Proceedings of The 9th International Natural Language Generation conference, pages 143–152,Edinburgh, UK, September 5-8 2016. c©2016 Association for Computational Linguistics

Statistical Natural Language Generation from Tabular Non-textual Data

Joy Mahapatra and Sudip Kumar Naskar and Sivaji BandyopadhyayJadavpur University, India

joymahapatra90@gmail.com, sudip.naskar@cse.jdvu.ac.in,sbandyopadhyay@cse.jdvu.ac.in

AbstractMost of the existing natural language gener-ation (NLG) techniques employing statisticalmethods are typically resource and time in-tensive. On the other hand, handcrafted rule-based and template-based NLG systems typ-ically require significant human/designer ef-forts. In this paper, we proposed a statisticalNLG technique which does not require any se-mantic relational knowledge and takes muchless time to generate output text. The sys-tem can be used in those cases where sourcenon-textual data are in the form of tuple insome tabular dataset. We carried out our ex-periments on the Prodigy-METEO wind fore-casting dataset. For the evaluation purpose,we used both human evaluation and automaticevaluation. From the evaluation results wefound that the linguistic quality and correct-ness of the texts generated by the system arebetter than many existing NLG systems.

1 Introduction

The aim of a natural language generation (NLG)system is to produce apprehensible natural languagetext from non-textual data source which could bea table, an image, numerical data or graphical data(Reiter and Dale, 1997). NLG is just the reverse pro-cess of the natural language understanding task.

Although many NLG systems have been pro-posed so far, there are mainly two types of lan-guage generation systems: knowledge-intensivesystems and knowledge-light systems (Adeyanju,2012). Knowledge-intensive NLG systems can becategorized mainly into two categories: template-based systems and handcrafted rule-based systems.

Knowledge-intensive generation approaches takesignificant human effort or expert advise for build-ing an NLG system. Some examples of this typeof NLG systems are SumTime system (Reiter etal., 2005), FoG system (Goldberg et al., 1994),PLANDOC system (McKeown et al., 1994), etc.On the other hand, knowledge-light NLG systemsmostly use statistical methods to generate output textand take less human effort. Being automatic sys-tems, knowledge-light systems mostly employ ma-chine learning and data mining techniques. Thereare many types of knowledge-light systems; n-grambased NLG (Langkilde and Knight, 1998), neuralnetwork based NLG (Sutskever et al., 2011), casebased NLG (Pan and Shaw, 2004), etc. However,it has been observed that knowledge-intensive sys-tems typically perform better than knowledge-lightsystems as per human evaluation (Adeyanju, 2012).

Beside knowledge-intensive and knowledge-lightNLG systems, there are also some NLG systemswhich can be built through semi-automatic tech-niques. Probabilistic synchronous context freegrammar (PSCFG) based NLG system (Belz, 2008)falls into this category of NLG systems.

In this paper we propose a novel, knowledge-lightapproach based NLG system which converts a tu-ple of tabular-formed non-textual data into its cor-responding natural language text data. Unlike mostof the existing NLG systems, our system does notrequire any human effort or domain expert help.Moreover, the system does not require much timeand computer resources (i.e., hardware equipments)for training and generation purpose. Most of theneural network (especially Recurrent Neural Net-

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work) based knowledge-light NLG systems demandadvanced computer resources and processing timefor training. Contrastingly, without taking much hu-man effort and resources, our system is able to gen-erate intelligible and readable text output.

The remainder of the paper is organized as fol-lows. Section 2 briefly presents relevant relatedwork. The proposed NLG system is described inSection 3. Section 4 elaborates the experimental set-tings, dataset and the corresponding results. Section5 concludes the paper.

2 Related works

Till date, a number of knowledge-light approachbased language generator systems have been pro-posed and some of them achieved quite good resultsin the generation task. Two such successful statis-tical NLG systems are Nitrogen (Knight and Hatzi-vassiloglou, 1995; Langkilde and Knight, 1998) andOxygen (Habash, 2000). These two NLG systemsare based on statistical sentence realizer. SimilarlyHalogen (Langkilde and Knight, 1998) representsanother statistical language generator which is basedon statistical n-gram language model. Oh and Rud-nicky (2000) also proposed an NLG system basedon statistical language model.

