X) Φ(X,Y ), we can use an algorithm similar to the CKY algorithm we defined above for decoding from the model.
Given all the log scores scores of labeled spans with from the biaffine function (technically, this will be a 3 dimensions matrix of shape [seq_length, seq_length, n_labels]), we can define pseudo-code for computing the partition function:
Input:
Score: a scoring matrix of shape [T, T, L]
# stores the logs of the phi scores computed earlier
T: length of sentence
L: the number of labels
Output:
S:aresultmatrixofshape[T,T],whereS[0,T-1]isthelogpartitionfunction
Algorithm:
# all the data are zero-indexed Initialize matrix S with -inf for i = 0 to T – 1:
S[i, i] = 0
For d = 2 to T: # for each span length
For i = 0 to T – d: # for each start
j := i + d – 1 # end of the span
For k = i to j – 1: # for each split point of span
S[i, j] := logSumExp(S[i, j], logSumExp(Score[i, j, *]) + S[i, k] + S[k+1, j])
Return S
In the starter code, we also provide some comments to help you understand how to implement the algorithm. All you need is to fill in the missing part of function inside(). You are allowed to implement it in your own way. However, we would suggest that you implement the vectorized version of it. Otherwise, your training process would significantly slow down on colab.
To implement the vectorized version, you will need first understand how the CYK decoding is implemented. You will find the helper function stripe() and the PyTorch’s Tensor operator diag() helpful in implementing the vectorized version.
Evaluation. The constituency trees in PTB data are n-array trees. However, our model actually reasons about Chomsky Normal Form (CNF) trees. In the provided evaluation script, we first convert the prediction back to an n-array tree and then compute the recall, precision, and F1 score.
4 Implementation Instructions
Implement the inside algorithm of the CRF layer defined above and use it for the task of Constituency Parsings. You are encouraged to clone and use the starter code from the CSE291 repository , which includes portions of the PTB data train/dev splits, data loading functionality, and evaluation using (labeled/unlabeled) span-based precision, recall, and F1 score. The only part you need to implement is to add the required Inside algorithm for CRF negative log likelihood learning.
Here’s the best scoring evaluation result on ./data/PTB_first_2000 (A full training log is also
attached in the appendix for reference):
dev: loss: 0.6538 – UCM: 18.76% LCM: 17.35%
UP: 84.74% UR: 86.06% UF: 85.40%
LP: 82.59% LR: 83.87% LF: 83.23% where
UCM/LCM stands for Unlabeled Complete Match,
UP/LP stands for Unlabeled/Labeled Precision, UR/LR stands for Unlabeled/Labeled Recall, UF/LF stands for Unlabeled/Labeled F1.
As a reminder, we will not be grading based on the absolute performance of your method as long as it performs reasonably well, indicating it is implemented correctly. If you approximately match the F1 numbers we provide above, you should expect you’ve implemented the algorithm correctly.
5 Deliverables
Please submit a 1–2 page report along with the source code on Gradescope. We suggest using the ACL style files, which are also available as an Overleaf template. The core of this project is about implementing the inside algorithm for a neural CRF parser. Thus, we don’t require as much analysis as in past projects. We ask you to briefly describe the overall approach, e.g. paraphrase the model description and math in your own words. Then we ask you briefly describe any specific design choices you made (e.g. how you vectorized your inside algorithm if you choose to do so). Then we ask you to present your evaluation results and runtime. Finally, we ask you to present two example predicted test trees and comment on errors you see vs. the ground-truth trees for the same examples. Shorter reports are fine for this project, e.g. 1-2 pages, though feel free to write more if you want to do additional analysis, try additional features (not required for full credit).
6 Useful References
While you can use the Parser package as a reference, the code in your implementation must be your own. Note that the model of constituency parsing in the package is different from that of our assignment.
7 Optional Extensions
• Simplify the neural architecture and compare it with our default model. For example, you can remove the tagging embedding layer, or use the same MLP layer (instead of two different MLP layers) to model the boundary representation r.
• Choose a pre-trained language model (like BERT or GloVe) and use its embeddings as input to the CRF model. Or use a char-level LSTM layer to encode words instead of directly using an embedding layer.
• Add a discrete grammar (e.g. X-bar or one read directly off the treebank) to constrain your neural CRF parser – e.g. not all triples of parent label, left child label, and right child label are permitted under real discrete grammars.
• Change the biaffine scoring function to capture interactions between parent and child labels –
i.e. learn a full neural grammar!
