Dialogue based Question Answering System in Telugu 
 
 
 
Abstract 
A dialogue based Question Answering 
(QA) system for Railway information in 
Telugu has been described. Telugu is an 
important language in India belonging to 
the Dravidian family. The main compo-
nent of our QA system is the Dialogue 
Manager (DM), to handle the dialogues 
between user and system. It is necessary 
in generating dialogue for clarifying par-
tially understood questions, resolving 
Anaphora and Co-reference problems. 
Besides, different modules have been de-
veloped for processing the query and its 
translation into formal database query 
language statement(s). Based on the re-
sult from the database, a natural language 
answer is generated. The empirical re-
sults obtained on the current system are 
encouraging. Testing with a set of ques-
tions in Railway domain, the QA system 
showed 96.34% of precision and 83.96% 
of dialogue success rate. Such a question 
answering system can be effectively util-
ized when integrated with a speech input 
and speech output system. 
1 Introduction  
Ever since Question Answering (QA) emerged as 
an active research field, the community has 
slowly diversified question types, increased 
question complexity, and refined evaluation met- 
rics, as reflected by the TREC (Text Retrieval 
Conference) QA track (Voorhees, 2004). Several 
QA systems have responded to these changes in 
the nature of the QA task by incorporating vari-
ous knowledge resources (Hovy et al., 2002), 
handling of additional types of questions tapping 
 
 
 
 
 
 
 
 
 
 
 
 
 
into external data sources such as web, encyclo-
pedia, and databases in order to find the answer 
candidates, which may then be located in the 
specific corpus being searched (Xu et al., 2003). 
   The most popular classes of technique for 
QA are open-domain and restricted-domain 
(Diekema et al., 2004, Doan-Nguyen et al., 
2004). These two domains use thesauri and lexi-
cons in classifying documents and categorizing 
the questions. Open domain question answering 
deals with questions about nearly everything and 
can only rely on general ontology. It has become 
a very active research area over the past few 
years. On the other hand, Restricted-domain 
question answering (RDQA) deals with ques-
tions under a specific domain. If we create such a 
RDQA interface for structured e.g. relational 
database, we call it as Natural language interface 
to database system (NLIDB) (Androutsopoulos 
et al., 1995), where it allows the user to access 
the information stored in database by typing re-
quests expressed in some natural language. 
RDQA has a long history, beginning with sys-
tems working over databases (e.g., BASEBALL 
(Green et al., 1961), and LUNAR (woods et al., 
1972)). 
 In practice, current QAs can only understand 
limited subsets of natural language. Therefore, 
some training is still needed to teach the end-user 
what kinds of questions the system can or cannot 
understand. There are kinds of questions (e.g. 
questions involving negation, or quantification) 
that can be easily expressed in natural language, 
but that seem difficult (or at least tedious) to ex-
press using graphical or form based interfaces. 
Anaphoric and elliptical expressions are also 
handled by the QA systems. In recent years a 
large part of the research in QAs has been de-
voted to portability, i.e., to the design of QAs 
that can be used in different knowledge domains 
 
    Rami Reddy Nandi Reddy 
    Dept. of Comp. Sc. & Engg, 
Jadavpur University,  
Kolkata, India 
nramireddy@gmail.com 
 
