Generalized Zero-shot ICD Coding
Authors: Congzheng Song
Shanghang Zhang
Najmeh Sadoughi
Pengtao Xie
Eric Xing
Venue:  (2020)
Keywords: Generalized Zero-shot Learning
ICD Coding
NLP
Generative Model
Deep Learning

Abstract: The International Classification of Diseases (ICD) is a list of classification codes for the diagnoses. Automatic ICD coding is in high demand as the manual coding can be labor-intensive and error-prone. It is a multi-label text classification task with extremely long-tailed label distribution, making it difficult to perform fine-grained classification on both frequent and zero-shot codes at the same time. In this paper, we propose a latent feature generation framework for generalized zero-shot ICD coding, where we aim to improve the prediction on codes that have no labeled data without compromising the performance on seen codes. Our framework generates pseudo features conditioned on the ICD code descriptions and exploits the ICD code hierarchical structure. To guarantee the semantic consistency between the generated features and real features, we reconstruct the keywords in the input documents that are related to the conditioned ICD codes. To the best of our knowledge, this works represents the first one that proposes an adversarial generative model for the generalized zero-shot learning on multi-label text classification. Extensive experiments demonstrate the effectiveness of our approach. On the public MIMIC-III dataset, our methods improve the F1 score from nearly 0 to 20.91% for the zero-shot codes, and increase the AUC score by 3% (absolute improvement) from previous state of the art. We also show that the framework improves the performance on few-shot codes.

Under review as a conference paper at ICLR 2020
GENERALIZED ZERO-SHOT ICD CODING
Anonymous authors
Paper under double-blind review
ABSTRACT
The International Classification of Diseases (ICD) is list of classification codes for the diagnoses. Automatic ICD coding multi-label text classification task with noisy clinical document inputs and extremely long-tailed label distribution, making it difficult to perform fine-grained classification on both frequent and zero- shot codes at the same time. In this paper, propose latent feature generation framework for generalized zero-shot ICD coding. where we aim to improve the prediction codes that have no labeled data without compromising the perfor- mance on seen codes. Our framework generates semantically meaningful features for zero-shot codes by exploiting ICD code hierarchical structure and novel cycle architecture that reconstructs the relevant keywords. To the best of our knowledge, this the first adversarial generative model for the generalized zero-shot learning on multi-label text classification. Extensive experiments demonstrate the effec- tiveness of our approach. On the public MIMIC-III dataset, our methods improve the F1 score from nearly O to 20.91% for the zero-shot codes, and increase the AUC score by 3% (absolute improvement) from previous state of the art.
INTRODUCTION
In healthcare facilities, clinical records classified into set of International Classification of Diseases (ICD) codes that categorize diagnoses. ICD codes are used for a wide range of purposes including billing, reimbursement, and retrieving of diagnostic information. Automatic ICD cod- ing (Stanfill et al., 2010) is in great demand as manual coding can be labor-intensive and error-prone. ICD coding is multi-label text classification task with a long-tailed class label distribution. Ma- jority of ICD codes only have a few or no labeled data due to the rareness of the discase. In the medical dataset MIMIC III (Johnson et al., 2016), among 17,000 unique ICD-9 codes, more than 50% of them never occur in the training data. It is extremely challenging to perform fine- -eeeggaidddd multi-label classification on both codes with labeled data (seen codes) and zero-shot (unseen) codes at the same time. Automatic ICD coding for both seen and unseen codes fits into the generalized zero-shot learning (GZSL) paradigm (Chao et al., 2016), where test examples are from both seen and unseen classes and we classify them into the joint labeling space of both types of classes. Nev- ertheless, current GZSL works focus on visual tasks (Xian et al., 2017: Felix et al., 2018; Liu et al., 2019). The study of GZSL for multi-label text classification is largely under-explored.
Modern automatic ICD coding models (Mullenbach et al., 2018; Rios & Kavuluru, 2018) can accu- rately assign frequent ICD codes while perform poorly zero-shot codes. To resolve this discrep- ancy, we aim to improve the predictive power of existing models on zero-shot codes by finetuning the models with synthetic latent features. To generate semantically meaningful features, we exploit the domain knowledge about ICD codes. The official ICD guidelines provide each code a short text description and a hierarchical tree structure on all the ICD codes (ICD-9 Guidelines). We propose AGMC-HTS, an Adversarial Generative Model Conditioned on code descriptions with Hicrarchical Tree Structure to generate synthetic feature. As illustrated in Figure 1, AGMC-HTS consists of generator to synthesize code-specifie latent features based on the ICD code descriptions, and discriminator to decide how realistic the generated features are. To guarantee the semantic consis- tency between the generated and real features, AGMC-HTS reconstructs the keywords in the input documents that are relevant to the conditioned codes. To further facilitate the feature synthesis of zero-shot codes, we take advantage of the hierarchical structure of the ICD codes and encourage the zero-shot codes to generate similar features with their nearest sibling code. The ICD coding models finetuned on the generated features to achieve more accurate prediction for zero-shot codes.

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Sibling code pid
labeled with I or gid
right
Real Fake
Feature Extractor
hepatic
Discriminator
artery
C
branch
Generator
zero-shot?
Keywords Reconstruction
hepatic, artery,
Label
ICD code I
code
Encoder
Z
Figure I: Overview of AGMC-HTS. The generator synthesizes features an ICD code and the discriminator
decides how realistic the input feature is. For zero-shot ICD code, the discriminator distinguishes between the generated features and the real features from the data of its nearest sibling in the ICD hierarchy. The generated features are further used to reconstruct the keywords in the input documents to preserve semantics.
