Personalized Dialogue Generation with Diversified Traits

Endowing a dialogue system with particular personality traits is essential to deliver more human-like conversations. However, due to the challenge of embodying personality via language expression and the lack of large-scale persona-labeled dialogue data, this research problem is still far from well-studied. In this paper, we investigate the problem of incorporating explicit personality traits in dialogue generation to deliver personalized dialogues. To this end, firstly, we construct PersonalDialog, a large-scale multi-turn dialogue dataset containing various traits from a large number of speakers. The dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers. Each utterance is associated with a speaker who is marked with traits like Age, Gender, Location, Interest Tags, etc. Several anonymization schemes are designed to protect the privacy of each speaker. This large-scale dataset will facilitate not only the study of personalized dialogue generation, but also other researches on sociolinguistics or social science. Secondly, to study how personality traits can be captured and addressed in dialogue generation, we propose persona-aware dialogue generation models within the sequence to sequence learning framework. Explicit personality traits (structured by key-value pairs) are embedded using a trait fusion module. During the decoding process, two techniques, namely persona-aware attention and persona-aware bias, are devised to capture and address trait-related information. Experiments demonstrate that our model is able to address proper traits in different contexts. Case studies also show interesting results for this challenging research problem.Comment: Please contact [zhengyinhe1 at 163 dot com] for the PersonalDialog datase

Out-of-domain Detection for Natural Language Understanding in Dialog Systems

Natural Language Understanding (NLU) is a vital component of dialogue systems, and its ability to detect Out-of-Domain (OOD) inputs is critical in practical applications, since the acceptance of the OOD input that is unsupported by the current system may lead to catastrophic failure. However, most existing OOD detection methods rely heavily on manually labeled OOD samples and cannot take full advantage of unlabeled data. This limits the feasibility of these models in practical applications. In this paper, we propose a novel model to generate high-quality pseudo OOD samples that are akin to IN-Domain (IND) input utterances, and thereby improves the performance of OOD detection. To this end, an autoencoder is trained to map an input utterance into a latent code. and the codes of IND and OOD samples are trained to be indistinguishable by utilizing a generative adversarial network. To provide more supervision signals, an auxiliary classifier is introduced to regularize the generated OOD samples to have indistinguishable intent labels. Experiments show that these pseudo OOD samples generated by our model can be used to effectively improve OOD detection in NLU. Besides, we also demonstrate that the effectiveness of these pseudo OOD data can be further improved by efficiently utilizing unlabeled data.Comment: Accepted by TALS

A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.Comment: Long paper accepted at AAAI 202

Building a Large-scale Persona Dialog Dataset

We proposed a primary version of a large scale multi-turn dialogue dataset in Chinese that contains over 25 million sessions of dialogues crawled from Weibo1. Diversified personality traits for each dialogue participant are collected to facilitate modelling persona in dialogues. Our dataset fills the blank of the resources for building personalised dialogue systems in open-domain conversations and can also serves as an important resource for a wide range of studies

Building a Large-scale Persona Dialog Dataset

We proposed a primary version of a large scale multi-turn dialogue dataset in Chinese that contains over 25 million sessions of dialogues crawled from Weibo1. Diversified personality traits for each dialogue participant are collected to facilitate modelling persona in dialogues. Our dataset fills the blank of the resources for building personalised dialogue systems in open-domain conversations and can also serves as an important resource for a wide range of studies

Analysis of the removability and stability of rock blocks by considering the rock bridge effect

In traditional block theory, the removability and stability of rock blocks are analyzed independently; that is, the stability of a removable block is analyzed in detail, and non-removable blocks are regarded as stable. However, in practical situations, non-removable blocks may pose more danger than removable blocks. This paper presents a unified method for analyzing the removability and stability of rock blocks. In this method, the cracking of rock bridges is considered and non-removable blocks are not assumed to be stable. First, possible cracking rock bridges are identified by extending finite-sized fractures and comparing the boundary surfaces of the resulting blocks with those of the original blocks. Then, the sliding direction associated with each possible moving block is determined by solving an optimization problem. The normal force acting on each sliding surface is determined, and the resisting force on each rock bridge is calculated and integrated into the total resisting force when calculating the safety factor of a possible moving block. Procedures to determine all possible moving blocks are introduced, and the possible moving block with the minimum safety factor is regarded as the actual moving block. The corresponding minimum safety factor is defined as the actual safety factor of the block. The proposed method is verified by considering a few examples. The results show that non-removable blocks may be unstable if the cracking of rock bridges is considered.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author