• Choose your own adventure! Propose and implement your own analysis or extension.
You are allowed to discuss the assignment with other students and collaborate on developing algorithms – you’re even allowed to help debug each other’s code! However, every line of your write-up and the new code you develop must be written by your own hand.
References
Appendix
The full training log is as below.
!cse291ass3 python main.py –batch-size 128
Building the fields
Namespace(batch_size=128, bos_index=2, buckets=32, clip=5.0, delete={’’, ’.’, ’,’, “’’”, ’?’, ’!’, ’S1’, ’:’, ’-NONE-’, ’‘‘’, ’TOP’}, dev=’data/ptb/dev.pid’, device=’cuda’, embed=None, encoder=’lstm’, eos_index=3, epochs=10, eps=1e-12, equal={’ADVP’: ’PRT’}, feat=None, lr=0.002, max_len=None, mu=0.9, n_embed=100, n_labels=71, n_tags=45, n_words=3511, nu=0.9, pad_index=0, test=’data/ptb/test.pid’, train=’data/ptb/train.pid’, unk=’unk’, unk_index=1, weight_decay=1e-05)
Building the model
Model(
(word_embed): Embedding(3511, 100)
(tag_embed): Embedding(45, 100)
(embed_dropout): Dropout(p=0.33, inplace=False)
(encoder): LSTM(200, 400, num_layers=3, dropout=0.33, bidirectional=True)
(encoder_dropout): Dropout(p=0.33, inplace=False)
(mlp_l): MLP(n_in=800, n_out=100, dropout=0.33)
(mlp_r): MLP(n_in=800, n_out=100, dropout=0.33)
(feat_biaffine): Biaffine(n_in=100, n_out=71, bias_x=True, bias_y=True)
(crf): CRFConstituency()
(criterion): CrossEntropyLoss()
)
train: Dataset(n_sentences=2000, n_batches=409, n_buckets=32) dev: Dataset(n_sentences=1700, n_batches=342, n_buckets=32) test: Dataset(n_sentences=2416, n_batches=484, n_buckets=32)
Epoch 1 / 10:
0 iter of epoch 1, loss: 4.9900
50 iter of epoch 1, loss: 1.7499
100 iter of epoch 1, loss: 1.0927
150 iter of epoch 1, loss: 1.3784
200 iter of epoch 1, loss: 0.6478
250 iter of epoch 1, loss: 0.8427
300 iter of epoch 1, loss: 1.0710
350 iter of epoch 1, loss: 0.6919 400 iter of epoch 1, loss: 0.9706 dev: loss: 0.8141 – UCM: 8.71% LCM: 7.41%
UP: 77.13% UR: 80.77% UF: 78.91%
LP: 74.12% LR: 77.63% LF: 75.84% Epoch 2 / 10:
0 iter of epoch 2, loss: 0.9249
50 iter of epoch 2, loss: 0.5275
100 iter of epoch 2, loss: 0.8728
150 iter of epoch 2, loss: 0.7860
200 iter of epoch 2, loss: 0.8848
250 iter of epoch 2, loss: 1.0504
300 iter of epoch 2, loss: 0.5526
350 iter of epoch 2, loss: 1.1311 400 iter of epoch 2, loss: 0.8681 dev: loss: 0.6986 – UCM: 12.47% LCM: 10.76%
UP: 81.49% UR: 83.45% UF: 82.46%
LP: 78.86% LR: 80.76% LF: 79.79% Epoch 3 / 10:
0 iter of epoch 3, loss: 0.7196
50 iter of epoch 3, loss: 0.3954
100 iter of epoch 3, loss: 1.0758
150 iter of epoch 3, loss: 0.3884
200 iter of epoch 3, loss: 0.7023
250 iter of epoch 3, loss: 0.6243
300 iter of epoch 3, loss: 0.5182
350 iter of epoch 3, loss: 0.5139 400 iter of epoch 3, loss: 0.5725 dev: loss: 0.6529 – UCM: 14.88% LCM: 13.59%
UP: 83.02% UR: 84.66% UF: 83.83%
LP: 80.62% LR: 82.20% LF: 81.40% Epoch 4 / 10:
0 iter of epoch 4, loss: 0.6344
