Generating Multiple Diverse Responses for Short-Text Conversation
نویسندگان
چکیده
منابع مشابه
Neural Responding Machine for Short-Text Conversation
We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoder-decoder framework: it formalizes the generation of response as a decoding process based on the latent representation of the input text, while both encoding and decoding are realized with recurrent neural networks (RNN). The NRM is trained with a large ...
متن کاملGenerating Multiple Diverse Counterexamples for an EFSM
Model checking is a powerful technique for debugging a system description because it generates a counterexample showing a path of the system that fails a property. Instead of the traditional cycle of find bug – fix bug – re-run model checker, often we would like to study multiple bugs before fixing the model to help isolate the cause of the error and to improve the user’s experience by avoiding...
متن کاملAnalysis of Similarity Measures between Short Text for the NTCIR-12 Short Text Conversation Task
According to rise of social networking services, short text like micro-blogs has become a valuable resource for practical applications. When using text data in applications, similarity estimation between text is an important process. Conventional methods have assumed that an input text is sufficiently long such that we can rely on statistical approaches, e.g., counting word occurrences. However...
متن کاملCYUT Short Text Conversation System for NTCIR-12 STC
In this paper, we report how we build the system for Chinese subtask in NTCIR12 Short Text Conversation (STC) shared task. Our approach is to find the most related sentences for a given input sentence. The system is implemented based on the Lucene search engine. The result shows that our system can deal with the conversation that involves related sentences.
متن کاملTowards Implicit Content-Introducing for Generative Short-Text Conversation Systems
The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq model is prone to generate trivial responses. In this paper, we aim to generate a more meaningful and informative reply when answering a given quest...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33016383