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Found 17 papers, 1 papers with code
Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation
There are plenty of studies showing that the knowledge distillation is efficient in transferring the knowledge from BERT into the model with a smaller size of parameters.
Guiding Variational Response Generator to Exploit Persona
Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years.
Improving the Robustness of Deep Reading Comprehension Models by Leveraging Syntax Prior
Despite the remarkable progress on Machine Reading Comprehension (MRC) with the help of open-source datasets, recent studies indicate that most of the current MRC systems unfortunately suffer from weak robustness against adversarial samples.
LocalGAN: Modeling Local Distributions for Adversarial Response Generation
This paper presents a new methodology for modeling the local semantic distribution of responses to a given query in the human-conversation corpus, and on this basis, explores a specified adversarial learning mechanism for training Neural Response Generation (NRG) models to build conversational agents.
MemeFaceGenerator: Adversarial Synthesis of Chinese Meme-face from Natural Sentences
Chinese meme-face is a special kind of internet subculture widely spread in Chinese Social Community Networks.
Learning to Generate Structured Queries from Natural Language with Indirect Supervision
Generating structured query language (SQL) from natural language is an emerging research topic.
Why Do Neural Response Generation Models Prefer Universal Replies?
Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task.
A Prospective-Performance Network to Alleviate Myopia in Beam Search for Response Generation
Generative dialog models usually adopt beam search as the inference method to generate responses.
LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics
The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably.
Group Linguistic Bias Aware Neural Response Generation
For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users{'} preference on language styles, topics, etc.
Predicting Users' Negative Feedbacks in Multi-Turn Human-Computer Dialogues
Besides, strategies of obtaining distance supervision data for pre-training are also discussed in this work.
Neural Response Generation via GAN with an Approximate Embedding Layer
This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated ones.
Ranking Responses Oriented to Conversational Relevance in Chat-bots
For automatic chatting systems, it is indeed a great challenge to reply the given query considering the conversation history, rather than based on the query only.
Incorporating Loose-Structured Knowledge into Conversation Modeling via Recall-Gate LSTM
Modeling human conversations is the essence for building satisfying chat-bots with multi-turn dialog ability.
