Variational Template Machine for Data-to-Text Generation
Authors: Rong Ye
Wenxian Shi
Hao Zhou
Zhongyu Wei
Lei Li
Venue:  (2020)

Abstract: How to generate descriptions from structured data organized in tables? Existing approaches using neural encoder-decoder models often suffer from lacking diversity. We claim that an open set of templates is crucial for enriching the phrase constructions and realizing varied generations.Learning such templates is prohibitive since it often requires a large paired <table,description>, which is seldom available. This paper explores the problem of automatically learning reusable "templates" from paired and non-paired data. We propose the variational template machine (VTM), a novel method to generate text descriptions from data tables. Our contributions include:  a) we carefully devise a specific model architecture and losses to explicitly disentangle text template and semantic content information, in the latent spaces, and b) we utilize both small parallel data and large raw text without aligned tables to enrich the template learning. Experiments on datasets from a variety of different domains show that VTM is able to generate more diversely while keeping a good fluency and quality. 

Published as a conference paper at ICLR 2020
VARIATIONAL TEMPLATE MACHINE FOR DATA-TO- TEXT GENERATION
Rong Ye Wenxian Shi, Hao Zhou, Zhongyu Wei", Lei Li
FFudan University
{rye18, zywei 3 fudan. edu. en
ByteDance AI Lab
{shiwenxian, zhouhao. nnp, lileilable bytedayce.cco... com
ABSTRACT
How to generate descriptions from structured data organized in tables? Exist- ing approaches using neural encoder-decoder models often suffer from lacking diversity. We claim that an open set of templates is crucial for enriching the phrase constructions and realizing varied generations. Learning such templates is prohibitive since it often requires large paired <table, descris iirtttta pon> corpus, which is seldom available. This paper explores the problem of automati- cally learning reusable "templates" from paired and non non-paired data. We propose the variational template machine (VTM), novel method to generate text descrip- tions from data tables. Our contributions include: a) carefully devise specific model architecture and losses to explicitly disentangle text template and semantic content information in the latent spaces, and b) we utilize both small parallel data and large raw text without aligned tables to enrich the template learning. Exper- iments on datasets from variety different domains show that VTM is able generate more diversely while keeping good fluency and quality.
INTRODUCTION
Generating text descriptions from structured data (data-to-text) is an important task with many prac- tical applications. Data-to-text has been used to generate different kinds of texts, such as weather re- ports (Angeli al.) 2010), sports news (Mei al., 2016)] Wiseman et al. 2017) and biographies (Le- bret al. 201 6 Wang et al. 2018b Chisholm et al. | 2017). Figure gives an example of data-to-text task, which takes an infobox as the input and outputs a brief description of the information the table. There are several recent methods utilizing neural encoder-decoder frameworks to generate text description from data tables (Lebret et al. 2016 Bao et al. 2018 Chisholm et al. 2017 Liu al. 2018).
Although current bible-to-texx models could generate high quality sentences, the diversity these output sentences not satisfactory. We find that templates erucial in increasing the variations of sentence structure. For example, Table [ gives three descriptions with their templates for the given table input. Different templates control the sentence arrangement, thus vary the generation. Some related work (Wiseman et al. 2018 Dou et al., 2018) employs hidden semi-Markov hidden model extract templates from table-tex pairs.
We argue that templates can be better considered for generating more diverse outputs. First, it is non-trivial to sample different templates for obtaining different output utterances. Directly adopting variational auto-encoders (VAEs, Kingma & Welling (2014) in table-to-text only enables to sample in the latent space. However. VAES always generate irrelevant outputs, which may change the table content instead of sampling templates. This may harm the quality of output sentences. To address the above problem, if we can directly sample in the template space, we may get more diverse outputs while keeping the good quality of output sentences.
Work done while Rong Ye was a research intern at ByteDance Lab. An infobox is table containing attribute-value data about certain subject. It mostly used on Wikipedia
pages.

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Table:
(ameenameeVrriable)) eatType[ppbb) food[Japanese], priceeRangejavoragel] custemerRattigglow]] area(riverside]
Templatel:
name] is a [Tood] restaurant, it is [eat[ype and it has an [priceRangel cost and [customerRating] rating, it is in [area].
Sentence1:
name nameVariable Variable is a Japanese restaurant. is a pub and it has an average cost and low rating. is in riverside.
Template2:
name] has an [priceRangel price range with |eustomerRatingl rating, and [name] is an [food] [eat- Type] [area].
Sentence2:
name Variable has average price range with low rating, and name Variable is an Japanse pub in riverside.
Template3:
[nmee] [ealType] with [customerRating] rating and pprieRangeel cost, it [food] restaurant and [name] is in [arca].
Sentence3:
name Variable pub with low rating and average cost, it is Japanese restaurant and name Variable is in riverside.
Table 1: An example: generating sentences based on different templates.
Second, we can hardly obtain promising sentences by sampling in the template space, if the template space is less informative. Namely, either encoder-decoder models or VAE-based models requires abundant parallel table-text pairs during the training. In such case, constructing high-quality parallel dataset is often labor-intensive. With limited table-sentence pairs, a VAE model cannot construct an informative template space. How to fully utilize raw sentences (without aligned table) enrich the latent template space is under study.
In this paper, to address the above problems, we propose the variational template ma- chine (VTM) for data-to-text generation, which enables to generate sentences with diverse templates while preserving the high quality. Particularly, we introduce two latent variables, representing tem- plate and content, to control the generation. The two latent variables are disentangled, and thus we can generate diverse outputs by directly sampling in the latent space for template. Moreover, we propose novel approach for semi- superviee learning in the VAE framework, which could fully exploit the raw sentences for enriching the template space. Inspired by back trak-translation (Sennrich) et al. 2016) Burlot & Yvon 2018; Artetxe et al. 2018)), we design a variational back-translation process. Instead of training a sentence-to-table backward generation model directly, we take the variational posterior of the content latent variable as the backward model to help to train the for- ward generative model. Auxiliary losses are introduced to ensure the learning of meaningful and disentangled latent variables.