Since its inception, statistical machine translation(Brown et al., 1993; Koehn, 2010) has gained im-mense popularity and it is the most prominent ap-proach and represents the state-of-the-art in auto-matic machine translation. The task of NLG canbe thought as a machine translation task because ofthe similarity between their end objectives - con-verting from one language to another. Langner andBlack (2009) proposed an NLG system, Mountain,which modelled the task of NLG as statistical ma-chine translation (SMT). They used the MOSES1

toolkit (Koehn et al., 2007) for this purpose. Belzand Kow (2009) proposed another SMT based NLGsystem which made use of the phrase-based SMT(PB-SMT) model (Koehn et al., 2003). The MOSEStoolkit offers an efficient implementation of the PB-SMT model. However, the linguistic quality andreadability of PB-SMT based NLG systems were notas good as compared to other statistical NLG sys-tems like Nitrogen, Oxygen, etc. (Belz and Kow,

1http://www.statmt.org/moses

2009).Some semi-automatic NLG systems had also been

proposed. The Probabilistic synchronous context-free grammar (PSCFG) generator (Belz, 2008) rep-resents this category of NLG systems which canbe created mostly automatically but requires man-ual help to certain extent. In synchronous context-free grammar (SCFG) a pair of CFGs are consid-ered, where one CFG of CFG pair is responsiblefor the meaning representation and the other CFGof the pair is responsible for generating the naturallanguage text output.

Another type of automatic NLG systems makesuse of case-based reasoning (CBR) or instance basedlearning. This type of CBR based NLG systemsare based on the concept that similar set of prob-lems will appear in future and the same set of so-lutions will compensate or solve those problems.SEGUE NLG system (Pan and Shaw, 2004) waspartly a CBR based NLG system; SEGUE was builtwith a mix of CBR approach and rule based pol-icy. Adeyanju (2012) designed a CBR approachbased weather forecasting text generation tool CBR-METEO. The advantage of CBR based system isthat it takes very little manual help and if the givenprior dataset covers almost all types of input in-stances then CBR based systems perform better.

Recently, some neural network based NLG sys-tems have been proposed. With the advent of re-current neural network (RNN) based language mod-els (RNNLM) (Mikolov et al., 2010), some RNNbased NLG systems have been proposed. An ideaof generating text through recurrent neural networkbased approach with Hessian-free optimization wasproposed by (Sutskever et al., 2011). However, thismethod takes a long training time. An RNN basedNLG technique was proposed by (Wen et al., 2015)based on a joint recurrent and convolutional neu-ral network structure. This system was able to trainon dialogue act-utterance pairs without any semanticalignments or predefined grammar trees.

Although rule based knowledge-intensive NLGsystems take long time and expert knowledge andfeedback to be developed, this type of systems mostof the times are able to generate high quality nat-ural language text output. For example, SumTime(Reiter et al., 2005) weather forecasting system isessentially a rule based NLG system, however, its

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output text quality was found to be quite better com-pared to other automatic NLG systems. Anotherrule based system, probabilistic context free gram-mar based generator (Belz, 2008) is also able togenerate high quality sentences which mostly corre-late well with the given corpus text. In PCFG basedsystem, all possible generation grammars are firstdiscovered manually and then probabilities are as-signed to those grammars automatically by observ-ing the corpus.

3 System Description

This section describes our knowledge-light, statis-tical NLG system. Like any other computer soft-ware system, the system goes through similar de-velopment stages. We describe those stages in thefollowing subsections.

3.1 Task definitionThe primary objective of the system is to generatenatural language text output from tuple record of anon-textual table structure dataset. The table con-tains a set of different attributes (qualitative or quan-titative). Each row or tuple of the table representsa single unit of non-textual data which represents avector of that particular tuple’s attribute values.