The contributions of this paper are summarized follows: 1) To the best of our knowledge, we propose the first adversarial generative model for the generalized zero-shot learning on multi-label text classification. AGMC-HTS generates latent features conditioned on the code descriptions and finetunes the zero-shot ICD code assignment classifiers. 2) AGMC-HTS exploits the hicrarchical structure of ICD codes to generate semantically meaningful features for zero-shot codes without any labeled data. 3) AGMC-HTS has a novel pseudo cycle generation architecture to guarantee the semantic consistency between the synthetic and real features by reconstructing the relevant keywords in input documents. 4) Extensive experiments demonstrate the effectiveness of our approach. On MIMIC-III dataset, our methods improve the Fl score from nearly 0 to 20.91% for the zero-shot codes and AUC score by 3% (absolute improvement) from previous state of the art. We also show that AGMC-HTS improves the performance on few-shot codes with handful of labeled data.
2 RELATED WORK
Automated ICD coding. Several approaches have explored automatic assigning ICD codes on clin- ical text data (Stanfill et al., 2010). Mullenbach et al. (2018) proposed to extract per-code textual features with attention mechanism for ICD code assignments. Shi al. (2017) explored charac- ter based short-term memory (LSTM) with attention and Xie & Xing (2018) 8) applied tree LSTM with ICD hierarchy information for ICD coding. Most existing work either focused on predicting the most common ICD code or did not utilize the ICD hierarchy structure for prediction. Rios & Kavuluru (2018) proposed a neural network models incorporating ICD hierarchy information that improved the performance on the rare and zero-shot codes. The performance is evaluated in terms of the relative ranks to other infrequent codes. The model hardly ever assign rare codes its final prediction show in Section 4.2, making it impractical to deploy in real applications.
Feature generation for GZSL. The idea of using generative models for GZSL is to generate latent features for unseen classes and train classifier on the generated features and real features for both seen and unseen classes. Xian et al. (2018) proposed using conditional GANs to generate visual features given the semantic feature for zero-shot classes. Felix et al. (2018) added cycle-consistent loss on generator to ensure the generated features captures the class semantics by using linear re- gression map visual features back to class semantic features. Ni al. (2019) further improves the semantics preserving using dual GANs formulation instead of linear model. Previous works focus on vision domain where the features are extracted from rell-trained deep models on large-scale im- age dataset. We introduce the first feature generation framework tailored for zero-shot ICD coding by exploiting existing medical knowledge from limited available data.
Zero-shot text classification. Pushp & Srivastava (2017) has explored zero-shot text classification by learning relationship between text and weakly labeled tags on large corpus. The idea similar to Rios & Kavuluru (2018) in learning the relationship between input and code descriptions. Zhang et al. (2019a) introduced a two-phase framework for zero-shot text classification. An input is first determined from seen an unseen classes before the final classification. This approach does not directly apply to ICD coding as the input is labeled with a set of codes which can include both seen and unseen codes. It is not possible determine if the data is from seen unseen class.
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right
multi-label binary ICD code prediction
hepatic
label attn
f
artery
branch
Dj
91
ICD-9 code descriptions
GRNN
Aneurysm anerry
442 Aneurysm of other specified artery
442 81 Aneurysm artery neck
442.82 Aneurysin subclavian
Vj
Figure 2: ZAGRNN the feature extractor model ZAGRNN extracts label-wise features and construct embedding each ICD codes using GRNN. ZAGRNN makes a binary prediction for each code based the dot product between graph label embedding and the label specific feature.
3 METHOD
3.1
OVERVIEW
The task of automatic ICD coding is to assign ICD codes to patient's 's clinical notes. We formulate the problem multi-label text classification problem. Let L be the set of all ICD codes and L [[] given an input text, the goal is to predict E {0, } for all / E L. Each ICD code has a short text description. For example, the description for ICD-9 code 403.11 is "Hypertensive chronic kidney disease, benign, with chronic kidney disease stage V or end stage renal disease." " There is also known hierarchical tree structure on all the ICD codes: for node representing an ICD code, the children of this node represent the subtypes of this ICD code.
We focus on the generalized zero-shot ICD coding problem: accurately assigning code given that I is never assigned to any training text (i.e. 31 = 0), without sacrificing the performance on codes with training data. We assume a pretrained model as a feature extractor that performs ICD coding by extracting label-wise feature fi and predicting y by (g fi), where is the sigmoid function and 9: is the binary classifier for code For the zero-shot codes, Ii is never trained on with yr and thus at inference time, the pretrained feature extractor hardly ever assigns zero-shot codes.
We propose to use generative adversarial networks (GAN) (Goodfellow et al., 2014) to generate f with y = 1 by conditioning on code Figure shows an overview of the generation framework. The generator G tries to generate the fake feature given an ICD code description. The discrim- inator tries to distinguish between ff and real latent feature f from the feature extractor model. After the GAN trained, we use G to synthesize fi and fine-tune the binary classifier gi with for given zero-shot code 1. Since the binary code classifiers independently fine-tuned for zero-shot codes, the performance on the seen codes is not affected, achieving the goal of GZSL.