Experimental results on Wikipedia biography dataset (Lebret et al,, 2016 and sentence planning NLG dataset (Reed et al.) 2018) show that model can generate texts with more diversity while keeping a good fluency. Training together with large amount of raw text, VTM can further improve the generation performance. Besides, VTM is more predominant in the case where sentence-to-table backward model is hard to train. Ablation studies also demonstrate the effects of the auxiliary losses on the disentanglement of template and content spaces.
PROBLEM FORMULATION AND NOTATIONS
As data-to-text task, we have table-text pairs D {(x, y.)) where x; is the table, and y is the output sentence.
Following the description scheme of Lebret al. (201)). table can be viewed as set of K records of field- poadd-posiio--alllee triples, I.e., x = (f. J. v); fimm index of value in the field f. For example, corresponding records: (Name, 1, John) and (Name, 2, Lennon). For each triple, we first embed field, position and value d-dim vectors Ep, Ep,ey, E Rd. Then, the de-dim representation of the record is obtained by h; tanh Banh(W(e,: (ey; Op. ex)"" +b), i i= 1...,, where W Rdxx3d x3d and b Rd. are parameters. The final representation of the table, denoted as (x), is obtained by max-pooling over all field-pone pelddposition-vallle triple records,
k where f is the field and p is the item "Name: John Lennon" is denoted as two
fene(x) = = MaxPool, (h,; I...K..
In addition to the table-text pairs, we also have raw texts without table input, denoted as D, = (ye 1- It usually has M > N.
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Data Type
Structured Data (Source)
Descriptive Text (Target)
Chris
Bow
business
Edverten
executive and
Table-tett pains
bend
y
Granding several Stiroa
Valley technology stampss
including ons bosed on poer lendin..
(1910-19077
was Roman Cachelic sun devoted life
Figure The graphical model of VTM: is the latent variable from template space, and c is the content variable, a is the corresponding table for the table- text pairs. y is the observed sentence. The solid lines depict the generative model and the dashed lines form the in- ference model.
Rew text
Not provited
servingthe poorard destitate around the wurld.
Figure I: Two types of data in the data-to-text task: Row 2 presents an example table-text pairs; Row shows sample of raw text, whose table imput is missing and only sentence is provided.
3 VARIATIONAL TEMPLATE MACHINE
As shown the graphical model in Figure 2. our VTM modifies the vanilla VAE model by intro- ducing independent latent variables and c, representing template latent variable and content latent variable respectively. c models the content information in the table, while 2 models the sen- tence template information. Target sentence y is generated by both content and template variables. The two latent variables are disentangled, which makes it possible to generate diverse and relevant sentences by sampling template variable and retraining the content variable. Considering pairwise and raw data presented in Figure I their generation process for the content latent variable is different.
For a given table-text pair (z, E Dps the content is observable from table x. As result, is assumed to be deterministic given table x, whose prior is defined delta distribution p(c|z) = 8(c- = (z)) The marginal log-likelihood is:
log logpo(y)z) = log cholllr..ccc(ee)pcccadddd
log (em (x. v) D'-
For raw text y D,,, the content unobservable with the absence of table x. As a result, the content latent variable should be sampled from prior of Gaussian distribution NN 7). The marginal log-likelihood
log (o()) log / p pol.lle.)p(eppppcclll. y E D,.
In order to make full use both table-text pair data and raw text data, the above marginal log- likelihood should be optimized jointly:
(()) (x,)) [log [)))=)] + Ey [log pe (v)).
Directly optimizing Equation 3 is intractable. Following the idea of variational inference (Kingmal & Welling 2014), a variational posterior (-) constructed as an inference model (dashed lines Figure to approximate the true posterior. Instead of optimizing the marginal log-likelihood in Equation we maximize the evidence lower bound (ELBÓ). Section 3.1 and 3.2 the ELBO of table-text pairwise data and raw text data discussed, respectively.
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3.1 LEARNING FROM TABLE-TEXI PAIR DATA
In this section, we will show the learning loss of table-text pair data. According to the aforemen- tioned assumption, the content variable observable and follows delta distribution centred in the hidden representation of the table x.
ELBO objective. Assuming that the template variable only relies on the template of target sentence, we introduce (=lv) as an approximation of the true posterior p(z)y, c,
The ELBO loss of Equation is written CELBO, (z, 3) -g (=lv) log (e (x))(x)) x) + DKL(Ue. (xle)ll(z)) (x, E Dp.
The variational posterior g0 (2ly) is assumed as multivariate Gaussian distribution (u,, (v), Eg. (y)), while the prior p(2) is taken as a normal distribution (0,
Preserving- Templatte Loss. learn good template representation space. Inspired by the work in style-transfer (Hu al., 2017b] Shen et al.) 2017 Bao et al., 2019; John et al.) 2018), an auxiliary loss is introduced to embed the template information of sentences into template variable z.
Without any supervision, the ELBO loss alone does not guarantee to
With table, we are able to roughly align the tokens in sentence with the records in the table. By replacing these tokens with a special token <ent>, we can remove the content information from sentences and get the sketchy sentence template, denote as ÿ. We introduce the loss ppt to ensure that the latent variable = only contains the information of the template.
tereplatt
Cx(x, v. = -- Bgg, v) log (ūl2) -gg (=lu) Σ log (Wel F<t))
where m is the length of the y. and 1 denotes the parameters of the extra template generator. Lm trained via parallel data. In practice, due the insufficient amount of parallel data, template generator Pr, may not be well-learned. However, experimental results show that this loss is sufficient to provide a guidance for learning template space.
3.2 LEARNING FROM RAW TEXT DATA
Our model is able to make use of a large number of raw data without table since the content infor- mation of table could be obtained by the content latent variable.
