Reference

Reference

for - AI evolution - prediction - 2027 - AI agent will replace AI chatbot

ChatGPT is like autocomplete

ChatGPT's synthesis of information versus Google Search's list of links

The key difference here, though, is that with a list of links, one can follow the links and evaluate the sources. With a ChatGPT response, there are no citations to the sources—just an amalgamation of statements that may or may not be true.

Difference between a calculator and ChatGPT: deterministic versus probabilistic

Brand, C. O., & Stafford, T. (2021). Covid-19 vaccine dialogues increase vaccination intentions and attitudes in a vaccine-hesitant UK population. PsyArXiv. https://doi.org/10.31234/osf.io/kz2yh

Facebook AI. (2021, July 16). We’ve built and open-sourced BlenderBot 2.0, the first #chatbot that can store and access long-term memory, search the internet for timely information, and converse intelligently on nearly any topic. It’s a significant advancement in conversational AI. https://t.co/H17Dk6m1Vx https://t.co/0BC5oQMEck [Tweet]. @facebookai. https://twitter.com/facebookai/status/1416029884179271684

This looks affordable lol. I should definitely try it.

NLPの処理はいろんな領域で運用できる：レコメンデーション、パーソナルアシスタント

”会話形式”だと推測している？

Find out how to build a chatbot in this article.

alimi

Deep Learning for Chatbots

A Network-based End-to-End Trainable Task-oriented Dialogue System

• https://github.com/shawnwun/NNDIAL

这个end-to-end的系统，在意图识别的阶段用的是cnn+LSTM 在状态管理(belief state tracking)也用的LSTM，在policy的时候自定义了一套算法，将前面的几个输出向量做了个线性模型，输出。

Learning Task-Oriented Dialog with Neural Network Methods

Question Answering, Task-Oriented Dialogues and Social Chatbots

The present paper surveys neural approaches to conversational AI that have beendeveloped in the last few years. We group conversational systems into three cat-egories: (1) question answering agents, (2) task-oriented dialogue agents, and(3) chatbots. For each category, we present a review of state-of-the-art neuralapproaches, draw the connection between them and traditional approaches, anddiscuss the progress that has been made and challenges still being faced, usingspecific systems and models as case studies

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

用一个模型来解决两个不同类型的问题，intent detect是分类，填槽是序列标注。都用基于attention机制的RNN来搞定了

整体来说，无法验证，没有任何实质的创新点。

说是构建了一个第一个中文电商机器人对话系统(really?)

M = (I, C, A)

I是intent，C是product category， A是商品attribute。 M是根据用户Query得到的信息的表示。

意图分类：PhraseLDA 1000个topic

产品分类： a CNN-based approach that resembles (Huang et al. 2013)and (Shen et al. 2014

在购物场景中主要的行为有：

• Recommendation
• Comparison
• Opinion Summary
• Question Answering
• Proactive Questioning
• Chit-chat

为了解决所谓的问题：

• 1 结构化商品信息
• 2 用户的搜索日志
• 3 社区网站，挖掘购买意图和产品相关的词

The Sogou Spoken Language Understanding System for the NLPCC 2018 Evaluation

基于检索的是从候选的回复中选出一个。检索式的关键是message-response的匹配。

B. Hu, Z. Lu, H. Li, and Q. Chen. Convolutional neu-ral network architectures for matching natural lan-guage sentences. InAdvances in neural informationprocessing systems, pages 2042–2050, 2014.

单轮的匹配 match(X,Y) = X^TAy

X：message的向量表示， y：回复的向量表示。

H. Wang, Z. Lu, H. Li, and E. Chen. A dataset for re-search on short-text conversations. InProceedings ofthe 2013 Conference on Empirical Methods in NaturalLanguage Processing, pages 935–945, Seattle, Wash-ington, USA, October 2013. Association for Compu-tational Linguistics

Z. Lu and H. Li. A deep architecture for matchingshort texts. InInternational Conference on Neural In-formation Processing Systems, pages 1367–1375, 2013.

