MaSMT: A Multi-agent System Development Framework for English-Sinhala Machine Translation

Transcription

1 MaSMT: A Multi-agent System Development Framework for English-Sinhala Machine Translation B. Hettige #1, A. S. Karunananda *2, G. Rzevski *3 # Department of Statistics and Computer Science, University of Sri Jayewardenepura, Nugegoda, Sri Lanka 1 budditha@dscs.sjp.ac.lk * Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2 asoka@itfac.mrt.ac.lk 3 rzevski@gmail.com Abstract Multi-agent System gives high quality solutions through communication, negotiation and coordination among agents. Agents are small self-contained computational objects capable of exchanging messages among themselves. Number of general purpose toolkits and frameworks are available to develop Multi-agent systems for modelling complex real world problems. However, none of them has been specialized for the area of natural language processing specially machine translation. MaSMT is a java based multi-agent system development framework, especially designed for development of English to Sinhala machine translation system. MaSMT provides two types of agents, namely ordinary agents and manager agents. A manager agent consists of number of ordinary agents within its control. Further, manager agents can directly communicate with other manager agents and each and every ordinary agent in the swarm is assigned to a particular manager agent. An ordinary agent in a swarm can directly communicate only with the agents in its own swarm and its manager agent. The framework primarily implements object-object communication, XML-based data passing and MySQL database connectivity to use agents ontology and message passing. Agent communication in the framework has been implemented to comply with FIFA-ACL specification. MaSMT framework is used to develop English to Sinhala machine translation system and Word Reader which is capable of analysing a given English word. Experimental result shows that, MaSMT framework can be used to develop Natural Language Processing applications successfully. Keywords Machine Translation, Multi-agent Systems. I. INTRODUCTION Multi-agent System (MAS) technology has emerged as a new software paradigm, which exploits the power of message passing as the key strategy for problem solving. Communication, negotiation and coordination among agents produce high quality solutions that cannot be generated by a single agent in its individual capacity. Nowadays hundreds of well-established general purpose toolkits and frameworks are available for the development of Multi-agent Systems. Among others, JADE [1], Jason [2], AgentBuilder [3] and SeSAm [4] are the standard Multi-agent System development frameworks. JADE (Java Agent DEvelopment Framework) is a software framework fully implemented in Java language. JADE framework provides supporting GUI tools for debugging and deployment phases in multi agent developments. Jason is an interpreter for an extended version of AgentSpeak [5]. AgentBuilder is an integrated software development tool that allows software developers to build agents quickly and easily without sound knowledge of Multi-agent technology. SeSAm (Shell for Simulated Agent Systems) is another framework that provides a generic environment for modelling and experimenting with agent-based simulation. Further, agent development framework saves developers time and provides standardization of the multi-agent system development [6]. Many of these frameworks are especially designed to develop general purpose applications, machine learning and simulations of complex systems. However, these existing frameworks do not support the distinct requirements of Machine Translations, coming under area of Natural Language Processing. Machine Translation System is computer software that translates text or voice from one natural language into another with or without human assistance [6]. Machine Translation system produces translation through three major steps including analysis of source language text, translation and generation of the texts in the target language [7]. These sub systems are required to handle morphology, syntax and semantic aspects of two languages. Words in a language are employed as the building block of natural language understanding. This is valid for people who read a sentence word by word or otherwise by locating selected words such as nouns and verbs. Consequently, meaning of a sentence is determined by the interaction among words, which draw from all aspects of morphology, syntax and semantic, as appropriate. Words in a sentence as agents, [8] is the philosophy behind to design English to Sinhala machine translation system. These word agents pass messages among them within and across different level of analysis. As such, this machine translation approach is different from existing ones that sequentially define linguistic aspects such as morphological, syntax and semantics analysis. Further, English to Sinhala Machine Translation system is capable of processing English Morphological analysis, English Syntax analysis, English to Sinhala Semantic level translation, Sinhala Morphological generation and Sinhala syntax generation through the Multi-agent approach [9].