Figure 1 visualizes this task for a typical weatherforecasting application. According to that figure,the task is to generate textual data ttx from a tuple-formed non-textual data tntx of the Tntx dataset (ta-ble). The Tntx dataset (table) has four attributes, ax,where x = 1....4.

Figure 1: Basic input-output structure of our system

3.2 Requirement analysisBeing a supervised statistical model, the systemneeds a parallel corpus for training purpose whichshould be a collection of non-textual tuple data and

the corresponding textual data. We make an assump-tion here, that most of the attribute values presentin any non-textual data will also appear in the cor-responding textual data for that non-textual data.For our experiments we used the Prodigy-METEO(Belz, 2009) parallel corpus on wind-speed forecastdata.

3.3 Design and development

To generate human readable and easily understand-able textual data from non-textual data, an NLG sys-tem must ensure two criteria. Firstly, natural lan-guage text output should be related to the corre-sponding non-textual data’s topic, i.e., output textmust contain appropriate information. Secondly, theoutput text must be fluent, i.e., the output text mustensure its linguistic quality.

In the design step, we divide our system into twomodules; one module holds the responsibility of en-suring informativeness and the other module main-tains linguistic quality of the generated output text.

3.3.1 Informativeness Management ModuleWe define informative quality of a generated out-

put text by considering how many attribute valuesof its corresponding non-textual data are present inthe generated output text and in which order. Itcan be noted that if a given parallel corpus holdsour requirement analysis criteria (cf. Section 3.2)then we can represent each textual data as a se-quence of attribute-names (which should later bereplaced with attribute-values) of its correspondingnon-textual data with interlinked word-groups be-tween two adjacent attribute values present in thesequence. This concept is illustrated in figure 2. Fig-ure 2 shows the steps which are necessary for main-taining informativeness of the output textual data.The example shown in Figure 2 is taken from a non-textual tuple formed data from a temperature andrainfall weather dataset which is not our actual ex-perimental dataset; it is presented only for illustra-tion purpose.

We subdivide the informativeness module intotwo submodules. First submodule predicts the ap-propriate sequence of attribute names while the sec-ond submodule’s job is to select all the interlinkedword-groups that should be present in the sequencepredicted by the first submodule.

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Figure 2: Steps for maintaining informativeness in the system

Let us consider generating text ttx from a givennon-textual data tntx. To achieve this we firstneed to find out the attributes’ name sequence stntx

and thereafter identify all interlinked word-groupsiwtntx∗−∗ . Mathematically we can express this as inEquation 1 since ttx is made up of stntx and iwtntx∗−∗ .

P (ttx|tntx) = P (stntx , iwtntx∗−∗ |tntx) (1)

P (ttx|tntx) = P (stntx |tntx) ∗ P (iwtntx∗−∗ |tntx, stntx)(2)

Equation 2 rewrites Equation 1 as the productof two individual models where the first model,P (stntx |tntx), denotes prediction of attribute namesequence stntx for the non-textual data tntx, whilethe second model, P (iwtntx∗−∗ |tntx, stntx), denotesprediction of all the interlinked word-groups iwtntx∗−∗for tntx in the attribute name sequence stntx pre-dicted by the first model.

i. Predicting Attribute Name SequenceThis model predicts the probable attribute namesequence for a given tuple-formed non-textualdata. For this prediction, firstly each attributevalue of a non-textual data needs to be identi-fied in the corresponding textual data. It may

so happen that some of the attribute values ofthe non-textual data might not be present inthe corresponding textual data. However, wemust add those attribute values (as features) inpredicting attribute name sequence since theycan play a crucial role in that prediction. Afteridentification of all the attribute values presentin each training textual data, we can train amodel on this dataset which can take a newnon-textual data and identify the correspondingtextual data’s attribute sequence. After predict-ing the attribute name sequence for a test non-textual data, we replace the attributes’ nameswith their corresponding values.