3.2 FEATURE EXTRACTOR
The pretrained feature extractor model is zero-shot attentive graph recurrent neural networks (ZA- GRNN) modified from zero-shot attentive graph convolution neural networks (ZAGCNN), which is the only previous work that is tailored towards solving zero-shot ICD coding (Rios & Kavuluru, 2018). We improve the original implementation by replacing the GCNN with GRNN and adopting the label-distributionaavy aware margin loss (Cao et al., 2019) for training. The detailed discussion of the performance gain by our modification is described Appendix E. Figure 2 shows the architecture of the ZAGRNN. At high-level, given an input ZAGRNN extracts label-wise feature fi and performs binary prediction on fi for each ICD code
Label-wise feature extraction. Given input clinical document containing words, repre- sent with a matrix X = [w1, W2..... Wx] where w; E Rd is the word embedding vector for the i-th word. Each ICD code 1 has textual description. To represent vector iy by averaging the embeddings words the description.
construct embedding
The word embedding is shared between input and label descriptions for sharing learned knowl- edge. Adjaacent word embeddings are combined using a one-dimension convolutional neural net- work (CNN) to get the n-gram text features H conv(X) E R.Nx,, Then the label-wise attention
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feature at IR for label l is computed by: 91 softmax(tanh(HH W., + bb) mz). at s H / = 1, 2,... L
where is the attention scores for all rows in H and a is the attended output of H for label I. Intuitively, dr extracts the most relevant information H about the code by using attention. Each input then has in total L attention feature vectors for each ICD code.
Multi-label classification. For each code I, the binary prediction ÿI is generated by:
rectifier (W.. aj bo);
ÿ1 = fi)
We use graph gated recurrent neural networks (GRNN) (Li al., 2015) to encode the classifier 9t- Let (1) denote the set of adjacent codes of from the ICD tree hierarchy and be the number of times we propagate the graph, the classifier 9I = gi is computed by:
o = UI hf
[VOI Jeennns of = -RUCCCl(M)
(1)
where GRUCell gated recurrent units (Chung et al., 2014) and the construction is detailed in Appendix A. The weights of the binary code classifier is tied with the graph encoded label embedding Jr so that the learned knowledge can also benefit zero-shot codes since label embedding is computed from shared word. The loss function for training is multi-label binary cross-entropy:
L
LACE 0) = Σ(v Jyr log(ū)) + - yx) log(1 - si)
(2)
As mentioned above, the distribution of ICD codes is extremely long-tailed. To counter the label imbalance issue, we adopt dis--iribbuuionnwwarrr margin (LDAM) (Cao et al., 2019), where we subtract the logit value before sigmoid function by a label-dependent margin Ay:
0/th = oو fi 1w 1)
(3)
where function 1()) outputs if 1/1 = 1 and A = C and n, is the number of training data labeled
n./4
with l and C' is constant. The LDAM loss is thus: LLUAX LeCE(y, (T)
3.3 ZERO-SHOT LATENT FEATURE GENERATION WITH WGAN-GP
For a zero-shot code I, the code label for any training data example is y1 = 0 and the binary classifier 9I for code assignment is never trained with data examples with yı due to the dearth of such data. Previous works have successfully applied GANs for GZSL in the vision domain (Xian al., 2018; Felix et al., 2018). We propose use GANs to improve zero-shot ICD coding by generating pseudo data examples in the latent feature space for zero-shot codes and fine-tuning the code-assignment binary classifiers using the generated latent features.
More specifically, we use the Wasserstein GAN (Arjovsky et al., 2017) with gradient penalty (WGAN-GP) (Gulrajani et al., 2017) to generate code-specific latent features conditioned on textual description of each code. Detail of WGAN-GP is described in Appendix B. To condition L C that maps the code description
the code description, we use a label encoder function to low-dimension vector c. We denote a C(I). The generator, G Z C - F, takes in random Gaussian noise vector E and an encoding vector e E C of a code description to generate a latent feature fi = G(z,c) c) for this code. The discriminator or critic, D F x C + R, takes in latent feature vector (either generated by WGAN-GP or extracted from real data examples) and the encoded label vector to produce a real-valued score D(S c) representing how realistic f is. The WGAN-GP loss
LNNAN =R -., (D(
(2 c))]+
X- A- (
1)
(4)
where (s)c) e the joint distribution of latent features and encoded label vectors from the set of seen code labels S, f a (1 a) f with a ~ 2( 1) and À the gradient penalty coefficient. WGAN-GP can be learned by solving the minimax problem: ming max p Lagza-
a
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Label encoder. The function C' is an ICD-code encoder that maps code description to embed- ding vector. For a code we first use LSTM (Hochreiter & Schmidhuber, 1997) to encode the sequence of M words in the description into sequence of hidden states (er C2....,, ,., We then perform a dimension-wise max- poxxppooing over the hidden state sequence to get fixed-sized encoding vector et- Finally, which is the embedding of produced by the graph encoding network. contains both the latent semantics of the description (in et) as well as the ICD hierarchy information (in g:).
obtain the eventual embedding c eilgg of code / by concatenating with
To ensure the generated feature vector St captures the semantic meaning of code 4, we encourage ft to be able to well reconstruct the keywords extracted from the clinical notes associated with code I.
Keywords reconstruction loss.
For each input text labeled with code I, extract the label-specific keyword set K₁ {wi, Wg..... w as the set of most similar words in to I, where the similarity is measured by cosine similarity between word embedding in æ and label embedding vg. Let Q be a projection ma- trix, K. be the set of all keywords from all inputs and .T.:,) denote the cosine similarity function, the loss for reconstructing keywords given the generated feature as following:
LxyY = log P(Kilf)) ==
Σ mEEK;
(Wkk, 01) - log P(wx
exp(w Q.1i)
Σ WAEK
π Twwk 01) . log
(5)
Ewek exp(wT Qfi)
Discriminating zero-shot codes using ICD hierarchy. In the current WGAN-GP framework, the discriminator cannot be trained on zero-shot codes due to the lack of real positive features. In order to include zero-shot codes during training, we utilize the ICD hierarchy and use fath, the latent feature extracted from real data of the nearest sibling 1ib of a zero-shot code I, for training the discriminator. The nearest sibling code is the closest code to that has the same immediate parent. This formulation would encourage the generated feature siblings of and thus can better preserving the ICD hierarchy. More formally, let c0 = C(1ss)). we propose the following modification to LAGAN for training zero-shot codes:
be close the real latent features of the
LAGAN-Z PE iiT (c, cbib) D(fais, c)1 - E (f.c) (T(c, D(f,c))+
(6) where ~ PG is the distribution of encoded label vectors for the set of zero-shot codes U and (..) ~ PV is defined similarly in Equation 4. The loss term by the cosine similarity <(c, cه to prevent generating exact nearest sibling feature for the zero-shot code After adding zero-shot codes to training, our full learning objective becomes:
A E (f.) [IIVD(f e)ll - 1)²1
min max LwasA + LwaaN-Z LREY
where / is the balancing coefficient for keyword reconstruction loss.