 
Sivaji Bandyopadhyay 
    Dept. of Comp. Sc. & Engg, 
Jadavpur University,  
Kolkata, India 
sivaji_cse_ju@yahoo.com 
EACL 2006 Workshop on Multilingual Question Answering - MLQA06
53
(Knowledge domain portability), with different 
underlying Database Management System 
(DBMS) (DBMS portability), or even with dif-
ferent natural languages (Natural language port-
ability). There is a growing body of research on 
integrating speech recognition, robust interpreta-
tion with the goal being to implement systems 
that engage users in spoken dialogue to help 
them perform certain tasks. We expect that this 
line of research will have a significant influence 
on future QAs, giving rise to systems that will 
allow users to access databases by spoken dia-
logue, in situations for which graphic and form-
based interfaces are difficult to use. 
A practical question answering system in re-
stricted domain (Hoojung et al., 2004) and our 
system handles user questions similarly. How-
ever, our system extracts the information from a 
relational database. Moreover, our system keeps 
track of user dialogue and handles clarifications, 
elaborations and confirmations needed from the 
user with respect to the query. Along with it re-
turns natural language answer in user-friendly 
format.  
ARISE (Automatic Railway Information Sys-
tem for Europe) is a spoken dialogue system to 
provide train timetable information over the 
phone.  Prototypes have been developed in four 
languages: Dutch, French, English, and Italian. 
ARISE uses a mixed initiative Dialogue Manager 
(DM). A mix of implicit and explicit confirma-
tion is used, based on how confident the system 
is in deciding whether an item has been correctly 
understood. 
We relate this paper as an experiment for de-
signing a keyword based QA system for a huge 
domain (i.e. for Railways), which aims at reply-
ing users questions in their native language (Te-
lugu). The system generates SQL query out of 
the natural language question, executes the SQL 
query over a relational database and then provide 
the answer. Dialogue Manager (DM) is main-
tained to generate dialogues with user and to 
handle the anaphoric and elliptical expression in 
our query. This system is implemented on a rela-
tively restricted domain that includes a number 
of aspects of railway information system (Arri-
val/Departure time, Fare between for particular 
stations, Trains between important stations etc.). 
The precision of the information extraction stage 
is essential to the success of a QA system, be-
cause it places an upper bound on the precision 
of the entire system. 
The empirical results obtained on the current 
system are encouraging. Testing with a set of 
questions in Railway domain, the QA system 
showed 96.34% of precision and 83.96% of dia-
logue success rate.    
   Section 2 deals with the System Architecture 
of the QA system. Section 3 details about the QA 
system design in the Railway information do-
main using the Keyword based approach. The 
evaluation has been carried out in Section 4. Sec-
tion 5 concludes with some directions for future 
work. 
2 System Architecture  
In this keyword based approach the input query 
statement is analyzed by the query analyzer, 
which uses domain ontology stored as knowl-
edge base, generating tokens and keywords. The 
appropriate query frame is selected based on the 
keywords and the tokens in the query statement. 
Each query frame is associated with a SQL gen-
eration procedure. The appropriate SQL state-
ment(s) is generated using the tokens retrieved 
from the input query.  
The QA system architecture is shown in Fig-
ure 1. The Dialogue Manager keeps track of the 
elliptical queries from the user that constitute the 
dialogue and helps in the SQL generation proce-
dure using dialogue history (Flycht-Erikson et 
al., 2000), which contains information about pre-
vious tokens and their types as well as other dia-
logue information like answers retrieved by the 
current SQL statements and the answers for pre-
vious queries in the dialogue. The SQL state-
ments used to retrieve the correct answer from 
the database.  Based on the result of the DBMS, 
a natural language answer is generated. This an-
swer is forwarded to the DM for onward trans-
mission to the user. 
 