B. Hu, Z. Lu, H. Li, and Q. Chen. Convolutional neu-ral network architectures for matching natural lan-guage sentences. InAdvances in neural informationprocessing systems, pages 2042–2050, 2014

M. Wang, Z. Lu, H. Li, and Q. Liu. Syntax-based deepmatching of short texts.InIJCAI, 03 2015

Y. Wu, W. Wu, Z. Li, and M. Zhou. Topic augmentedneural network for short text conversation.CoRR,2016

多轮匹配

策略学习 基于前面state tracker的状态表示，策略学习(policy learning)是来生成下一个可用的系统行动。无论是监督学习或者强化学习都可以用来优化策略学习。 H. Cuayhuitl, S. Keizer, and O. Lemon. Strategic di-alogue management via deep reinforcement learning.arxiv.org, 2015.

通常都用一个基于规则的agent来初始化系统。 Z. Yan, N. Duan, P. Chen, M. Zhou, J. Zhou, andZ. Li. Building task-oriented dialogue systems for on-line shopping. InAAAI Conference on Artificial Intel-ligence, 2017

然后用监督学习来基于规则生成的规则来学习。Building task-oriented dialogue systems for on-line shopping. 强化学习，Strategic di-alogue management via deep reinforcement learning.结果据说比很多系统，rule based，superviesed都好

在生物医药领域很缺数据，为了解决这个问题，常识了基于模版，术语大的数据扩展技术。先在大的数据集上用ELMo来构建词向量。把任务评估分成两个子任务来进行，slot-filling和意图分类。

偏应用的一篇文章，结果也说明不了什么

Natural language understanding for task oriented dialog in the biomedical domain in a low resources context

PyDial: A Multi-domain Statistical Dialogue System Toolkit

一个开源的端到端的统计对话系统工具。

• http://pydial.org
• https://github.com/claycollier/pydial
• https://bitbucket.org/dialoguesystems/pydial.git

其总的架构包含Sematic Decode,Belief Tracker,Policy Reply System,Language generator. 整体来说整个系统都支持了基于规则的判断过程，也融合了模型的支持。源码值得一看的。

Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding

Using Recurrent Neural Networksfor Slot Filling in Spoken Language Understanding

非常简单的介绍，可以看作是一个文献阅读索引

detail :

• http://opendialogue.miulab.tw

• https://www.csie.ntu.edu.tw/~yvchen/doc/OpenDialogue_Tutorial_ISCSLP18.pdf

• https://www.csie.ntu.edu.tw/~yvchen/doc/OpenDialogue_Tutorial_IJCNLP.pdf

Open-Domain Neural Dialogue Systems

Open-Domain Neural Dialogue Systems

Open-Domain Neural Dialogue Systems

ntegrating User and Agent Models: A Deep Task-Oriented Dialogue System Weiyan Wang, Yuxiang WU, Yu Zhang, Zhongqi Lu, Kaixiang Mo, Qiang Yang (Submitted on 10 Nov 2017) Task-oriented dialogue systems can efficiently serve a large number of customers and relieve people from tedious works. However, existing task-oriented dialogue systems depend on handcrafted actions and states or extra semantic labels, which sometimes degrades user experience despite the intensive human intervention. Moreover, current user simulators have limited expressive ability so that deep reinforcement Seq2Seq models have to rely on selfplay and only work in some special cases. To address those problems, we propose a uSer and Agent Model IntegrAtion (SAMIA) framework inspired by an observation that the roles of the user and agent models are asymmetric. Firstly, this SAMIA framework model the user model as a Seq2Seq learning problem instead of ranking or designing rules. Then the built user model is used as a leverage to train the agent model by deep reinforcement learning. In the test phase, the output of the agent model is filtered by the user model to enhance the stability and robustness. Experiments on a real-world coffee ordering dataset verify the effectiveness of the proposed SAMIA framework.

Deep Reinforcement Learning for Dialogue Generation

Recent neural models of dialogue generationoffer great promise for generating responsesfor conversational agents, but tend to be short-sighted, predicting utterances one at a timewhile ignoring their influence on future out-comes. Modeling the future direction of a di-alogue is crucial to generating coherent, inter-esting dialogues, a need which led traditionalNLP models of dialogue to draw on reinforce-ment learning. In this paper, we show how tointegrate these goals, applying deep reinforce-ment learning to model future reward in chat-bot dialogue. The model simulates dialoguesbetween two virtual agents, using policy gradi-ent methods to reward sequences that displaythree useful conversational properties: infor-mativity, coherence, and ease of answering (re-lated to forward-looking function). We evalu-ate our model on diversity, length as well aswith human judges, showing that the proposedalgorithm generates more interactive responsesand manages to foster a more sustained conver-sation in dialogue simulation. This work marksa first step towards learning a neural conversa-tional model based on the long-term success ofdialogues.