Fine-tuning on generated features. After WGAN-GP is trained, we fine-tune the pretrained classi- fier from baseline model with generated features for given zero-shot code l. We use the generator to synthesize a set of fi and label them with = 1 and collect the set of fı from training data with yi 0 using baseline model feature extractor. We finally fine-tune 9i on this set of labeled feature vectors to get the final binary classifier for given zero-shot code l.
EXPERIMENTS
4.1 SETUP
Dataset description. We use the publicly available medical dataset MIMIC-III (Johnson et al., 2016) for evaluation, which contains approximately 58,000 hospital admissions of 47,000 patients who stayed in the ICU of the Beth Israel Deaconess Medical Center between 2001 and 2012. Each admission record has discharge summary that includes medical history, diagnosis outcomes, sur- gical procedures, discharge instructions, etc. Each admission record is assigned with set of most
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Table 1: Results on seen codes using ZAGRNN feature extractor described in Section 3.2
Micro
Macro
Method
Pre
Rec
F1
AUC
Pre
Rec
F1
AUC
ZAGRNN
58.06
44.94 47.14
50.66
96.67
30.91
25.57 28.06
27.99 29.78
94.03
ZAGRNN LIAAM
56.06
51.22
96.70
31.72
94.08
Table 2: Zero-shot ICD coding results. Scores are averaged over 10 runs different seeds.
Micro
Macro
Method
Pre
Rec
F1
AUC
Pre
Rec
F1
AUC
ZAGRNN
0.00
0.00
0.00
89.05
0.00
0.00
0.00
90.89
ZAGRNN + LIDAM
0.00
0.00
0.00
90.78
0.00
0.00
0.00
91.91
ZAGRNN + Meta (Liu et al., 2019)
46.70
0.89
1.74
90.08
3.88
0.95
1.52
91.88
Duznn (Xian et al., 2018)
23.92
17.63
20.30
91.94
17.30
17.38
17.34
92.26
Curzn + Lets (Xian et al., 2018)
23.57
16.55
19.44
91.71
18.39
16.81
17.56
92.32
LINZA Leve (Felix al., 2018)
23.97
17.93
20.51
91.88
17.86
17.83
17.84
92.27
DWEAR-2 + CelS
22.49
17.40
19.62
91.80
16.56
17.26
16.90
92.16
LMZAM-2 + Leve
21.44
17.24
19.11
91.90
16.05
17.06
16.54
92.25
DUSAN Caey (Ours)
23.26
18.24
20.45
91.73
17.09
18.38
17.71
92.21
LUGAR-2 (Ours)
22.18
19.03
20.48
91.79
16.87
18.84
17.80
92.28
LMGAN-2 + LeEY (Ours)
22.54
19.51
20.91
92.18
17.70
19.15
18.39
92.34
relevant ICD-9 codes by medical coders. The dataset is preprocessed as in (Mullenbach al., 2018). Our goal is to accurately predict the ICD codes given the discharge summary.
We split the dataset for training, validation, and testing by patient ID. In total have 46,157 discharge summaries for training, 3,280 for validation and 3,285 for testing. There are 6916 unique ICD-9 diagnosis codes MIMIC-II and 6090 of them exist in the training set. We use all the codes for training while using codes that have more than 5 data examples for evaluation. There are 96 out of 1,646 and 85 out of 1,630 unique codes are zero-shot codes in validation and test set, respectively.
Baseline methods. We compare our method with ZAGRNN modified from previous state of the art approaches on zero-shot ICD coding (Rios Kavuluru, 2018) as described in Section 3.2, meta meta-embedding for long- tailed problem (Liu et al., 2019) and WGAN-GP with classification loss Less (Xian al., 2018) and with cycle-consistent loss Leve (Felix et al., 2018) that were applied to GZSL classification in computer vision domain. Detailed description and hyper-parameters of bascline methods are in Appendix C.
Training details. For WGAN-GP based methods, the real latent features are extracted from the final layer in the ZAGRNN model. Only features fi for which y = are collected for training. We use single-layer fully-co malle-connected network with hidden size 800 for both generator and discriminator. For the deseriisttntonn encoder LSTM, we set the hidden size to 200. We train the discriminator iterations per each generator training iteration. We optimize the WGAN-GP with ADAM (Kingma & Ba, 2015) with mini-batch size 128 and learning rate 0.0001. We train all variants of WGAN-GP for 60 epochs. We the weight of LetS to 0.01 and Gexe:fxum to 0.1. For LEEY, we predict the top 30 most relevant keywords given the generated features.
After the generators are trained, we synthesize 256 features for each zero-shot code / and fine-tune the classifier Si using ADAM and set the learning rate to 0.00001 and the batch size to 128. We fine-tune on all sero codes and select the best performing model on validation set and evaluate the final result on the test set.
4.2 RESULTS
Evaluation metrics. We report both the micro and macro precision, recall, Fl and AUC scores on the zero-shot codes for all methods. Micro metrics aggregate the contributions of all codes to compute the average score while macro metrics compute the metric independently for each code and then take the average. All scores averaged over 10 runs using different random seeds.