ELBO objective. raw data is
According to the definition of generative model in Equation 2. the ELBO of
Po 2, (=, cly)
(ogpp()) (z,cll)))) log
y E Dr.
With the mean field approximation (Xing et al. 2003). 20 (2 c/a) can be factorized as: go(2,cly)) = 4s. (zlv)ag. (cly). We have:
(E) = - Ey,, (slu)a,, log logro
+ DKL (go. (z|g)]lp(z)) + DKL
p(c)),
D
In order to make use of template information contained raw text data effectively, the parameters of generation network po(v|2,0, c) and posterior network 9o, (2(y) are shared for pairwise and raw data. decoding process. for raw text data, we use content variable e as the table embedding for the missing of table Variational posterior for is deployed another multivariate Guassian lo. (cly) N(ja,, (v), Eo. (v)). Both p(=) and p(c) are taken as normal distribution (0, 7).
Preserving-Content Loss. In order to make the posterior 4o, (cly) correctly infers the content information, the table-text pairs are used as the supervision to train the recognition network of 4o (cly). To this end, we add preserving-content loss
(p)(
(ew)ll h|l2 DKL (cl))))((e), (x, E Dpp
where h = (x) is the embedding of table obtained by the table encoder. Minimizing Eyc is also helpful to bridge the gap of e between pairwise (taking c = h) and raw training data (sampling find that the first term of Lpe is equivalent to (1) make the mean of q. (cly) closer to h; (2) minimize the trace of co- co-variance of qo (cly). The second term serves as a regularization. Detailed explanations and proof are referred in supplementary materials.
from qs (clg)). Moreover,
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Algorithm Training procedure
Input: Model parameters Φ2, cc. 0,17
Table-text pair data D [( )) raw text data D, {11;)==1 M >
Procedure TRAIN(Dp, Dr):
1: Update de, 0, v) by gradient descent on LELBO, Lau + Ept + Lae
2: Update dz, de, 0 by gradient descent on LELOO, + Lmn
3:
Update Øz, de, 0, 1 by gradient descent on Ltot
3.3 MUTUAL INFORMATION Loss
As introduced by previous works (Chen et al. 2016 Zhao al. 2017; 2018), adding mutual infor- mation term to ELBO could alleviate KL collapse effectively and improve the quality of variational posterior. Adding mutual information terms directly imposes the association of content and tem- plate latent variables with target sentences. Besides, theoretical proo 2 and experimental results show that introducing mutual information bias is necessary the presence of preserving-templart loss Exr" y")).
As result, in our work, the following mutual information term is added objective (y)(y) (z, 3) [(c, v)-
3.4 TRAINING PROCESS
The final loss of VTM is made up of the ELBO losses and extra losses:
Lrat (w², v²,"") = LILOO, (c",,"" + LEINO. + AM (MI(LM((y") + Lmm(y²)) >pcCpil", .y²) ) + Ap y²")) (x², E Dr. E Dr..
AMI, Apt and Ape are hyperparameters with respect to auxiliary losses.
The training procedure is shown in Algorithm posterior network 2s.e could be trained jointly by both table-text pair data and raw text data. this way, a large number of raw text data can be used to enrich the generation diversity.
The parameters of generation network 0 and
4 EXPERIMENT
4.1 DATASETS AND BASELINE MODELS
Dataset. We perform the experiment on SPNLG (Reed 2018 and WIKI (Lebret al. 2016
Wang et al., 2018b). Two datasets come from two different domains. The former is collection of restaurant descriptions, which expands the E2E datase into a total of 204, 955 utterances with more varied sentence structures and instances. The latter contains 728, 321 sentences of biographies from Wikipedia. To simulate the environment that a large number raw texts provided, we just use part of the table-text pairs from two datasets, leaving most of the instances as raw texts. Concretely, for two datasets, we initially keep the ratio of table-text pairs to raw texts as 1:10. For WIKI dataset, in addition to the data from WikiBio (Lebret et al. 2016). the raw text data is further extended by 2 from external Wikipedia Person and Animal Dataset (Wang al. 2018a). The statisties for the number of table-text pairs and texts in the training, validation and test sets are shown in 2
the biographical descriptions of people
Evaluation Metrics. For WIKI dataset, evaluate the generation quality based BLEU-4, NIST, ROUGE-L (F-score). For SPNLG, we use BLEU-4, NIST, METEOR, ROUGE-L (F-score), and CIDEr. We use the same automatic evaluation script from E2E NLG Challenge The diversity of generation is evaluated by self-BLEU (Zhu et al. 2018). The lower self- eelf-BEEU, the more diversely the model generates.
Proof can be found in Appendix C
soe. wwe.e.csscc..
http::/wee http://wwww.aaa..........aeaaaes
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Train
Valid #table-text pair
Test
Dataset SPNLG
#talle-text pair
#raw text
Braw text
#table-text pair
14, 906 84, 150
149, 058
20, 495
20, 496
WIKI
841, 507
72, 831
42, 42,874
72, 831
Table 2: Dataset statistics in our experiments.
Baseline models.
We implement the following models as baselines:
Table2seq: Table2seq model first encodes the table into hidden representations then generates the sentence in a sequence-to-sequenee architecture (Sutskever et al. 2014). For fair comparison, apply the same table-encoder architecture as in Section 2 and the same LSTM decoder with attention mechanism as our model. The model is only trained on pair-wise data. During the testing, we generate five sentences with beam size ranging from one to five to increase some variations. We denote the model as Table2seq-beam. We also implement the decoding with forward sampling strategy (namely Table2seq-sample first pretrain the decoder using raw text as a language model, then train Table2seq on the table-text pairs, which noted Table2seq- prretzse-.retrain.. table2seq-pretraiin has the same decoding strategy as Table2seq-beam.
Moreover, to incorporate raw data,
Temp-KN: Template- KN model (Lebret al. 2016) first generates template according to the interpolated 5-gram Kneser-Ney (KN) language modeled over sentence templates, then replaces the special token for the field with the corresponding words from the table.