2 MaSMT (Multi-agent System for Machine Translation) is a Java based Multi agent system development framework, which is design to develop English to Sinhala machine translation solution. MaSMT provides two types of agents, namely ordinary agents and manager agents. Further, manager agents can directly communicate with other manager agents and each and every ordinary agent in the swarm is assigned to a particular manager agent. An ordinary agent in a swarm can directly communicate only with the agents in its own swarm and its manager agent. The framework primarily implements object-object communication, XML-based data passing and MySQL database connectivity for message passing in order to use domain ontology. Agent communication in the framework has been implemented to comply with FIFA-ACL [10] specification This paper reports the design and implementation of the MaSMT which is designed to develop Natural language processing applications especially for machine translation systems. The design architecture, class module and agent communication methods are also given in the paper. The rest of the paper is organized as follows. Section 2 reports brief summary of the existing frameworks and systems for Multi-agent system development. Section 3 gives design of the MaSMT with brief description of each module. The section 4 demonstrates applications of MaSMT. Then section 5 gives how MaSMT works as an English Morphological analyzer. Finally section 6 gives conclusion and further works of the project. II. EXISTING MULTI-AGENT SYSTEM DEVELOPMENT FRAME WORKS Multi-agent systems explore new software paradigms to model complex systems. These systems are large networks of small agents, run in parallel [11]. Performance of the MAS depends on how agents are modelled and capability of negotiation of each agent. Therefore, different approaches are used to model agents including common standard as well as ad-hoc development methods. Numbers of agent development frameworks have supported to develop Multi-agent technology with agent development standard such as the FIPA-ACL and KQML [12]. This section gives a brief description about selected standard Multi-agent development frameworks. JADE (Java Agent DEvelopment Framework) is a free and open source software Framework fully implemented in Java language. JADE is a middle-ware that complies with the FIPA specifications. JADE provides supporting GUI tools for debugging and deployment phases in the Multi Agent development. This framework can be used to develop distributed systems capable to control agents via a remote GUI. The latest version of JADE (4.3.0) was released on A number of applications are already developed through JADE [13]. Json is an Open Source fully java based Multi-agent development framework, which is developed through an improved version of agent oriented language AgentSpeak. Through Json, developers can easily programme agent behaviour of individual agents [14]. Json comes with several features including strong negation, speech-act based interagent communication and possibility to run a multi-agent system distributed over a network. AgentBuilder is an integrated software development tool to develop general purpose multi-agent systems that allows software developers with no knowledge in intelligent agent technologies to quickly and easily build intelligent agentbased applications. AgentBuilder consists of three versions such as Lite, Pro and Pacs. AgentBuilder Lite provides several tools for development including Project Manager and Ontology Manager. The Ontology Manager provides tools for creating ontologies and automatic code generation using graphical object modelling tools. AgentBuilder Lite distributions are available for the Windows and Linux Platform. SeSAm (Shell for Simulated Agent Systems) provides a generic environment for modelling and experimenting with agent-based simulation. SeSAm provides tool for construction of complex models easily. SeSAm consist of several features and tools including GUI based agent modelling, integrated graphical simulation analysis, distributed simulation capability etc. At present, SeSAm is applied in many application domains including, logistics, production, traffic, passenger flow and urban planning etc. ADK (Agent Development Kit) [15] is an open source large scale distributed agent development application which can be used to develop mobile (distributed) agents. ADK allows Java Developers to easily build, deploy and manage secure, large-scale distributed solutions. Many of these frameworks are especially designed to develop general purpose applications, machine learning and simulations of complex systems. However, these existing frameworks do not support distinct requirements of Machine Translations systems, coming under the area of Natural Language Processing. There are limited numbers of research available for use of Multi-agent technology for natural language processing. Aref and others have developed a multi-agent system for natural language understanding [16]. This system uses lexical structural approach and a cognitive structural approach to understand text through the speech-to-text, text-to-speech, morphological, semantic, discourse, and query analysing systems Minakow and others [17] have developed a Multi-agent based text understanding system for car insurance. This system uses Multi-agent system based approach to understand a given text. The system uses four steps to text understanding, namely morphological analysis, syntax analysis, semantic analysis and pragmatic analysis. To analyse, the whole text is divided into sentences. Then first three stages are applied to each sentence. After analysing each paragraph, text is passed to pragmatic analysis. Stefanini and others [18] have also developed a Multi-agent based general Natural Language Processing system named Talisman. The Talisman agents can communicate with each other without the central control. These agents are capable of