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455.51
192e
4b5.51
192e
515.11
19261
518.51
09261
574.00
058.23
574.00
9:8.23
230.07
996.1
730.07
596.81
(a) LUSAN
(b) LuSaN-7
Figure 3: T-SNE projection generated features for zero-shot codes using (a) Cucus and (b) Lwara-z. Lighter color are projection of generated features and darker color are of real features from the nearest sibling codes. Features generated for zero-shot codes using LuSAN-z are closer to the real features from the nearest sibling codes. This shows that using LaGAx-2 generate features that better preserve the ICD hierarchy.
Results on seen codes. Table shows the results of ZAGRNN models on all the seen codes. Note that fine-tuning the zero-shot code classifiers with meta-embedding WGAN-GP will not affect the classification for seen codes since the code assignment classifiers are independently fine-tuned.
Results zero-shot codes. Table 2 summarizes the results for zero-shot codes. For the base- line ZAGRNN and meta-embedding models, the AUC on zero-shot codes is much better than ran- dom guessing. LISRM improves the AUC scores and meta-embedding scores. However, since these methods never train the binary classifiers for zero-shot codes on pos- itive examples, both micro and macro recall and Fl scores close to zero. In other words, these models almost never assign zero-shot codes at inference time. For WGAN-GP based methods, all the metrics improve from ZAGRNN and eeetaeembedinng except for micro precision. This due to the fact that the binary zero-shot classifiers are fine-tuned on positive generated features which drastically increases the chance of the models assigning zero-shot codes.
achieve slighter better F1
Ablation studies on WGAN-GP methods. We next examine the detailed performance of WGAN- GP methods using different losses as shown Table Adding Leus hurts the micro metries, which might be counter-intuitive at first. However, since the Let. is computed based on the pretrained classifiers, which are not well-generalized on infrequent codes, adding the loss might provide bad gradient signal for the generator. Adding Levc, LREY and CWGAN-Z improves LEGAN and achieves comparable performances in terms of both micro and macro metrics. At a closer look, CwEAN improves the recall most, which matches the intuition that learning with the sibling codes enables the model to generate more diverse latent features. The performance drops when combing LuGAN-Z with Lets and Lerg- We suspect this might be due conflict of optimization that the generator tries to synthesize ff close the sibling code fsúb and simultancously maps f back to the exact code semantic space of l. Using Lxey resolves the conflict as it reconstructs more generic semantics from the words instead of from the exact code descriptions. Our final model that the combination of LwaAN- 2 and LREY achieves the best performance on both micro and macro F1 and AUC score.
T-SNE visualization of generated features. We plot the T-SNE projection the generated features for zero-shot codes using WGAN-GP with Cxcan and LwEAN-2 in Figure 3. Dots with lighter color represent the projections of generated features and those with darker color correspond to the real features from the nearest sibling codes. Features generated for zero-shot codes using Lwanx-z are closer to the real features from the nearest sibling codes. This shows that using LuGAN-z can generate features that better preserve the ICD hierarchy.
Keywords reconstruction from generated features. We next qualitatively evaluate the generated features by examining their reconstructed keywords. We first train a keyword predictor using Cany on the real latent features and their keywords extracted from training data. Then we feed the gener- ated features from zero-shot codes into the keyword predictor to get the reconstructed keywords.
Table 3 shows some examples of the top predicted keywords for zero-shot codes. Even the keyword predictor is never trained on zero-shot code features, the generated features are able find relevant words that are semantically close to the code descriptions. In addition, features generated with Lucas-z can find more relevant keywords than Lxcau- For instance, for zero-shot code V10.62, the

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Keywords found by generated features using Lucan and Lwaan for zero-shot ICD-9 codes. Bold words are the most related ones to the ICD-9 code description.
Table 3:
Code
Description
Keywords from CyeAM
Keywords from Lweax-2
V10.62
Personal history of mycloid leukemia
AICD, inferoposterior, diogenic, leukemia, silent
leukemia, Zinc, myeloge- nous, CML, metastases
E860.3
Accidental poisoning iso- propyl alcohol
apneie, pulses, choking, sub- stance, fractures
intoxicated, alcoholic, AST, EEG, alcoholism
956.3
Injury to peroneal nerve
vault, injury, pedestrian,
injuries, neurosurgery. in- jury, TSICU, coma
thopedics, TSICU
851.05
Cortex contus-deep coma
contusion, injury, trauma, neurosurgery. head
brain, head, contusion, rosurgery. intracranial
772.2
Subarachnoid hemorrhage of fetus or newborn
subarachnoid, SAH, neuro- surgical, screening
subarachnoid, Snharrachhoidhemorrhaaeee hemorragg,, SAH. newborn, pregnancy
Table 4: Few-shot ICD coding results. Scores are averaged over 10 on different seeds.
Micro
Macro
Method
Pre
Rec
Fl
AUC
Pre
Rec
F1
AUC
ZAGRNN
64.00
1.27
2.48
92.11
4.15
1.23
1.90
90.99
ZAGRNN + -LDDM
60.53
1.82
3.53
92.10
6.29
1.80
2,80
90.74
ZAGRNN + Meta (Liu et al., 2019)
48.88
6.75
11.84
92.15
16.65
6.77
9.62
90.92
Lusan (Xian al., 2018)
29.18
18.14
22.37
92.59
20.76
18.09
19.33
90.99
Duzan + Lets (Xian et al., 2018)
29.18
17.67
22.01
92.54
19.88
17.62
18.68
91.01
Lusan + Leve (Felix et al., 2018)
28.82
18.43
22.48
92.57
20.39
18.28
19.28
90,96
LuSAN- + Leus
27.97
17.70
21.68
92.58
20.18
17.59
18.80
91.01
LUSAR-E + Love
28.40
18.24
22.22
92.61
20.82
18.17
19.40
90,99
LUGAR + LREY (Ours)
28.97
18.31
22.44
92.62
20.92
18.24
19.49
91.05
LWESA-E (Ours)
27.66
18.81
22.39
92.56
20.45
18.81
19.59
90,97
CMEAN-E LEEY (Ours)
27.95
18.96
22.60
92.63
21.55
18.92
20.15
91.00
top predicted keywords from LaGAN z include leukemia, myelogenous, CML (Chronic myelogenous leukemia) which are all related to myeloid leukemia, a type of cancer of the blood and bone marrow.