 
Figure 1. QA System Architecture 
EACL 2006 Workshop on Multilingual Question Answering - MLQA06
54
If the system cannot decide on the query frame 
by using the keywords extracted from the input 
query, the system enters into a dialogue with the 
user through the DM. During SQL generation if 
it is detected that more information is needed 
from the user to generate the SQL statement then 
an interactive message is sent to the user through 
the DM. The user will then send the needed in-
formation to the system. If user could not pro-
vide correct information then DM sends an error 
message to the user indicating the error in the 
user query. In case, the SQL statement generates 
a null response from the database the DM will 
send a cooperative message depending on the 
user query.  
3 Design of Railway Information Sys-
tem  
The most important issue in the design of the 
Railway information system is the design of the 
Railway database and the Knowledge base. 
These are detailed in Sections 3.1 & 3.2 respec-
tively. The different components of the dialogue 
based QA system, i.e., Query Analyzer, Query 
Frame Decision, Dialogue Manager, SQL Gen-
eration and Answer Generation sub systems are 
described in subsequent sections. 
3.1 Railway Database Management 
The system as a whole is engaged in data access, 
and is a hybrid system with subsystem to analyze 
the natural language query and formal query lan-
guage SQL, and a data retrieval and database 
management system. The database is structured 
and contains the information to provide the rail-
way information service. For example in a Rail-
way information system, database contains in-
formation about the arrival/departure time of 
trains, their fares and their running information 
etc. The aim of database management is to de-
scribe the information, in order to offer the ser-
vice.  
For our purposes the relational model has im-
portant advantage: The relational model stresses 
on data independence. This means that the user 
and front-end programs are effectively isolated 
from the actual database organization. 
The main tables used here are schedule table 
for each train, fare tables for special trains like 
Rajdhani, Shatabdi etc. that have a different fare 
structure, Route tables for each route and tables 
that include train running frequency details etc. 
Some temporal tables are maintained in order to 
check the status of the railway ticket (which is 
known as checking the Passenger Name Record 
or PNR status of the ticket) and reservation 
availability information of a particular train. 
3.2 Design of the Knowledge Base 
The system maintains a knowledge base of the 
domain to facilitate question answering. For a 
system operating on a restricted domain this is 
quite obvious since it will greatly improve the 
disambiguation and parsing. 
The words that occur in the database query for 
Railway information system includes words de-
scribing train name, station name, reservation 
class, and date and/or period of journey or key-
words that specify the topic of the query. Hence 
we stored a domain dependent ontology in the 
knowledge base.  
Knowledge base, which contains tables for 
train name, station name and alias tables for train 
name and station name. We have stored possible 
Telugu inflections (˜ý (ke [to]), –ýœ (ku [to]), ÙÌ 
(loo [in]), Æ–þœÕýÂšþ (tundi [ing]), °ý (vi [have]) etc. 
for ex: ”–ýœÕýÖþœÕ–ýœ–ýœ (gunturku [to Guntur])), which 
can be used in morphological analysis of input 
query. We have considered possible postposi-
tions like −´ýœÕýÀ˜þ (nundi [from]), −´ýœÕýÉþ (nunchi 
[from] etc. (For ex: −´ýœóÀ¨˜þÚ¡öþ −´ýœÕýÀ˜þ (newdelhi nundi 
[from New Delhi])), which can be used to iden-
tify the source station in the input query and 
route words like Â–þ”à–ýÕ–ý (daggara [near]), Â—øÕ— 
(dwara [through]),  ”–ýœÕýÀ— (gunda [through]), ¯´ýÂî–þ 
(vadda [at]), °¡ýœÂ–þœ”— (meedugaa [via]) etc. (For 
ex: ”–ýÕýœ´œ °¡ýœÂ–þœ”— (gaya meedugaa [via Gaya])), 
which can be used to identify the route station of 
the journey. We kept a list of keywords in a table 
in order to identify the proper query frame. 
3.3  Query Analyzer 
During query analysis, Morphological analysis of 
the input query statement is carried out to iden-
tify the root words / terms. Analyzing the whole 
input query, the system identifies several tokens 
such as Train name, Station name, Reservation 
class, date and period of the day etc. and a set of 
keywords.  
The query analyzer consults the domain-
dependent ontology i.e. knowledge base for rec-
ognizing these tokens and keywords. It may hap-
pen that some words/terms may not found in the 
knowledge base. Those words do not contain any 
semantic information and are simply discarded. 
 