3 directly exchanging information using an interaction language. Linguistic agents are governed by a set of local rules. The TALISMAN deals with ambiguities and provides a distributed algorithm for conflict resolutions arising from uncertain information. Compare with exiting agent development frameworks, there are no agent development framework specific to Machine Translation. III. DESIGN MaSMT is a Java-based Multi-agent development framework, which can be used to develop machine translation applications in general. The framework has been implemented by using JAVA. It consist of 5 modules namely ordinary agents, manager agents, global message space, local message space and ontology. Fig. 1 shows the design diagram of the MaSMT. Brief description of the each module is given below. Multi-agent system. Further, manager agent can control the priority of the client agents and the stage of the clients. This facility is used to remove unnecessary workload from its client agents. B. Ordinary Agents Ordinary agents are worked under the control of the manager agent and each ordinary agent must have a manager agent. MaSMT agent is a simple java program (Thread) which support limited task(s). These agents can communicate with each other through the messages space by using peer-peer or object-object communication methods. This ordinary agent consists of local message queue, access rule, communication module and the ontology. Fig. 2 shows the design of an ordinary agent. Global Message Space Agent Manager Agent #1 Local Message Space A. Manager Agent Agent #2 Agent #3 Agent #4 Agent #n Fig. 1: Design of the MaSMT framework Ontology Agent Manager is an agent (java thread) of the system that manages its client agents. According to the MaSMT architecture, each manager can fully control its client agents. Therefore, manager can create, remove or control its client agent(s). The Manager agent in the MaSMT, creates all its clients automatically at the initialization stage. This agent accesses the rule-base (agents ontology) and assigns each rule for a client agent. It means, manager agent creates an agent for a rule which is available in the rule-base. In addition, manager can directly access its client agents and send messages directly. The Manager agent reads input massages from the global message queue and assigns relevant tasks for the client agents. These messages are sent by the other manager agents in the Fig. 2. Design of the MaSMT agent These agents respond for the messages which are available in its local message queue. Each agent is assigned for only a limited task and it responses only for the assigned task (task is available as an access rule). For instance, morphological agents in the English to Sinhala machine translation system response only for the two messages namely who are you and who am i. The agent reserve message who are you from the message queue and then provides information about itself to the message sender by using message space. After reserving the message who am i, it tries to do the morphological analysis with the support of the rules and its ontology. The communication module gives way to access ontology and the message spaces through a given media such as MySQL database Access, Objet-Object access, XML database access, peer-to-peer network access or client-server communication. C. Local Message Space Local message space is the visible area of the each agent in the local agent group (Swam). Each agent can directly communicate with each other through the local message space. An agent in a given group has a local message queue with public access. Manager agent and other client agents in the

4 group can directly access this message queue through the object-object communication method. D. Global Message space Global message space is used to communicate among managers in the MaSMT framework. This message space is visible only for the managers who are in the MaSMT framework. Mangers send messages with the support of the communication module. In the distributed environment, managers work in different locations and communicate with each other through the client-server or peer-to-peer communication. E. Ontology Ontology is the knowledge of each agent (ordinary and manager agents). Agent uses ontology to make the action. For instance, English morphological agents in the English to Sinhala machine translation system use English dictionary as the ontology. Morphological rules of Morphological agents are also stored in the ontology. based English to Sinhala Machine Translation System has been design through the MaSMT framework. English to Sinhala machine translation system consist of 6 sub-systems, namely English Morphological analyser, English Syntax analyser, English Semantic analyser, English pragmatic analyser, Sinhala Syntax generator and Sinhala Morphological generator. Section