Results on few-shot codes. As we have our feature generation framework on few-shot ICD codes, where the number training data for such codes less than equal to 5. We apply the exact same setup zero-shot codes for synthesizing features and fine-tuning classifiers for few-shot codes. There are 220 and 223 unique few-shot codes in validation and test set, respectively.
promising results zero-shot codes, also evaluate
Table 4 summarizes the results. The performance of ZAGRNN models on few-shot codes is slightly better than zero-shot codes yet the recall are still very low. Meta-embedding can boosts the recall and Fl scores from baseline models. WGAN-GP methods can further boosts the performance on recall, Fl and AUC scores and the performance using different combination of losses generally follows the pattern in zero-shot code results. In particular, our final model trained with LeCAR-2 and Cxey can perform slightly better than other WGAN-GP models in terms of F1 and AUC scores.
5 CONCLUSION
We introduced the first feature generation framework, AGMC-HTS, for generalized zero-shot multi- label classification in clinical text domain. We incorporated the ICD tree hierarchy to design GAN models that significantly improved zero-shot ICD coding without compromising the performance on seen ICD codes. We also qualitatively demonstrated that the generated features using framework can preserve the class semantics as well as the ICD hierarchy compared to existing feature generation methods. In addition to zero-shot codes, showed that our method can improve the performance on few-shot codes with limited amount of labeled data.
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Under review as a conference paper at ICLR 2020
REFERENCES
Martin Arjovsky, Soumith Chintala, and Léon Bottou. arXhv:1701.07875 2017.
Wasserstein gan. arXiv preprint
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. Learning imbalanced datasets with aibelddistrbuttin--wware margin loss. In NeurIPS, 2019.
Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, and Fei Sha. An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In ECCV, 2016.
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks sequence modeling. arXiv preprint arXiv:1412.3555, 2014.
Rafael Felix, Vijay BG Kumar, Ian Reid, and Gustavo Carneioo. Multi-modal cycle-consistent generalized zero-shot learning. In ECCV, 2018.
lan Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. NeurlPS, 2014.
Ishaan Guirajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. Im- proved training of wasserstein gans. NeurlPS, 2017.
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 1997.
ICD-9 Guidelines. International classification of nista revision, clinical modification (ICD-
9-CM). https: /wwwwwwwwooommmm/.//o cdc. gorlcc..oo//ciiioo hwt.///////////oo/ooeeooeeeeeee/ ctdii///ieee icddcmmmmm.mmmm..m....mmm..mm. htm.
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng. Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, a freely accessible critical care database. Scientific data, 2016.
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015.
Yujia Li, Daniel Tarlow, Mare Brockschmidt, and Richard Zemel. Gated graph sequence neural networks. arXiv preprint
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Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu. Large- scale long-tailed recognition in an open world. In CVPR, 2019.
James Mullenbach, Sarah Wiegreffe, Jon Duke, Jimeng Sun, and Jacob Eisenstein. Explainable prediction of medical codes from clinical text. NAACL, 2018.
Jian Ni, Shanghang Zhang, and Haiyong Xie. Dual adversarial semantics-consistent network for generalized zero-shot learning. In NeurlPS, 2019.
Pushpankar Kumar Pushp and Muktabh Mayank Srivastava. Train once, test anywhere: Zero-shot learning for text classification. arXiv preprint arXiv: 2017.
Anthony Rios and Ramakanth Kavuluru. Few-shot and zero-shot multi-label learning for structured label spaces. EMNLP, 2018
Haoran Shi, Pengtao Xie, Zhiting Hu, Ming Zhang, and Eric P Xing. Towards automated icd coding using deep learning, arXiv preprint 1rXiv:1711.04000 2017.
Mary H Stanfill, Margaret Williams, Susan H Fenton, Robert A Jenders, and William R Hersh. A systematic literature review of automated clinical coding and classification systems. JAMIA, 2010.
Yongqin Xian, Bernt Schiele, and Zeynep Akata. Zero-shot learning-the good, the bad and the ugly. In CVPR, 2017.
Yonggin Xian, Tobias Lorenz, Bernt Schiele, and Zeynep Akata. Feature generating networks for zero-shot learning. In CVPR, 2018

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Pengtao Xie and Eric Xing. A neural architecture for automated ied coding. In ACL, 2018.
Jingqing Zhang, Piyawat Lertvitaayakumjorn, and Yike Guo. Integrating semantic knowledge to tackle zero-shot text classification. In ACL, 2019a.
Yijia Zhang, Qingyu Chen, Zhihao Yang. Hongfei Lin, and Zhiyong Lu. Biowordvec, improving biomedical word embeddings with subword information and mesh. Scientific data, 2019b.
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Under review as a conference paper at ICLR 2020
A APPENDIX: GATED RECURRENT UNITS
Below is the detailed construction of GRUCel1 in equation 1 from Section 3.2: 2 o(W, h/ + U2 96 + b,)
rf oW, hi + Ur ot 1-1 b,) of (1 = gf of @ tanh(W,, h + Un (r) 2 -) + bn)
where @ is the dimension-wise multiplication.