EACL 2006 Workshop on Multilingual Question Answering - MLQA06
55
For example: If our input query is  
ˆý§´ýðœÀ–þœ §¨´ýÙþ¦ −´ýœ¯´ýœ ˆý¦ýûô§´ý©¾ý ”–ýœÕýÖþœÕ–ýœ–ýœ ¯ŸýÛìÃþ³ÕýÂšþ (ep-
pudu falaknuma express gunturuku veltundi 
[When the Falaknuma Express goes to Guntur])  
Here query is parsed based on spaces. After 
parsing each word, it is searched in the knowl-
edge base until the word is found. After search-
ing each word/term in the knowledge base, their 
types and semantic information are put in a list 
of tokens. Each token has three properties: the 
token value, its type and semantic information 
that it contains. These tokens and keywords are 
used to decide the proper query frame. 
For the above example, the tokens identified 
are §¨´ýÙþ¦ −´ýœ¯´ýœ ˆý¦ýûô§´ý©¾ý (Falaknuma Express) as 
Train name and ”–ýœÕýÖþœÕ–ýœ (Guntur) as Station 
name. Whereas ˆý§´ýðœÀ–þœ (eppudu [when]), ¯ŸýÛìÃþ³ÕýÂšþ 
(veltundi [goes]) are under keywords list.  
3.4 Query Frame Decision 
During the analysis of query, the keywords in the 
input query are detected. In this step, based on 
the tokens and keywords, we identify the appro-
priate query frame. 
   Restricting the query domain and informa-
tion resource, the scope of the user request can 
be focused. That is, there are a finite number of 
expected question topics. Each expected question 
topic is defined under a single query frame. 
Some query frame examples for Railway infor-
mation system are fare of a journey [Fare], arri-
val [Arr_Time] or departure time [Dep_Time] of 
a train, trains between important stations 
[Trains_Imp_Stations], scheduled time 
[Sched_Time], weekly frequency of a train 
[Arr_Frequency / Dep_Frequency], Availability 
of reservation class in a particular train [Reserva-
tion_Availability] and PNR enquiry 
[PNR_Enquiry]. 
It is important to select the appropriate query 
frame for the user request; because in some cases 
ambiguity will occur i.e. a single natural lan-
guage query statement may belong to one or 
more query frames means same keywords are 
used to identify the query frames. 
 For example keywords like ¯ŸýÛÃþ³÷ (vellu [go]), 
¯´ýÈ–þœâ (vachhu [come]), ÈŸþ¥Õ–ýœ (cheru [reach]), and 
×þÕýœ´œÙþœÂŸþ¥Õ–ýœ (bayuluderu [start]) etc. are used to 
identify the query frames [Arr_Time], 
[Dep_Time], and [Trains_Imp_Stations]. To re-
solve this ambiguity, we consider what/which 
(question having words ‰ (ee [what]), ‰‰ (eeee 
[what]), ‰°ý (evi [which]) etc.) type of questions 
like −´ýœóÀ¨˜þÚ¡öþ −´ýœÕýÀ˜þ ±´ýÍÕ—–ýœ ×þÕýœ´œÙþœÂŸþ¥Õ–ýœ Õ¢ŸýÛÃþ³÷ ‰°ý 
(newdelhi nundi howrahku bayaluderu raillu evi 
[What are the trains starts from New Delhi to 
Howrah]) are under [Trains_Imp_Stations] query 
frame. Where as, when (questions having words 
ˆý§´ýðœÀ–þœ (eppudu [when]), ˆý®ýï”–ýÕýÖþÙþ–ýœ (enniganta-
laku [at what time]), ˆý®ýïÕýÖþ¶˜ý (ennintiki [at what 
time]) etc.) type of questions like ˆý®ýï”–ýÕýÖþÙþ–ýœ ¤¥ÙÎþ–
ýÆ— Õ—ÑþÂ¨—®ý ˆý¦ýûô§´ý©¾ý ×þÕýœ´œÙþœÂŸþ¥Õ–ýœÆ–þœÕýÂšþ (ennigantalaku 
kolkata rajadhani express bayaluderutundi 
[When Kolkata Rajdhani Express starts]) are un-
der [Dep_Time] query frame. Similarly, week-
day names like ª¼¯´ýœ¯—Õ–ý¯´ýœœ (somavaaramu) [Mon-
day], ¯´ýœÕý”–ýÛÃþ¯—Õ–ý¯´ýœœ (mangalavaaramu) [Tuesday] 
etc. and keywords used in [Arr_Time]/ 
[Dep_Time] query frame are used to identify the 
[Arr_Frequency]/ [Dep_Frequency] query frame. 
In contrast, separate keywords are used to 
identify [Arr_Time] and [Dep_Time] query 
frames. But keywords like ˆ¼Æ–þœÕýÂšþ (potundi [go]), 
¯ŸýÛÃþ³÷ (vellu [go]) etc. are used to identify both 
[Arr_Time] and [Dep_Time] query frames. To 
resolve this ambiguity, we consider the station 
type, i.e. whether the station is source or destina-
tion. If the station is source station (station name 
succeeded by postpositions like −´ýœÕýÀ˜þ (nundi 
[from]), −´ýœÕýÉþ (nunchi [from])), then we conclude 
that our query is under [Dep_Time] query frame. 
Otherwise query will be under [Arr_Time] query 
frame. For example, questions like ˆý§´ýðœÀ–þœ §¨´ýÙþ¦ 