Let us consider that we want to find the at-tribute name sequence stntx corresponding tosome non-textual data tntx. Let the non-textualdataset Tntx (tntx ∈ Tntx) contain a1, a2, ...an

attributes and the corresponding attribute val-ues in tntx are atntx

1 , atntx2 , ...atntx

n . Then wecan express the attribute name sequence predic-tion for tntx as given in Equation 3.

P (stntx |tntx) =

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P (stntx |atntx1 , atntx

2 , ..., atntxn ) (3)

ii. Predicting interlinked word-groupsFor a sequence containing n possible attributenames/values corresponding to a textual data,there will be n + 1 number of interlinkedword(s) (or word-groups) as we introduce twodefault pseudo-attributes, one at the start andthe other at the end of the text.

We predict the interlinking word-group be-tween two attribute names along the attributename sequence predicted in the earlier step fora test textual data from a context window of sixattribute names around the two attribute namescurrently being considered. Therefore, predict-ing the interlinked word-groups for a textualdata are considered to be independent of eachother. We also consider the attribute names ofthe non-textual data which do not appear in theattribute name sequence. Let us consider thatwe want to predict the interlinking word-groupsiwtntx∗−∗ for an attribute name sequence stntx of anon-textual data tntx. Let this stntx sequence be[...al am an ao ar as at au...] and wewant to determine the intermediate word-groupbetween ao and ar. Let us also assume thatsome of the attributes’ (ae, af , ag) values oftntx are not present in stntx , which are atntx

e ,atntx

f and atntxg . We model this task of predict-

ing interlinking word-group between ao and ar

for tntx and stntx as in Equation 4.

P (iwtntxo−r |tntx, stntx) =

P (iwtntxo−r |atntx

m , atntxn , atntx

o , atntxr , atntx

s ,

atntxt , atntx

e , atntxf , atntx

g ) (4)

More precisely we can write theP (iwtntx∗−∗ |tntx, stntx) term as the productof independent interlinking word-group pre-diction tasks as in Equation 5, where prev(previous) and next (next) are any of twoadjacent attribute names in stntx .

P (iwtntx∗−∗ |tntx, stntx) =∏P (iwtntx

(prev)−(next)|tntx, stntx) (5)

Therefore, Equation 2 can be rewritten as inEquation 6.

P (ttx|tntx) = P (stntx , iwtntx∗−∗ |tntx)

= P (stntx |tntx) ∗ P (iwtntx∗−∗ |tntx, stntx)

= P (stntx |tntx)∗∏

P (iwtntx

(prev)−(next)|tntx, stntx)

(6)

3.3.2 Linguistic Quality Management ModuleThe informativeness management module tries to

answer “what will be content within the output tex-tual data ”, but it does not concern the linguisticquality of the generated text, e.g., fluency, readabil-ity, etc. In the linguistic quality management mod-ule we try to deal with the deficiency of linguisticquality in the generated textual data. Statistical lan-guage modelling is a well established technique forensuring fluency and readability in natural languagetext. Therefore, as a final component, we incor-porate a language model in our system. Hence, tomaintain both informativeness and linguistic qualityof the generated textual data, we model the task ofNLG as given in Equation 7, where PP (x) standsfor the perplexity of string x.

P (ttx|tntx) = P (stntx |tntx)

∗∏

P (iwtntx

(prev)−(next)|tntx, stntx) ∗ PP−1(ttx)

(7)For our experiments, we trained a trigram lan-

guage model on the training set textual data with aminor modification by replacing the attribute valueswith their attribute names.

3.3.3 DecodingThe search space of the NLG problem as mod-

elled by Equation 7 is enormous. For example, if weconsider top ten attribute name sequences, and foreach attribute name sequence there are overall fif-teen interlinked word-groups and for selecting eachof these interlinked word-group we consider onlytop five candidates, then the search space for thegeneration task will contain 10 ∗ 515 candidates. To

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reduce the size of the search space and keep the com-putation problem tractable, we implemented the taskof text generation as modelled in Equation 7 usingstack decoding (Jelinek, 1969) with histogram prun-ing which limits the number of most promosing hy-potheses to be explored by the size of the stack. Instack decoding with histogram pruning, the stack atany point of time contains only N (size of the stack)most promising partial hypotheses and during hy-pothesis expansion, a partial hypothesis is placed onthe stack provided there is space in the stack, or,it is more promising than at least one of the par-tial hypotheses already stored in the stack. In caseof stack overflow, the least promising hypothesis isdiscarded.