B APPENDIX: GENERATIVE ADVERSARIAL NETWORKS
GANs (Goodfellow et al., 2014) have been extensively studied for generate highly plausible data. The idea of GAN train a generator and discriminator through a minimax game. The generator takes in random noise and generate fake data to fool the discriminator while the discriminator tries to distinguish between generated data and real data. The training procedure of GANs can be unstable, thus Arjovsky et al. (2017) proposes Wasserstein-GAN (WGAN) to counter the instability problem by optimizing the Wasserstein distance instead of the original Lenson-Shanme divergence. Gulrajani et al. (2017) further improves WGAN by using gradient instead of weight clipping for the required I-Lipschitz constraint in WGAN discriminator.
C APPENDIX: MORE TRAINING DETAILS
ICD-9 code information. We extract the ninth version of the ICD code descriptions and hierarchy from the CDC website In addition to the official description, we extend the descriptions with medical knowledge, including synonyms and clinical information, crawled from online resources?.
ZAGRNN. For the ZAGRNN model, we use 100 convolution filters with filter size of 5. We use 200 dimensional word vectors pretrained on PubMed corpus' (Zhang et al., 2019b). We use dropout on the word embedding layer with rate 0.5. We the ADAM (Kingma & Ba, 2015) for optimization with a minibatch size of 8 and a learning rate of 0.001. The final feature size and GRNN hidden layer size are both set to 400. We train the ZAGRNN model for 40 epochs.
Meta-embeddingg Liu et al. (2019) proposed meta-embedding for solving large long-tail problem by transferring knowledge from head classes to tail classes. The method naturally fits ICD coding due to the long-tailed code distribution. To apply meta-embedding in ICD coding, we first construct set of centroids M as the mean of fi for each code from the training data. Let @ denote dimension- wise multiplication, then the meta-embedding for f is calculated as:
fmata f + e(f) @ (o(f)T -M)
where off) is the attention scores for selecting centroids M and e(f) is dimension-win wise coefficient for selecting the attended features. Both and r are parameterized as neural networks and are learned during fine-tuning. The final classification performed by ÚI a I fmete).
For meta eebedddnbbdddi, we fine-tune the neural network modules and o using ADAM and set learning rate to 0.0001 and batch size 32.
WGAN-GP with classification loss. Xian et al. (2018) proposed to add cross-entropy loss during training WGAN-GP to generate features being correctly classified as conditioned labels. In ICD coding, this loss translates to enforcing being classified as positive for code I:
Lets = - log P(y = 1lf) = - log (g. fi)
WGAN-GP with cycle consistency loss. Similar to adding LetS prevent the generated features being random, Felix et al. (2018) proposed to add a loss that constrains the synthetic representations to generate back their original semantic features. Let R : F - C be linear regression estimate the label embedding e; from the generated feature fi, the cycle consistency loss is defined as:
Leve ||q
(10)
http:////d///////e// htm Phttp: iddddtta.com 'https://githon com/ coom//cbinn11e http:///ooooooveeee.oo.. owordvec
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Under review as a conference paper at ICLR 2020
D APPENDIX: PRECISION-RECAM CURVES
1.0
Baseline: AUC-PR=0.095
Baseline: AUC- -UCC-PR=0.133
0.8
Ours: AUC-PR=0.129
0.8
Ours: AUC-PR -UC-PR=0.151
Precision
Precision
0.6
0.6
0.4
0.4
0.2
0.2
0.0.0
0.2
0.4
0.6
0.8
1.0
0.0.0
0.0
0.2
0.4
0.6
0.8
1.0
Recall
Recall
(a) Zero-shot codes
(b) Few-shot codes
Figure 4: Micro averaged precision-recall curves comparisons on zero-shot codes (a) and few-shot codes (b). Red line is the haseline feature extractor described in Section 3.2 and blue line is from our final model. The legend denotes the correspond area under the curve (AUC) value.
We plot the precision-recall curve with micro averaged scores for previous state the ICD coding model and ours on few-shot codes and zero-shot codes. As demonstrated in Figure 4, our model can achieve better precision and recall trade-off and also higher area under the curve scores for both zero-shot codes and few-shot codes.
E COMPARISON OF FEATURE EXTRATORS ON SEEN ICD CODES
Table 5: Results all the seen codes using different feature extractors described in Section 3.2
Micro Rec
Macro
Method
Pre
F1
AUC
Pre
Rec
F1
AUC
ZAGCNN (Rios Kavuluru, 2018)
58.29
44.64
50.56
96.59
30.00
24.65
27.06
94.00
ZAGRNN (ours)
58.06
44.94
50.66
96.67
30.91
25.57
27.99
94.03
ZAGRNN LIDON (ours)
56.06
47.14
51.22
96.70
31.72
28.06
29.78
94.08
We compare the performance of our modified ZAGRNN with the original ZAGGNN proposed by Rios & Kavuluru (2018) seen ICD codes in Table 5. Discussion of the modification is de- tailed in Section 3.2. With ZAGRNN, almost all metrics slightly increased from ZAGCNN except for micro precision. With LIOAM loss, our final feature extractor can improve more significantly from ZAGCNN especially for macro metrics and achieve better precision recall trade-off. These modifi- cations not enough to get reasonable performance zero-shot codes shown in Table 2, mainly due to the lack positive example for zero-shot codes during training.
