A Neural Conversational Model A Neural Conversational Model
Paper summary #### Introduction * The paper presents a domain agnostic approach for conversational modelling based on [Sequence to Sequence Learning Framework](https://gist.github.com/shagunsodhani/e3608ccf262d6e5a6b537128c917c92c). * [Link to the paper](http://arxiv.org/abs/1506.05869) #### Model * Neural Conversational Model (NCM) * A Recurrent Neural Network (RNN) reads the input sentence, one token at a time, and predicts the output sequence, one token at a time. * Learns by backpropagation. * The model maximises the cross entropy of correct sequence given its context. * Greedy inference approach where predicted output token is used as input to predict the next output token. #### Dataset * IT HelpDesk dataset of conversations about computer related issues. * OpenSubtitles dataset containing movie conversations. #### Results * The paper has reported some samples of conversations generated by the interaction between human actor and the NCM. * NCM reports lower perplexity as compared to n-grams model. * NCM outperforms CleverBot in a subjective test involving human evaluators to grade the two systems. #### Strengths * Domain-agnostic. * End-To-End training without handcrafted rules. * Underlying architecture (Sequence To Sequence Framework) can be leveraged for machine translation, question answering etc. #### Weakness * The responses are simple, short and at times inconsistent. * The objective function of Sequence To Sequence Framework is not designed to capture the objective of conversational models.
A Neural Conversational Model
Vinyals, Oriol and Le, Quoc V.
arXiv e-Print archive - 2015 via Local Bibsonomy
Keywords: dblp

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