The aape-parrmeters of the VTM are chosen based on the lowest LELBO, on the validation set of SPNLG and LELBO, + LELRO, on the validation set of WIKI. Word embeddings are randomly initialized with 300-dimension. During training, we use Adam optimizer (Kingma & Ba 2013) with the initial learning rate as 0.001. Details hyperparameters are listed in Appendix D
4.2 EXPERIMENTAL RESULTS ON SPNLG DATASET
Quantitative analysis. According to the results in Table we find that our variational template machine (VTM) can generally produce sentences with more diversity under a promising perfor- mance in terms of BLEU metrics. Table2seq with beam search algorithm (Table2seq-bean)) only trained on parallel data, generates the most fluent sentences, but its diversity rather poor. Although the sampling decoder Tfable2seq-samplee gets the lowest self-Bli BLEU, it sacrifices the flu- ency at the cost. Table2seq performs even worse when the decoder is pre-trained by raw data as a language model. Because there is still gap between the language model and data-to-text task, the decoder fails to learn how to use raw text in the generation of data-to-text stage. On the contrary, VTM can make full use of the model, KN receives the lowest self-BLEU score, but it fails to generate fluent sentences.
which
data with the help of content variables. As cemplate-based
To study the effectiveness of the auxiliary loses and the augmented raw texts, we progressively remove the auxiliary losses and raw data in the ablation study. We reach the conclusions as follows.
Ablation study.
Without the preserving-conten loss Lpc, the model has a relative decline in generation quality. This implies that, by training the same inference model of content variable pairwise data, preserving-contedd loss provides an effective instruction for learning the content space.
VTM- the model trained without using raw data, where only the loss functions in Section 3.1 are optimized. Comparing with VTM-noraw, VTM gets substantial improvement in gener- ation quality. More importantly, without extra raw text data, there also a decline in diversity (self-BLEUU. Experimental results show that raw data plays valuable role in improving both generation quality and diversity, which often neglected by previous studies.
We further remove the mutual information loss and preserving-templatt loss from VTM-noraw model. Both generation quality and diversity continuously decline, which verifies the effective- ness of the two losses. Moreover, the automatic evaluation results of VTM-noraw-Cuur pirically show that preserving-templee loss may be hinder if we only add it during the training, as illustrated in Section 3.3
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Methods
BLEU
NIST
METEOR
ROUGE
CIDEr
Self-BLEU
Table2seq-beam
40.61
6.31
38,67
56.95
3.74
97.14
Sablelseg-sample
34.97
5.68
35.46
52.74
3.00
63.69
Table2seq-prettee
40.56
6.33
38.51
56.32
3.75
100.00
Temp-KN
6.45
0.45
12.53
27.60
0.23
37.85
VIM
40.04
6.25
38.31
56.48
3.64
88.77
Lpp
39.58
6,24
38.30
56.24
3,69
87.20
VTM-noraw
39.94
6.22
38,42
56.72
3.66
88.92
-LMI
38.33
6.02
37.77
$5.92
3.51
96.55
-pm
39.63
6.24
38.35
56.36
3.70
92.54
Table Result for SPNLG data Under the 0.05 significance level, VTM gets significantly higher results in all the fluency metrics than all the baselines except Table2seq-beam.
92
self-BLEU
90
89
TableP
2
4
5
6
7
8
9
10
0.4
0.7
0.8
proportion
Figure 3: Quality-diversio trade-off curve on SPNLG dataset.
Figure 4: Self-BLEU and the proportion of raw texts to table-sentence pairs.
Experiment on quality and diversity trade-off. The quality and diversity trade-off is further analyzed to illustrate the superiority of VTM. In order to evaluate the quality and diversity under different sampling methods, conduct experiment temperatures. Sampling from the softmax with temperature is commonly applied to shape the dis- tribution (Ficler & Goldberg 2017 Holtzman al. 2019). Given the logits "1:|v] and temperature T, we sample from the distribution:
sampling from the softmax with different
exp (uI/T)
ply Vilyer, F. 2, Ev cxp (ur/T)
When + 0, it approaches greedy decoding. When ward sampling. In the experiment, we gradually adjust temperature from O to 1, taking t = evaluated
1.0, is the same for-
0.1, 0.2, 0.3, 0.5, 0.6, 0.9, 1.0. BLEU and self-BLEU under different temperatures for both Table2seq and VTM. The self-BLEU in different temperatures and BLEU and self-BLEU curves are plotted Figure 3 It empirically demonstrates the trade-off between the generation quality and diversity. By sampling from different temperatures, we can plot the portfolios of (Self-BLEU, BLEU) pairs of Table2seq and VTM. The closer the curve to the upper left, the better the performance of the model. VTM generally gets lower self-BLEU with more diverse outputs under the comparable level of BLEU score.
Human evaluation In addition to the quantitative experiments, human evaluation is conducted as well. We randomly select 120 generated samples (each has five sentences) and ask three annotators to rate them on 1-5 Likert scale in terms of the following features:
Accuracy: whether the generated sentences are consistent with the content table.
Coherence: whether the generated sentences are coherent.
Diversity: whether the sentences have many patterns/structtres possible.
Based on the qualitative results in Table VTM generates the best sentences with the highest accuracy and coherence. Besides. VTM is able to obtain the comparable diversity with Table2seq- sample and Temp-KN. Compared with the model without using raw data (VTM-no raw), there is significant improvement in diversity, which indicates that raw data essentially enriches the latent

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Methods
Accuracy
Coherence 4.54
Diversity
Table2seq-sample
3,44
4.87
Temp-KN
2.90
2.78
4.85
VTM
4.44
4.84
4.33
VTM-noraw
4.33
4.62
3.44
Table Human evaluation results on different models. The bold numbers are significantly higher then others under 0.01 significance level.