12


Reviews:
[{"review_id": "rJeTy-FAYS", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper addresses the problem of zero-shot prediction for ICD codes for medical notes. The main idea of the paper is to use GANs to generate latent features for the text conditioned on the ICD codes and taking into account the tree-like structure of the ICD codes themselves. The classification of ICD codes is trained on the GAN-extracted features.\n\nThe input clinical document is represented as a concatenation of its word embeddings. The representation of an ICD code is realized by averaging the embeddings of the words in its corresponding description. The feature extraction model takes the word embeddings from the input text and passes them through a CNN; in addition, attention is used for the encodings of all the ICD codes.\n\nFor further encoding the ICD codes, the tree-like organization of the codes is exploited by using GRNNs. The generated features together with the graph-encodings of the ICD codes are used to classify the clinical text.\n\nSome suggestions for improvement:\n\n* It was not clear the relationship with previous work by Rios and Kavuluru. I had to refer to the paper to be able to appreciate the differences. The feature extraction network seems to be similar (Rios & Kavuluru used GCNNs instead of GRNNs to encode the ICD codes). The GAN-based feature generation seems to be new in this paper. \n\n* I did not understand the comment regarding the independent training of the zero-shot cases vs the rest.\n\n* Siblings ICD codes mean codes that share an immediate parent?\n\n* Table 1. seems to cite the related work results, but it doesn't seem to include the results of the proposed method.\n\n* In Table 2, for the cases where precision and recall are 0, are the AUC numbers correct? Table 2 also shows the results for the ablation studies that are discussed later in the section. It took me a while to understand the difference between Table 2 and Table 4.\n\n* For Table 4, you could add the explanation on what you considered few-shot cases for those results. \n\nI think the paper could improve the explanation of the components of the model and the presentation of the evaluation results.\n\nI think the idea of using GANs to generate features that can help with the classification + taking advantage of the relationship/structure among the ICD codes are both interesting ideas.", "extra_scores": {"experience_assessment": "I have published one or two papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}, {"review_id": "Sygm8F2p5S", "reviewer": "AnonReviewer3", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper proposes a method to do zero-shot ICD coding, which comes down to determining which elements from a set of natural language labels (ICD codes) apply to a given text (diagnostic summary). The main problem is that many codes have zero or very few examples. The proposed solution for this problem is to learn a feature-space generator for examples which can be conditioned on a code. This generator is trained using a GAN, which moves the few-/zero-shot problem to training the discriminator. Here, the tree structure of the ICD codes is used, by using examples with sibling labels as approximate examples for codes with few or on labelled data points. Additionally, a keyword reconstruction loss is used, based on the idea that the keywords of the corresponding ICD code can be reconstructed from a good feature vector.\n\nThe paper is written and executed well; the setup, which involves a fair number of components, is described and motivated clearly. The experiments include comparisons to state of the art results, finding that applying the GAN-based technique they choose (introduced in Xian et al., 2018 for computer vision problems) produces significant gains in recall of low-data classes (few or zero examples). The precision decreases, probably due to a shift from false negatives to false positives, while the AUC shows modest gains.\n\nThe main potential issue with the paper is the degree of (effective) novelty. The bulk of the gain relative to the SotA seems to be achievable by applying the GAN-based method, which is described in the Xian et al. (2018) reference, or by applying the label-distribution-aware margin, which are known techniques even if they have not been applied to this particular dataset yet. The additional elements introduced by the authors - the use of sibling codes and the keyword reconstruction loss - are good ideas, and it is worthwhile having a documented test of the benefit they provide, but they don’t seem to have a major influence on the quality of the model.\n\nAll in all, I would argue for the paper to be accepted. The work done here is valuable to have on record, and the presentation and execution are well done.\n\nTwo questions for the authors:\n\n1. Could the model benefit from having a self-attention model, like a transformer? This applies mostly to the diagnostic text encoder, as interpreting the ICD code descriptions seems not to depend strongly on structure or context. From the text of the paper it appears that a 1D convolution was used to process the diagnostic texts, but I would expect that longer-distance links between words can be quite relevant there.\n\n2. Could the precision-recall curves be added to the supplementary material?", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "HkxLy93pFB", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The paper deals with the problem of text classification when the number of class is large (17000) and most of the classes do not have examples in the training set. This problem is known as Zero-shot learning. The paper proposes to use adversarial methods and the hierarchical organisation of the classes to improve current models.\n\nThe paper lacks a clear description of the complete system : Figure 1 seems to be the one but there is no mention of the classification part. The description of each bloc is clear enough independently  but many question remains on the global picture. \n\nThe author should for example emphasis the fact that in their problemn the classes come with a short text  description. This is somehow unususal for a text classification problem where usually the classes are not defined by a description. In the figure, the difference of the processing of the text from the clinical document and from the class description is not clear. \n\n3.1 is related to feature extraction for the clinical document, but it is also said that this processing is also apply to class description.\n\n3.2 label encoder : if the notations were the same as on figure A, it would help understanding.\n\nResults : the claim \"our methods improve the F1 score from nearly 0 to 20.91% for the zero-shot codes\" is not true : state-of-the-art models such as Xian2018 and Felix2018 are already over 20% F1. \n \nWhen looking at Figure 3 and the analysis, WGAN-Z seem to provide better representation that WGAN with it has almost no impact on the classification results (F1 20.48 versus F1 20.30)\n\nIn conclusion, it seems that the proposed methods make the model more complex without bringing a significant improvements.", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper proposes a method to do zero-shot ICD coding, which involves assigning natural language labels (ICD codes) to input text. This is an important practical problem in healthcare, and it is not straightforward to solve, because many ICD codes have none or very few training examples due to the long distribution tail. The authors adapt a GAN-based technique previously used in vision to solve this problem. All of the reviewers agree that the paper is well written and well executed, and that the results are good. However, the reviewers have expressed concerns about the novelty of the GAN adaptation step, and left this paper very much borderline based on the scores it received. Due to the capacity restrictions I therefore have to recommend rejection, however I hope that the  authors resubmit elsewhere. 
Decision: Reject