−´ýœ¯´ýœ ˆý¦ýûô§´ý©¾ý ”–ýœÕýÖþœÕ–ýœ–ýœ ¯ŸýÛìÃþ³ÕýÂšþ (eppudu falak-
numa express gunturuku veltundi [When the Fa-
laknuma Express goes to Guntur]) is under 
[Arr_Time] query frame. But, questions like 
ˆý§´ýðœÀ–þœ §¨´ýÙþ¦ −´ýœ¯´ýœ ˆý¦ýûô§´ý©¾ý ”–ýœÕýÖþœÕ–ýœ −´ýœÕýÀ˜þ ¯ŸýÛìÃþ³ÕýÂšþ 
(eppudu falaknuma express gunturu nundi vel-
tundi [When the Falaknuma Express goes from 
Guntur]) is under [Dep_Time] query frame. 
The selection process of query frame has a 
great influence on the precision of the system, 
while there is not much likelihood of errors in 
other processes, such as getting the information 
from the dialogue history or generating SQL 
statement(s) from the selected query frame 
and/or retrieving the answer from the database 
and generating natural language answer from the 
retrieved result.  
EACL 2006 Workshop on Multilingual Question Answering - MLQA06
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3.5 Dialogue Manager  
The role of the Dialogue Manager (DM) differs 
slightly between different dialogue systems. But 
the primary responsibility of the DM is to control 
the flow of dialogue by deciding how the system 
should respond to a user request and the coordi-
nation of the other components in the system. If 
some information is missing or a request is am-
biguous, clarification questions are specified by 
the DM and posed to the user. 
For example in general, users ask questions 
about Arrival/Departure time without mentioning 
journey of train i.e. Upward/Downward journey, 
then system asks the user for proper information. 
 Sometimes user may not give correct informa-
tion (like missing Train name, Station name or 
query does not belong to any of the query frames 
etc.). At that time DM generates error message 
describing that missed information. In another 
case user asks questions without knowledge. In 
this case DM generates a cooperative message, 
which will help the user in further requests. 
As a basis for the above tasks the DM utilizes 
the dialogue history. Here dialogue history re-
cords the focal information, i.e what has been 
talked in the past and what is talking at present. 
It is used for dialogue control and disambigua-
tion of context dependent requests. The DM gets 
a semantic frame from the other system compo-
nents. This frame is filled by interpreting the re-
quest in the context of the ongoing dialogue, 
domain knowledge, and dialogue history. The 
DM then prompts for missing information or 
sends a SQL query. Before the query is sent off, 
DM checks whether new information is con-
tained in the query or the information is contra-
dictory to information given before. If this is the 
case then the DM can either keep the original 
information or replace it with the new one in the 
dialogue history or engage in a confirmation sub-
dialogue. 
The DM looks at the query after language 
processing has been completed (but before the 
formal query is issued), as well as after the result 
has been obtained from the formal query. The 
accuracy of the system mainly depends on the 
representation of the dialogue history and how 
the DM responds to the user’s dialogue. 
3.6 SQL Generation 
Once the query frame is selected for a ques-
tion, the corresponding procedure for the SQL 
query generation is called. For each query frame 
there is a procedure for SQL statement(s) genera-
tion. In order to generate the SQL query, it needs 
the tokens generated by the query analyzer. 
If the tokens are presented in the current 
query, it uses them. Otherwise it gets the token 
information from the dialogue history. For ex-
ample, in the arrival time queries user has to 
specify Train name/no and station/city name 
where he/she needs to go. If he/she did not men-
tion that information, SQL generation procedure 
gets the information from the dialogue history. 
Figure 2 depicts the conversion of natural lan-
guage query to its SQL query.  
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 2: Interpreting the natural language  
question to the SQL query 
 