Methods
BLEU
NIST
ROUGE
Self-BLEU
Table2seq heam
26.74
5.97
48.20
92.00
Table2seq sample
21.75
42.09
36.07
335
Table2seq-pretrain
25.43
5.44
45.86
99.88
325
Temp-Ki KN
11.68
2.04
40.54
73.14
VTM
25.22
5.96
45.36
74,86
40.91
VTM-noraw
21.59
5.02
39.07
78.19
Cyn
21.30
4.73
40.99
79.45
-Lur-p
16.20
3.81
38.04
84.45
0.5
6.2
20
Table 5: Results for WIKT dataset. All the metrics are significant under 0.05 significance level.
Figure 5: Quality-diversity trade-off curve
compared with NER+Tableeseq.
template space. Although obtaining the highest scores in diversity for Table2seq-sampn and Temp- KN, their generation qualities are much inferior to the VTM, and comparable generation quality is the prerequisite when comparing the diversity.
Experiment on the diversity under different proportions of raw. order to show how much data may contribute to the VTM model, train the model under different proportions of raw data to pairwise data in training. Specifically, we control the ratio of raw sentences to the table-text pairs under 0.5:1, 1:1, 2:1, 3:1, 5:1, 7:1 and 10:1. As shown in Figure + the self-BLEU rapidly decreases even adding small number of raw data, and continuously decreases until the ratio equals 5:1. The improvement is marginal after adding more than 5 times of raw data.
Case study. According to Table 8 (in Appendix E), despite template-like structures vary much in forward sampling model, the information in sentences may be wrong. For example, Sentence says that the restaurant is Japanese place. Notably. VTM produces correct texts with more diversity of templates. VTM is able to generate different number of sentences and conjunctions. For example, "[name] is [food] place in [area] with price range of [priceRange). It is [ea[Type]]" (Sentence 1, two sentences, "with" aggregation). "[name] a [eatType] with price range of [priceRange]. It is in [area]. It is [food] place." (Sentence 2, three sentences, "with" aggregation). "[name] is [food] restaurant in [area] and it is [food]." (Sentence 4, one sentence, "and" aggregation).
4.3 EXPERIMENTAL RESULTS ON WIKI DATASET
Table 5 shows the results for WIKI dataset, the same conelusions can be drawn as in the results in SPNLG dataset for both the quantitative analysis and ablation study. VTM is able to generate sentences with the comparable quality Table2seq-bean but more diversity.
Comparison with the pseudo-table-based method. Another way to incorporate raw data is to construct pseudo-table from the given sentence by applying sentence-to-tabl backward model via name entity recognition (NER). However, when the type of entities is complicated, such as in product introduction, or the raw data comes from the different domains as pairwise data, the commonly- used model for NER cannot provide accurate pseudo-tables. In this experiment, we replace 841 ,5007 biography raw sentences with 101,807 sentences that describe the animals (Wang et al., 2018b) to test the generalization of model in raw data of different domains. NER+Table2sen is the two- step model that first constructs the pseudo-table by a Bi-LSTM--RF (Huang et al. 2015) model trained from the table-text pairs, then trains Table2seq from both table-text pairs and pseudo-table: text pairs. We control the temperature in decoding method as previous, and results are plotted in Figure We find that compared with NER+ Tableeabl the curve of VTM is closer the upper left,
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Table2seq
VTM-noraw
VTM
Train
30min / 66 epochs
30min 6 epochs
1660mn 15 epochs
Test
~00mm
~80min
~~8min
Table 6: Computational cost for each model.
name[Jack Ryder]. country[Australia, fullname(Johh Ryder]. nickname(the king of Collingwoodl, birth date|8 August 1889). birth birthh_lace(Collinnen place(Collingwood Victoria, Austrulia], daath April 1977).
Table
death. srath-place(Fizzooy Victoria, Australia], club(Victoria) testdebutyear(1120 england), aritcle.title([an Ryder (cricketeri)
John "Jack" Ryder, mbe August 1889 3 April 1977) was cricketer who played for Victoria and Australia.
Reference
1: john Ryder (8 August 1889 - 3 April 1977) was an Australian cricketer
2: john Ryder Ryder 8 August 1889 -3 April 1977) was an Australian cricketer.
3: john Ryder Ryder 8 August 1889 3 April 1977) was Australian cricketer who played for glouces-
Table2seq-sumple
tershire cricket club in 1912.
4: john Ryder 8 August 1889 - 3 April 1977) was an Australian cricketer.
5: john Ryder oliveira August
April 1977) was an Australian test cricketer who played against great Britain with international cricket club
1: jack Ryder ( born August 8, 1859) Is a former professional cricketer).
2: "jack" Ryder born August 8, 1889) a former professional cricketer) who played in the national
footbull league.
Temp-KN KN
3: jack Ryder hom 8 August 1889 in Collingwood, Victoria.) is former prifessional cricketer).
4: Jack Ryder born August 8, 1889, in Collingwood, Victoria, Australia) is former professional football
player who currently member the united states
5: jack Ryder ( horn August 8, 1889) is former professional cricketer).
1: John Ryder (8 August 1889 - 4 April 1977) was an Australion cricketer.
2: Jack Ryder (born August 21, 1951 in Melbourne, Victoria) was an Australian cricketer.
VTM-noraw
3: John Ryder (21 August 1889 -4 April 1977) was Australian cricketer.
4: Jack Ryder (8 March 1889 3 April 1977) was an Australian cricketer.
5: John Ryder (August 1889 April 1977) was an Australian cricketer.
1: John Ryder August 8899 April 1977)
Australian cricketer.
2: John Ryder (homn 8 August 1889) was an Australian cricketer.
3: Jack Ryder (born August 9, 1889 in Victoria, Australia) was an Australian cricketer.
VTM
4: John Ryder (August 8. 1889 April 1977) was Australiun rules footballer who played for Victoria
in the Victorian foothall league (VFL.).
5: John Ryder, also known as the king of Collingwood (8 August 1889 4 April 1977) was an Australian cricketer.