50 iter of epoch 4, loss: 0.4191
100 iter of epoch 4, loss: 0.6893
150 iter of epoch 4, loss: 0.5572
200 iter of epoch 4, loss: 0.9136
250 iter of epoch 4, loss: 1.1216
300 iter of epoch 4, loss: 0.6391
350 iter of epoch 4, loss: 0.5298 400 iter of epoch 4, loss: 0.8414 dev: loss: 0.6712 – UCM: 15.41% LCM: 14.18%
UP: 81.03% UR: 84.04% UF: 82.51%
LP: 78.68% LR: 81.60% LF: 80.11% Epoch 5 / 10:
0 iter of epoch 5, loss: 0.4830
50 iter of epoch 5, loss: 0.5475
100 iter of epoch 5, loss: 0.7482
150 iter of epoch 5, loss: 0.9239
200 iter of epoch 5, loss: 0.3832
250 iter of epoch 5, loss: 0.6287
300 iter of epoch 5, loss: 0.4790
350 iter of epoch 5, loss: 0.4887 400 iter of epoch 5, loss: 0.5246 dev: loss: 0.6426 – UCM: 17.24% LCM: 16.00%
UP: 84.04% UR: 85.02% UF: 84.53%
LP: 81.96% LR: 82.92% LF: 82.44% Epoch 6 / 10:
0 iter of epoch 6, loss: 0.6358
50 iter of epoch 6, loss: 0.3243
100 iter of epoch 6, loss: 0.4326
150 iter of epoch 6, loss: 0.2156
200 iter of epoch 6, loss: 0.2075
250 iter of epoch 6, loss: 0.3221
300 iter of epoch 6, loss: 0.6566
350 iter of epoch 6, loss: 0.3273 400 iter of epoch 6, loss: 0.4782 dev: loss: 0.6857 – UCM: 17.71% LCM: 16.24%
UP: 85.12% UR: 84.78% UF: 84.95%
LP: 83.04% LR: 82.71% LF: 82.88% Epoch 7 / 10:
0 iter of epoch 7, loss: 0.6639
50 iter of epoch 7, loss: 0.2892
100 iter of epoch 7, loss: 0.4901
150 iter of epoch 7, loss: 0.4327
200 iter of epoch 7, loss: 0.3621
250 iter of epoch 7, loss: 0.4680
300 iter of epoch 7, loss: 0.3519
350 iter of epoch 7, loss: 0.3976 400 iter of epoch 7, loss: 0.2718 dev: loss: 0.6737 – UCM: 18.00% LCM: 16.65%
UP: 84.99% UR: 84.80% UF: 84.89%
LP: 83.04% LR: 82.86% LF: 82.95% Epoch 8 / 10:
0 iter of epoch 8, loss: 0.2797
50 iter of epoch 8, loss: 0.4043
100 iter of epoch 8, loss: 0.4003
150 iter of epoch 8, loss: 0.3425
200 iter of epoch 8, loss: 0.5250
250 iter of epoch 8, loss: 0.6620
300 iter of epoch 8, loss: 0.2005
350 iter of epoch 8, loss: 0.7108 400 iter of epoch 8, loss: 0.5317 dev: loss: 0.6942 – UCM: 18.65% LCM: 17.41%
UP: 84.95% UR: 85.95% UF: 85.45%
LP: 82.77% LR: 83.75% LF: 83.26% Epoch 9 / 10:
0 iter of epoch 9, loss: 0.5051
50 iter of epoch 9, loss: 0.7551
100 iter of epoch 9, loss: 0.5349
150 iter of epoch 9, loss: 0.3717
200 iter of epoch 9, loss: 0.3090
250 iter of epoch 9, loss: 0.5121
300 iter of epoch 9, loss: 0.8158
350 iter of epoch 9, loss: 0.4875 400 iter of epoch 9, loss: 0.5440 dev: loss: 0.6900 – UCM: 20.24% LCM: 19.00%
UP: 84.65% UR: 86.30% UF: 85.47%
LP: 82.43% LR: 84.04% LF: 83.23% Epoch 10 / 10:
0 iter of epoch 10, loss: 0.3559
50 iter of epoch 10, loss: 0.2764
100 iter of epoch 10, loss: 0.2985
150 iter of epoch 10, loss: 0.3805
200 iter of epoch 10, loss: 0.4140
250 iter of epoch 10, loss: 0.4455
300 iter of epoch 10, loss: 0.8627
350 iter of epoch 10, loss: 0.4133 400 iter of epoch 10, loss: 0.6942 dev: loss: 0.6538 – UCM: 18.76% LCM: 17.35%
UP: 84.74% UR: 86.06% UF: 85.40%
LP: 82.59% LR: 83.87% LF: 83.23%