For the fare related query, SQL generation 
procedure would be called depending on the type 
of train. The procedure considers that the user 
will provide the train name and reservation class. 
If the train is of Express type, it considers that 
the user may provide either the source and desti-
nation stations of journey or the distance of jour-
ney. If it is of Rajdhani type, it considers that the 
user may provide source and destination station 
of journey. Similarly for the other query frames, 
SQL generation procedure considers that the user 
provide the necessary information. 
ˆý§´ýðœÀ–þœ §¨´ýÙþ¦ −´ýœ¯´ýœ ˆý¦ýûô§´ý©¾ý ”–ýœÕýÖþœÕ–ýœ–ýœ ¯ŸýÛìÃþ³ÕýÂšþ (ep-
pudu falaknuma express gunturuku veltundi 
When the Falaknuma Express goes to Gun-
tur])? 
 
Train name: §¨´ýÙþ¦ −´ýœ¯´ýœ ˆý¦ýûô§´ý©¾ý (Falaknuma Ex-
press) 
Station name: ”–ýœÕýÖþœÕ–ýœ (Guntur)  
Keywords: ˆý§´ýðœÀ–þœ (eppudu [when]), ¯ŸýÛìÃþ³ÕýÂšþ 
(veltundi [goes]).  
 
The [Arr_Time] Query frame is selected. 
 
The system checks with the user for up/down 
journey of the train  
 
Let user asked about upward journey of train 
via DM. 
 
SELECT Arr_Time FROM Schedule2703 
WHERE StationName=’”–ýœÕýÖþœÕ–ýœ’. 
EACL 2006 Workshop on Multilingual Question Answering - MLQA06
57
3.7 Answer Generation 
Once the SQL statement for an input query 
statement is generated, it is triggered on the da-
tabase and the retrieved information is used to 
represent the answer. The retrieved information 
is updated in the dialogue history for further ref-
erence.  
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3: Generating answer from the retrieved 
result. 
 
Each query frame has its corresponding An-
swer generator. We use template based answer 
generation method. Each template consists of 
several slots. Those slots are filled by the re-
trieved answer and the tokens generated from the 
query. Figure 3 shows the answer generation 
from the SQL query generated from the natural 
language query shown in Figure 2. The answer 
will be sent to Dialogue Manager, which will 
further send it to the user. 
4 Evaluation 
For evaluating our system we have taken queries 
from our Telugu-speaking friends. We have de-
scribed the Railway Information system to them. 
They have also been told about the constraints on 
the nature of queries in the systems. They have 
also been shown list of example queries for the 
systems. 
Here we are considering two measures for 
evaluating our system: Dialogue success rate and 
Precision. The QA system was evaluated by giv-
ing 26 sets of dialogue consisting 95 natural lan-
guage queries in total. The two evaluation meas-
ures are defined as follows:  
 
Dialogue success rate for each set=Number of 
Answers or Responses generated by the system 
/Number of turns issued by the user. 
Dialogue success rate = (∑ Dialogue success rate 
for each set / Number of sets of dialogues)*100. 
Precision= (Number of correct answers given by 
the system/Number of answers given by the sys-
tem)*100. 
The number of turns issued by the user in a 
dialogue is the total of the number of questions 
issued to the system and the number of responses 
provided by the user to the system. 
Each set of dialogue consisted of around 3 to 5 
natural language queries. The total dialogue suc-
cess rate for the 26 sets was obtained as 21.83. 
The dialogue success rate for the system is calcu-
lated as  
Dialogue success rate= (21.83/26)*100= 83.96%. 
Out of 95 questions, system generated answers 
for 82 questions of which 79 were correct an-
swers. So, the precision of the system is calcu-
lated as  
Precision= (79/82)*100= 96.34%. 
This low dialogue success rate is due to the 
fact that the system coverage of the domain is 
not extensive enough, i.e., query frames for some 
natural language queries were not correctly iden-
tified. The information given by the user in the 
query was sometimes inadequate and the system 
was not able to identify the missing information 
because of the incorrect choice of the query 
frame. Sometimes the system is unable to obtain 
tokens correctly from the input query even if it 
had identified the right query frame, thereby 
generating wrong answers. Misinterpretation of 
dialogue history is also another problem.   
5 Conclusion 
In this dialogue based QA system following the 
keyword based approach, each word need not be 
found in the knowledge base. Only the words 
that contain semantic information needs to be 
found in the knowledge base. 
  By restricting the coverage of questions, our 
system could achieve relatively high dialogue 
success rate. However, for a real practical system 
this success rate must be improved. 
In extension to our work we are developing 
the modules for the remaining query frames. The 
system needs to be upgraded so that a user can 
query for railway information over phone. The 
speech input can be converted to textual query. 
This textual query can be input of our system and 
the textual out can be converted to speech again 
to answer the user. 

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