Table 7: An example of the generated text by our model and the baselines on WIKI dataset.
which implies that VTM generate more diverse (lower Belf-BLEU) under the commensurate BLEU.
Computational cost. We further compare the computational cost of VTM with other models, for both training and testing phases. We train and test the models on a single Tesla V100 GPU. The time spent to reach the lowest ELBO in the validation set is listed in T VTM trained about five times longer than the baseline Table2seq model (160 minutes, 15 epochs in total) because of the training of an extra large number of raw data (84k pairwise data and 841k raw texts). In the testing phase, VTM enjoys the same speed as other competitor models, approximately 80 minutes to generate 72k wiki sentences in the test set.
Case study. Table 7 shows an example of sentences generated by different models. Although forward sampling enables the Table2seq model to generate diversely, it is more likely to generate incorrect and irrelevant content. For example, it generates the wrong club name in Sentence 3. By sampling from template space, VTM-noraw can generate texts with multiple templates, like dif- ferent expressions for birth date and death date, while preserving readability. Furthermore, with extra raw data, VTM is able to generate more diverse expressions, which other models cannot pro- duce, such "fuulhame], also known [nickname] (lbirth_date [article name 4]." (Sentence 5). It implics that raw sentences not in the pairwise dataset could ad- ditionally enrich the information in template space.
adaeth_date) was [country]
9

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Published as a conference paper at ICLR 2020
5 RELATED WORK
Data-to-text generation aims to produce summary for the factual struc- tured data, such as numerical table. Neural language models have made distinguished progress by generating sentences from the table in end-to-end style. JJin et al. (2018) proposed mixed hi- erarchical attention model to generate weather report from the standard table. [Gong et al. (2019) proposed hierarchical table-encoder and decoder with dual attention. Although encoder-decoder models can generate fluent sentences, they are criticized for deficiency in sentence diversity. Other works focused controllable and interpretable generation by introducing templates as latent vari- ables. [Wiseman et al. 2018) designed a Semi-HMM decoder to learn discrete templates represen- tation, and Dou al. (018)) created platform, Data2TextStuddo, equipped with Semi-HMMs model, to extract template and generate from table input in an interactive way.
Data-to-text Generation.
Semi-supervised Learning From Raw Data. It easier to acquire raw text than get structured data, and most neural generators cannot make the best use of raw text, universally. Ma et al. (2019) proposed that encoder-decoder framework may fail when not enough parallel corpus is provided. In the area of machine translation, back trck-translation have been proved to be an effective method to utilize monolingual data (Sennrich et al. 2016 Burlot & Yvon 2018).
Latent Variable Generative Model. (VAE) (Kingma Welling 2014) have shown a promising performance in generation. [Bomman et al. (2016) showed that a RNN-based VAE model can produce diverse and well-formed sentences by sampling from the prior of continuous latent variable. Recent works explored methods to learn disentangled latent variables (Hu et al.] 2017a Zhou & Neubig, 2017 Bao et al.) (009).. For in- stance, Bao et al. (2019) devised multi-takk losses adversarial losses to disentangle the latent space into syntactic space and semantic space. Motivated by the idea of back-translation and variational autoencoders, VTM model proposed in this work can not only fully utilize the non-parallel text corpus, but also learn disentangled representation for template and content.
Deep generative models, especially variational autoencoders
CONCLUSION
In this paper, we propose the Variational Template Machine (VTM) based on a semi-supervised learning approach in the VAE framework. Our method not only builds independent latent spaces for template and content for diverse generation, but also exploits raw texts without tables to further expand the template diversity. Experimental results two datasets show that VTM outperforms the model without using raw data in terms of both generation quality and diversity, and it can achieve a comparable quality in generation with Table2seq, as well as promote the diversity by a large margin.
ACKNOWLEDGMENTY
We thank the anonymous reviewers for their insightful comments. Hao Zhou and Zhongyu Wei are the corresponding authors of this paper.
REFERENCES
Gabor Angeli, Percy Liang, and Dan Klein. A simple Nomain-independee probabilistic approach to generation. In Proceedings of the Conference Empirical Methods in Natural Language Processing, 2010.
Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Kyunghyun Cho. Unsupervised neural machine translation. In Proceedings of the International Conference on Learning Representations, 2018.
Junwei Bao, Duyu Tang, Nan Duan, Zhao Yan, Yuanhua Lv, Ming Zhou, and Tiejun Zhao. Table- to-text: Describing table region with natural language. In Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Yu Bao, Hao Zhou, Shujian Huang, Lei Li, Lili Mou, Olga Vechtomova, Xinyu Dai, and Jiajun Chen. Generating sentences from disentangled syntactic and semantic spaces. In Proceedings of the Conference of the Association for Computational Linguistics, 2019.
10

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Published as a conference paper at ICLR 2020
Samuel Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio. Generating sentences from a continuous space. In Proceedings of the Conference on Computa- tional Natural Language Learning.. 2016.
Franck Burlot and François Yvon. Using monolingual data in neural machine translation: a system- atic study. In Proceedings of the Conference on Machine Translation: Research Papers, 2018.
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, llya Sutskever, and Pieter Abbeel. Info- gan: Interpretable representation learning by information maximizing generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, 2016.
Andrew Chisholm, Will Radford, and Ben Hachey. Learning to generate one-sentence biographies from wikidata. In Proceedings of the Conference of the European Chupter of the Association for Computational Linguisties, 2017.
Longxu Dou, Guanghui Qin, Jinpeng Wang, Jin-Ge Yao, and Chin Yew Lin. Data2text studio: Automated text generation from structured data. In Proceedings of the Conference on Empirical Methwds in Natural Language Processing: System Demonstrations, 2018.
Jessica Fieler and Yoav Goldberg. Controlling linguistic style aspects in neural language generation. In Proceedings of the Workshop on Stylistic Variation, 2017.
Heng Gong, Xiaocheng Feng, Bing Qin, and Ting Liu. Table-to-text generation with effective hier- archical encoder on three dimensions (row, column and time). In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the International Joint Conference on Natural Language Processing. 2019.
