| Title | Neural Encoder-Decoder based Urdu Conversational Agent |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Alam, Mehreen |
| Conference Name | 2018 9th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON) |
| Keywords | attention driven deep encoder-decoder based neural conversational agent, chatbot, conversation agents, conversational agent, conversational agents, conversational model, deep encoder-decoder based neural conversational agent, Deep Learning, encoding, Human Behavior, knowledge based systems, language translation, machine learning, Metrics, multi-agent systems, natural language processing, neural encoder-decoder based Urdu conversational agent, neural network based techniques, pubcrawl, recurrent neural nets, recurrent neural network, rule based methods, Scalability, sequence to sequence, Urdu language |
| Abstract | Conversational agents have very much become part of our lives since the renaissance of neural network based "neural conversational agents". Previously used manually annotated and rule based methods lacked the scalability and generalization capabilities of the neural conversational agents. A neural conversational agent has two parts: at one end an encoder understands the question while the other end a decoder prepares and outputs the corresponding answer to the question asked. Both the parts are typically designed using recurrent neural network and its variants and trained in an end-to-end fashion. Although conversation agents for other languages have been developed, Urdu language has seen very less progress in building of conversational agents. Especially recent state of the art neural network based techniques have not been explored yet. In this paper, we design an attention driven deep encoder-decoder based neural conversational agent for Urdu language. Overall, we make following contributions we (i) create a dataset of 5000 question-answer pairs, and (ii) present a new deep encoder-decoder based conversational agent for Urdu language. For our work, we limit the knowledge base of our agent to general knowledge regarding Pakistan. Our best model has the BLEU score of 58 and gives syntactically and semantically correct answers in majority of the cases. |
| DOI | 10.1109/UEMCON.2018.8796688 |
| Citation Key | alam_neural_2018 |
Neural Encoder-Decoder based Urdu Conversational Agent