4 Experiments

This section presents the dataset used in our experi-ments and the evaluation results of our system com-pared to some other NLG systems.

4.1 Dataset

As mentioned in Section 3.2, a non-textual–textualparallel dataset is required to train our system. Theparallelism should be in such a form that each non-textual data can be represented as a tuple of attributevalue instances and most of those attribute valuesshould be present in its corresponding textual data.

We used the Prodigy-METEO2 corpus (Belz,2009), a wind forecast dataset, for our experi-ment. In the Prodigy-METEO corpus a single pairof non-textual–textual data stands for a particularday’s wind forecast report. A non-textual data inthat dataset is represented by a seven-componentvector, where each component expresses a partic-ular feature of wind data measurement at a mo-ment of time. The seven components belong toa vector represented by [id, direction, speed min,speed max, gust-speed min, gust-speed max, time].In that vector representation id stands for identifi-cation of the vector, direction mentions the windspeed direction, speed max and speed min denotethe maximum and mnimum wind speed respectively,gust max and gust min represent the maximum andminimum wind gust speed respectively, and the lastcomponent time denotes the specific time instance

2http://www.nltg.brighton.ac.uk/home/Anja.Belz/Prodigy

when rest of components’ readings were measured.For example, 1st April, 2001 wind forecast datais represented in this dataset as [[1, SW,28,32,42,-,0600],[2,-,20,25,-,-,0000]] , where ‘-’ represents amissing reading value.

As mentioned earlier our proposed model can pro-cess only a single tuple formed non-textual data at atime. However, the Prodigy-METEO corpus repre-sents each non-textual data (wind forecast data fora particular day) by a sequence of multi-componentvectors. For this reason, we merge all the vectors ofa particular day’s wind forecast data into a single tu-ple formed data. The merging of a particular day’swind forecast data vectors is illustrated in Figure 3.The Prodigy-METEO corpus comes with five pre-

Figure 3: Transformation of Prodigy-METEO corpus non-

textual data representation for the system’s input

defined splits each of which has on an average 490pairs of non-textual tuple data and the correspondingtextual data.

4.2 Evaluation

We evaluated our system using both automaticevaluation metrics and human evaluation. For bothhuman and automatic evaluation, we comparedour system with ten existing NLG systems whoseoutputs on the Prodigy-METEO testset are alsoavailable in the Prodigy-METEO corpus. These tenNLG systems are PCFG-Greedy, PSCFG-Semantic,PSCFG-Unstructured, PCFG-Viterbii, PCFG-2gram, PCFG-Roulette, PBSMT-Unstructured,

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Figure 4: An sample of input and outputs of different NLG system

SumTime-Hybrid, PBSMT-Structured and PCFG-Random (Belz and Kow, 2009). Figure 4 shows asample input and outputs of all the above mentionedsystems including our system.

4.2.1 Automatic Evaluation

For automatic evaluation, we used two automaticevaluation metrics; BLEU (Papineni et al., 2002)and METEOR (Banerjee and Lavie, 2005). BothBLEU and METEOR were originally proposed forevaluation of machine translation (MT) systemsHowever, due to the similarity between the two tasks(i.e., MT and NLG) from the point of view of theirworking principles, most of the NLG systems arealso evaluated using these two automatic MT evalu-ation metrics.

Because of the relatively small size of the dataset,we took a five-fold cross validation policy whichwas predefined in the Prodigy-METEO corpus. Ta-ble 1 presents the evaluation results obtained withBLEU and METEOR on our system along with theten other NLG systems.