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. The curious case of neural text degener- ation. arXiv preprint 19Xivv/904.0975/ 2019.
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. Toward controlled generation of text. In Proceedings of the International Conference Machine Learning. 2017a.
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. Toward controlled generation of text. In Proceedings of the International Conference on Machine Learning, 2017b.
Zhiheng Huang, Wei Xu, and Kai Yu. Bidirectional Istm-crf models for sequence tagging. arXiv preprint 108.v91508.00991, 2015.
Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Preksha Nema, Mitesh M Khapra, and Shreyas Shetty. mixed hierarchical attention based encoder-decoder approach for standard table summarization. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, 2018
Vineet John, Lili Mou, Hareesh Bahuleyan, and Olga Vechtomova. Disentangled representation learning for non-parallel text style transfer. In Proceedings of the Conference of the Association for Computational Linguistics, 2018
Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations, 2015.
Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. In Proceedings of the International Conference on Learning Representations, 2014.
Rémi Lebret, David Grangier, and Michael Auli. Neural text generation from structured data with application to the biography domain. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2016.
Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, and Zhifang Sui. Table-to-text generation by structure-aware seq2seq learning. In Proceedings of the AAAI Conference on Artificial Intelli- gence, 2018.
Shuming Ma, Pengcheng Yang, Tianyu Liu, Peng Li, Jie Zhou, and Xu Sun. Key fact as pivot: A two-stage model for low resource table-to-text generation. In Proceedings of the Annual Meeting of the Association for Computational Linguisties, 2019.
11

---

Published as a conference paper at ICLR 2020
Hongyuan Mei, Mohit Bansal, and Matthew R Walter. What talk about and how? selective generation using Istms with coarse-to- alignment. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016.
Lena Reed, Shereen Oraby, and Marilyn Walker. Can neural generators for dialogue learn sentence planning and discourse structuring? In Proceedings of the International Conference on Natural Language Generation, 2018.
Rico Sennrich, Barry Haddow, and Alexandra Birch. Improving neural machine translation models with monolingual data. In Proceedings of the Annual Meeting of the Association for Computa- tional Linguistics, 2016.
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. Style transfer from non-parallel text by cross-alignment. In Proceedings of the Advances in Neural Information Processing Systems, 2017.
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Proceedings of the Advances in Neural Information Processing Systems, 2014.
Qingyun Wang, Xiaoman Pan, Lifu Huang, Boliang Zhang, Zhiying Jiang, Heng Ji, and Kevin Knight. Describing a knowledge base. In Proceedings of the International Conference on Natural Language Generation, 2018a.
Qingyun Wang, Xiaoman Pan, Lifu Huang, Boliang Zhang, Zhiying Jiang, Heng Ji, and Kevin Knight. Describing a knowledge base. In Proceedings of the International Conference on Natural Language Generation, 2018b.
Sam Wiseman, Stuart Shieber, and Alexander Rush. Challenges data-to-document generation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2017.
Sam Wiseman, Stuart Shieber, and Alexander Rush. Learning neural templates for text generation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2018.
Eric P Xing, Michael Jordan, and Stuart Russell. A generalized mean field algorithm for variational inference in exponential families. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2003.
Shengjia Zhao, Jiaming Song, and Stefano Ermon. Infovae: Information maximizing variational
autoencoders. arXiv preprint 1eXivv:700..2200 2017.
Tiancheng Zhao, Kyusong Lee, and Maxine Eskenazi. Unsupervised discrete sentence representa- tion learning for interpretable neural dialog generation. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2018.
Chunting Zhou and Graham Neubig. Multi-space variational encoder-decoders for semi-supervise labeled sequence transduction. Proceedings of the Annual Meeting of the Association for Com- putational Linguistics, 2017.
Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu. Texygen: A benchmarking platform for text generation models. In Proceedings of the International ACM SIGIR Conference Research & Development in Information Retrieval, 2018
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Published as a conference paper at ICLR 2020
A EXPLANATION FOR PRESERVING-CCNTENN LOSS
The first term of -pp (x, y) is equivalent to:
K
(elw) 1112 (clx))
Σ
(c - h1))
1=1
K
[c (c: - -hs)2
Σ E(B((: -1:))² 2 var(c))
Σ [[(E(c)) -11) var(c)))
K
2(44 - h1,22 + Enil
i=1
|| h11 + tr())
When we minimize it, we jointly minimize the distance between mean of approximated posterior distribution, and the trace the co-variance matrix.
B PROOF FOR ANTI-INFORMATION PROPERTY OF ELBO
Consider the KI. divergence over the whole dataset (or a mini-batch of data), we have
En-ott)DDKu((( =E =Eg(=l))((()) [log (<2(2) ( logp(z)]
H=|xx) - Eyt=) logp(=)
H(([z) + (z) + Drr (kL(v<))lp(())
=I(2,0) + (g())((()))))))))))))))))))))( lp()))
H(2) (=[æ). Since KL divergence can be viewed as a regularization term in ELBO loss, When ELBO is maximized, the KI. term minimized, and mutual information between and latent [(2, x)) is minimized. This implies that and eventually become more independent.
where ((=) ({)()(() and I(2,x) x)
C PROOF FOR THE PRESERVING-TEMPLATT LOSS WHEN POSTERIOR COLLAPSE HAPPENS
When posterior collapse happens, DRL (I(2))))) 0.
(Y, V) -Reppp00. B Beng(slu) (ūl=) -E () E. Bx) log P,, (ū]z)
p(ū) p log 2) dů
p(=)
p(0) log (olz)dz
= E. Eg [Lo (v)]2] = E. log (1)
During the back-propagation,
11 v. CY, Y)1 =
thus, 02 is not updated.