A gives more details about English Morphological analysis in the English to Sinhala Machine Translation System. A. English Morphological Analyser Figure 3 shows the top level design of the Multi-agent based English Morphological analyser. According to the MaSMT architecture, English Morphological analyser consists of Morphological manager and 22 client agents to handle morphology of the English language. English dictionary is used as ontology of the morphological system including lexical resource and morphological rules. F. MaSMT Messages Messages are used to communicate among each other. These messages have been developed by using FIPA-ACL message standard. ACL Message consists with Participant in communication: sender, receiver, reply-to, Content of message: content, Description of Content: language, encoding, ontology. Control of conversation: protocol, conversation-id, reply-with, in-reply-to, reply-by etc. MaSMT uses MaSMTMmessage class to handle all the messages in the framework which is implemented based on ACL messages. G. Communication Modules Communication modules are used to communicate among agents. These modules also give full support to develop MaSMT as a distributed system. In addition to the common MySQL database access, Objet-Object access and XML database access, system can communicate with client-server or peer-to-peer network modes. H. Message Reader Message reader is the supporting tool for developers that can be used to show each message in the selected message queue(s). This tool reads the existing messages in the given message queue and display them. As a result, developers can used this reader as a debugging tool to view how agents communicate with others. I. Agent Monitor Agent monitor is the controlling tool that can be used to monitor the agent s state such as active, busy, wait or dead. Through this monitoring tool, developers can manually control the selected agent stage and monitor the behaviour of the Multi A-agent system. IV. APPLICATIONS OF MASMT MaSMT framework is especially designed to develop Machine Translation applications. For instance, Multi-agent Fig. 3 Design of the English Morphological analyser The English dictionary consists of more than English words including more than regular and irregular nouns, more than verbs and more than 5000 adjectives. This analyser has been designed as a desktop application. Therefore all the modules in the system are available in a local machine. English morphological manager communicates with its client agents through the direct object-object communication and ontology access is provided with the support of MySQL database. Fig 4 shows the Graphical user interface of the English Morphological Analyser. B. Word Reader Word Reader is a distributed Multi agent System that can be used to completely analysis a given English word. This Multi-agent system consists of five modules namely, GUI manager of the word reader, Morphological Manager,

5 conjugation manager, context manager and the dictionary manager. These MaSMT managers are activated on different locations and communicate among others through the MaSMT communication modules. Fig 5 shows the design diagram of the word reader. bilingual dictionary. The English-Sinhala bilingual dictionary consists of more than English-Sinhala words, which is designed and developed for the English to Sinhala Machine Translation. Context manager is another sub system that gives sample sentences for the given word to get idea about context. The context manager get results from search engines and get the sample sentence through the available web resources. Fig. 4. GUI of the English Morphological Analyser V. HOW MASMT WORKS This section presents how MaSMT framework was used as an English Morphological analyser. For instance English Morphological analyser reads the sentence the boy reads books everyday as an input. Then word agent manager creates 5 word agents to represent each word in the input sentence. After that, each word agents are activated and send message whoami to emaagents (English Morphological agent) to identify its morphology. Following sample message code shows word agents sent message who am i to the English Morphological agents. [wordagent(the)] --- whoami --> [emaagents] [wordagent(boy)] --- whoami --> [emaagents] [wordagent(reads)] --- whoami --> [emaagents] [wordagent(books)] --- whoami --> [emaagents] [wordagent(everyday)] --- whoami --> [emaagents] These messages were read by the Morphological manager and was sent to its clients (add message to their local message queue). At this point each morphological agent reserved 5 messages from its manger which was sent by the word agents. The following sample message code shows how English morphological agent get message who am i form word agents. [emaagents(14)] <-- whoami -- [wordagent(the)] [emaagents(13)] <-- whoami -- [wordagent(boy)] [emaagents(6)] <-- whoami -- [wordagent(reads)] Fig. 5. Design of the MaSMT word reader GUI manager of the word reader is the main module of the distributed Multi-agent system. This manager send messages to other managers through the communication modules and get reply from them. English Morphological manager of the Word reader analyse the morphology of the given English word. After analysing a word through its client agents (morphological agents) this manager send reply to GUI manager. GUI manager reserve these reply