System BLEU score Meteor scoreCorpus 1 1PCFG-Greedy 0.65 0.85PSCFG-Semantic 0.64 0.83PSCFG-Unstructured 0.62 0.81Proposed System 0.61 0.82PCFG-Viterbii 0.57 0.76PCFG-2gram 0.56 0.76PCFG-Roulette 0.52 0.76PBSMT-Unstructured 0.51 0.81SumTime-Hybrid 0.46 0.67PBSMT-Structure 0.34 0.59PCFG-Random 0.28 0.52

Table 1: Comparison using automatic metric evaluation

4.2.2 Human-based EvaluationEvaluation using automatic evaluation metrics

is very popular among researchers and developerssince automatic evaluation is very fast and cheap.Automatic evaluation metrics are good indicators ofsystem performance and they greatly help day-to-day system development. However, despite beingvery time intensive and costly, human evaluationstill serves as the de-facto evaluation standard and

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the worth of automatic evaluation metrics are typi-cally judged based on how well they correlate withhuman evaluation.

Figure 5: Comparison using human evaluation

We also evaluated the systems using human eval-uation on a part of the test dataset. We carried outhuman evaluation to measure the clarity and read-ability of the texts generated by the NLG systems.Clarity measures truthfulness and correctness of atextual data whereas readability concerns fluency ofthe textual data. 30 instances (out of total 232) wererandomly chosen from the testset for the pilot hu-man evaluation and the output from 11 different sys-tems along with the corresponding non-textual datawere presented to the human evaluators. Five stu-dents from different backgrounds who acted as hu-man evaluators were asked to rate 72 outputs each ina 10 point scale. The output of human evaluation ispresented in Figure 5.

4.3 Result Analysis

As per the outcomes of automatic evaluation, oursystem provided the third and fourth best results inMETEOR and BLEU, respectively. According toMETEOR, our system is only behind the PCGF-Greedy and PSCFG-Semantic systems while ac-cording to BLEU, PCFG-greedy, PSCFG-Semanticand PSCFG-Unstructured systems perform betterthan our proposed model. However, these systemswhich are ahead of our system in performance asper automatic evaluation are not fully automatic,whereas our system does not require any humaneffort or additional knowledge other than a non-textual–textual parallel corpus.

Human evaluation preferred rule based systemsover automatic knowledge-light systems. The Sum-Time system, which is a rule based system and is

placed in the ninth position according to both BLEUand METEOR, is adjudged the best system in hu-man evaluation. Our system ranks fourth among the11 systems according to human evaluation. The goldstandard reference set was also provided to the hu-man evaluators for evaluation without their knowl-edge and, surprisingly, the reference set was rankedfourth.

We calculated Pearson correlation coefficient be-tween scores produced by automatic evaluation met-rics (BLEU and METEOR) and human evaluation.For human evaluation we considered the averageof clarity and readability. The correlation coeffi-cients are r(Human, Bleu)=0.61 and r(Human, ME-TEOR)=0.57 .

5 Conclusions

The statistical NLG system presented in this paperdoes not require any external agents’ involvement.It is a domain independent system and can easilybe shifted from one application domain to anotherwithout any change.

To avail good quality output text from the system,one must conform to the requirement specified inSection 3.2. The NLG system will perform accu-rately if all attributes present in the training tuple-formed non-textual data contain distinct values. Ifthis criterion can be assured, then it will be trivialto match each of those attribute values present inthe non-textual data to their appearance in the cor-responding textual data. However, if this constraintis not possible to be satisfied the system will stillwork. It is worth to be mentioned here that in caseswhen not a single attribute value of non-textual datacan be found in the corresponding textual data, thenour system will behave like an instance based NLGsystem.

Acknowledgments

This work is partially supported by Media Lab Asia,Department of Electronics and Information Technol-ogy (DeitY), Government of India, under the YoungFaculty Research Fellowship of the VisvesvarayaPhD Scheme for Electronics & IT and through theproject “CLIA System Phase II”.

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