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Published as a conference paper at ICLR 2020
D IMPLEMENTATION DETAILS
For the model trained on WIKI dataset, the the dimension of latent template variable set as 100, and the dimension of latent content variable is set as 200. The dimension of the hidden for table is 300. For the hyperparameters of total loss Lior, we set AMI 0.5, Apr = 1.0 and Ape = 0.5.
For the model trained SPNLG dataset, the dimension of latent template variable is set as 64, and the dimension of latent content variable is set as 100. The dimension of the hidden for table is also 300. For the hyperparameters of total loss Liot. we set AMI Apt Ape 1.0.
E
CASE STUDY ON SPNLG EXPERIMENT
Table
name Varevariablell eat patu Frenchrench), priceRangee20-222 ureaariverside
Reference
name Variable French place with price range of 20-25. It in riverside. is pub.
I: name Variable pub with price range of 20-25.
French restaurant riverside.
2: name Variabl
French restaurant in riverside with price range of 20-25, name Variable is pub.
Table2seq-sumple
3: name Variale is pub with price range of 20-25 and name Variable is a French restaurant in riverside.
4: name Variable pub with price range of 20-25, also it is riverside.
Japanese place. 5: name Variahle pub with average rating and it is French place in riverside.
1: name Variahle is in riverside, also it is in riverside.
2: name Variable French restaurant.
Temp-KN
3: Variable the restaurant.
4: name nameVariable Variable in riverside, und Variable is in [location].
5: name Variable in. Its French restaurant and it is in [location] with food and, even if Variahee is
food qual]. it is the best place.
1: name Variable pub with price range of 20-25, is French place in riverside.
2: name Variable is pub with price range of 20-25. it is pub. It in riverside.
VIM-noraw
3: name Variable French place riverside
a price range 20-25, It is a pub.
4: name Variable French place in riverside with price range of 20-25. It is pub.
5: name Variable is a French place in riverside with a price range of 20-25. It is a pub.
1: name Variable French place in riverside with a price range 20-25, 1t 18 pub.
2: name Variable
with price runge of 20-25. It in riverside. It French place.
VTM
3: name name Variable French pub in riverside with price range of 20-25, and it is pul.
4: name Variable French restaurant in riverside and is pub.
5: Variable French place riverside with price range of 20-25. It is a pub.
Table 8: An example of the generated text by our model and the baselines on SPNLG dataset.
14


Reviews:
[{"review_id": "SkeetYT-5H", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 8, "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 an approach to generate textual descriptions from structured data organized in tables, by using a \"variational template machine\" (VTM), which is essentially a generative model to separately represent template and content as disentangled latent variables to control the generation.\n\nThe contribution is well-written and well-motivated, the model exposition is clear, and the results are convincing. The experiment setup, depth, and breadth are particularly convincing. I see no reason to not accept this paper.\n\nRemarks:\n- It should be clearly stated which languages feature in the paper. From what I gather, it's only English. How does the method generalize to other languages? How does it scale with (the lack of) resources?", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_thoroughness_in_paper_reading": "I made a quick assessment of this paper.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I did not assess the derivations or theory."}, "extra_text": {}}, {"review_id": "HJlIc6pdYH", "reviewer": "AnonReviewer3", "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": "This paper proposes Variational Template Machine (VTM), a generative model to generate textual descriptions from structured data (i.e., tables). VTM is derived from the variational autoencoder, where the input is a row entry from a table and the output is the text associated with this entry. The authors introduce two latent variables to model contents and templates. The content variable is conditioned on the table entry, and generates the textual output together with the template variable. The model is trained on both paired table-to-text examples as well as unpaired (text only) examples. Experiments on the Wiki and SpNLG datasets show that models generate diverse sentences, and the overall performance in terms of BLEU is only slightly below the best baseline Table2Seq model that does not generate diverse sentences. The results also show that additional losses for preserving contents and templates introduced by the authors play an important role in the overall model performance. \n\nI have several questions regarding the experiments:\n- For the Table2Seq baseline, how was the beam size chosen? Did it have any effect on the performance of the baseline model?\n- Did the authors try other sampling methods for Table2Seq? (e.g., top-K or nucleus sampling)\n- VTM is only able to achieve comparable performance to Table2Seq in terms of BLEU after including the unlabeled corpus, especially on the Wiki dataset. A way to incorporate this unlabeled data to Table2Seq is by first pretraining the LSTM generator on it before training it on pairwise data (or in parallel). How would this baseline model perform in comparison to VTM?\n- In the conclusion section, the authors mentioned that VTM outperforms VAE both in terms of diversity and generation quality. What does this VAE model refer to? The experiments show that VTM is comparable to Table2Seq in terms of quality and is better in terms of diversity. \n\nGenerating text from structured data is an interesting research area. However, I am not convinced that the proposed method is a significant development based on the results presented in the paper. There are also many grammatical errors in the paper (e.g., ... only enable to sample in the latent space ..., and many others), so I think the writing of the paper can be improved.", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "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 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": "B1x-h8r9dH", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 8, "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 is interesting and proposes a novel approach for addressing a currently not largely considered problem.\nThe proposed model is sound and appropriate, as it relies on state-of-the-art methodological arguments. \nThe derivations are correct; this concerns both the model definition and the algorithmic derivations of model training and inference.\nThe experimental evaluation is adequate: it compares to many popular approaches and on several datasets; the outcomes are convincing.\nIt would be good if the authors could provide an analysis of the computational costs of their methods, as well as of the considered competitors. ", "extra_scores": {"experience_assessment": "I have published in this field for several years.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper thoroughly."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper addresses the problem of generating descriptions from structured data. In particular a Variational Template Machine  which explicitly disentangles templates from semantic content. They empirically demonstrate that their model performs better than existing methods on different methods. 

This paper has received a strong acceptance from two reviewers. In particular, the reviewers have appreciated the novelty and empirical evaluation of the proposed approach. R3 has raised quite a few concerns but I feel they were adequately addressed by the reviewers. Hence, I recommend that the paper be accepted. 
Decision: Accept (Poster)