messages from Morphological manager and extract the relevant information and display appropriately. The conjugation manager works as a word generator (conjugator) that gives all the conjugation forms of the given English word. For instance, this Conjugator gives the words wife (singular), wives (Plural), wife's (Singular Possessive) and wives' (Plural Possessive) for the input English word wife. The dictionary manager gives related Sinhala word(s) for the given English word with support of the English-Sinhala The morphological agents received these messages from its local message queue. After processing the task of the read messages, agent get next message available in its local message queue until the local message queue is empty. Each morphological agent contains a morphological rule(s). Task of the each morphological agent depended on the above morphological rules. Morphological agent tries to apply its morphological rule to the given word which was sent by the word agent. If the rule is accepted then English morphological agent sends the relevant morphological information to the appropriate word agent through its morphological manager. The following sample message code shows how English morphological agents sent morphological information to the relevant word agents. [emaagents(9)] -- det-drw- --> [wordagent(the)] [emaagents(0)] -- veb-spr --> [wordagent(books)] [emaagents(4)] -- nun-tpe-nue-plu --> [wordagent(books)] [emaagents(14)] -- veb-inf-spr --> [wordagent(reads)]

6 [emaagents(2)] -- nun-tpe-nue-sin --> [wordagent(boy)] [emaagents(10)] -- adj- --> [wordagent(everyday)] Note that, morphological agents are run in parallel in its local group. Therefore, there is no order to get morphological information from the English morphological agents. In the above example morphological information of the word books have been reserved before the morphological information of the words reads and boy. VI. CONCLUSIONS & FURTHER WORKS This paper has reported, design of the multi-agent development framework namely MasMT. MaSMT is specific to develop Multi-agent based Natural Language Processing applications. MaSMT provides two types of agents (ordinary agents and manager agents) for agent modelling and 4 communication frameworks (object-object, XML based, MySQL database and TCP/UDP network connection) are used to communicate among agents. Agent communication messages of the MaSMT have been developed with support of the ACL standard messages. Through the MaSMT framework, the English Morphological analyser has been implemented with 22 ordinary agents and a manager agent to represent English morphological rules. Ontology of the system consist of more than English words including more than regular and irregular nouns, more than verbs and more than 5000 adjectives. Through the word reader, MaSMT shows the distributed Multi-agent system development capabilities. Experimental result shows that, MaSMT can be used to develop Natural Language Processing applications as well as distributed Multiagent systems. Use of the MaSMT framework to implement the other phases of the English to Sinhala Machine Translation system such as English syntax analysis, Sinhala syntax generation etc., is considered as the key further work of the research. REFERENCES [1] (2013) The JADE website, [online], Available: [2] (2013) The Jason website [online], Available: [3] (2013) AgentBuilder, [online], Available: [4] (2013) SeSAm, [Online], Available: [5] M. Dinverno, M. Luck, Engineering AgentSpeak(L): A Formal Computational Model, Logic Computat., Vol. 8 No. 3, pp. 1-27, [6] (2013) The Wikipedia website, [online], Available: [7] B. Hettige, A. S. Karunananda, "Computational Model of Grammar for English to Sinhala Machine Translation", Proceedings of the International Conference on Advances in ICT for Emerging Regions - ICTer2011, Colombo, 2011 [8] B. Hettige, A. S. Karunananda, "A Word as an Agent for Multi-agent based Machine Translation", Proceedings of the ITRU Research Symposium, Moratuwa, [9] B. Hettige, A. S. Karunananda and, G. Rzevski, Multi-agent System Technology for English-Sinhala Morphological Analysis Proceedings of the Ninth Annual Sessions, Sri Lanka Association for Artificial Intelligence (SLAAI), [10] (2013) FIPA:Agent Communication specifications, [online], Available: [11] G. Rzevski, A new direction of research into Artificial Intelligence, Sri Lanka Association for Artificial Intelligence 5th Annual Sessions [12] T. Finin, Y. Labrou, J. Mayfield, KQML as an agent communication language, Software Agents, MIT Press, Cambridge, [13] F. L. Bellifemine, G. Caire, D. Greenwood, Developing Multi-Agent Systems with JADE, John Wiley & Sons, Ltd, [14] R. H. Bordini, J. F. Hübner, M. Wooldridge, Programming Multi-agent Systems in AgentSpeak Using Jason, John Wiley & Sons Ltd, England, [15] (2013) Agent Development Kit (ADK) [online] available: [16] M. M. Aref, A multi-agent system for natural language understanding, Integration of Knowledge Intensive Multi-Agent Systems, [17] I. Minakov and et al., Creating Contract Templates for Car Insurance Using Multi-Agent Based Text Understanding and Clustering, Third International Conference on Industrial Applications of Holonic and Multi-Agent Systems., HoloMAS:, 2007, pp [18] M. H. Stefanini, Y. Demazeau, TALISMAN: A multi-agent system for natural language processing, In Proceedings of SBIA'95. - Springer Verlag:, 1995, pp