Dialog System & Technology Challenge 6 Overview of Track 1 - End-to-End Goal-Oriented Dialog learning

End-to-end dialog learning is an important research subject inthe domain of conversational systems. The primary task consistsin learning a dialog policy from transactional dialogs of a givendomain. In this context, usable datasets are needed to evaluatelearning approaches, yet remain scarce. For this challenge, atransaction dialog dataset has been produced using a dialogsimulation framework developed and released by Facebook AIResearch. Overall, nine teams participated in the challenge. Inthis report, we describe the task and the dataset. Then, we specifythe evaluation metrics for the challenge. Finally, the results ofthe submitted runs of the participants are detailed.

A COMPARATIVE STUDY OF RECURRENT NEURAL NETWORK MODELS FOR LEXICAL DOMAIN CLASSIFICATION

coursera 对话系统课程

京东的机器人架构

Existing solutions to task-oriented dia-logue systems follow pipeline designswhich introduce architectural complex-ity and fragility. We propose a novel,holistic, extendable framework based ona single sequence-to-sequence (seq2seq)model which can be optimized with su-pervised or reinforcement learning. Akey contribution is that we design textspans namedbelief spansto track dia-logue believes, allowing task-oriented dia-logue systems to be modeled in a seq2seqway. Based on this, we propose a sim-plisticTwo Stage CopyNetinstantiationwhich demonstrates good scalability: sig-nificantly reducing model complexity interms of number of parameters and train-ing time by an order of magnitude. Itsignificantly outperforms state-of-the-artpipeline-based methods on two datasetsand retains a satisfactory entity match rateon out-of-vocabulary (OOV) cases wherepipeline-designed competitors totally fail

Dialogue systems have attracted more and more attention.Recent advances on dialogue systems are overwhelminglycontributed by deep learning techniques, which have beenemployed to enhance a wide range of big data applicationssuch as computer vision, natural language processing, andrecommender systems. For dialogue systems, deep learningcan leverage a massive amount of data to learn meaningfulfeature representations and response generation strategies,while requiring a minimum amount of hand-crafting. In thisarticle, we give an overview to these recent advances on di-alogue systems from various perspectives and discuss somepossible research directions. In particular, we generally di-vide existing dialogue systems into task-oriented and non-task-oriented models, then detail how deep learning tech-niques help them with representative algorithms and ?nallydiscuss some appealing research directions that can bringthe dialogue system research into a new frontier.

We introduce end-to-end neural network based models for simulat-ing users of task-oriented dialogue systems. User simulation in di-alogue systems is crucial from two different perspectives: (i) auto-matic evaluation of different dialogue models, and (ii) training task-oriented dialogue systems. We design a hierarchical sequence-to-sequence model that first encodes the initial user goal and systemturns into fixed length representations using Recurrent Neural Net-works (RNN). It then encodes the dialogue history using anotherRNN layer. At each turn, user responses are decoded from the hid-den representations of the dialogue level RNN. This hierarchical usersimulator (HUS) approach allows the model to capture undiscov-ered parts of the user goal without the need of an explicit dialoguestate tracking. We further develop several variants by utilizing a la-tent variable model to inject random variations into user responses topromote diversity in simulated user responses and a novel goal regu-larization mechanism to penalize divergence of user responses fromthe initial user goal. We evaluate the proposed models on movieticket booking domain by systematically interacting each user sim-ulator with various dialogue system policies trained with differentobjectives and users.

腾讯的数据集

A Manually Annotated Chinese Corpus forNon-task-oriented Dialogue SystemsJing Li, Yan Song, Haisong Zhang, Shuming ShiTencent AI Labfameliajli,clksong,hansonzhang,shumingshig@tencent.comAbstractThis paper presents a large-scale corpusfor non-task-oriented dialogue responseselection, which contains over27K dis-tinct prompts more than82K responsescollected from social media.1To anno-tate this corpus, we define a5-grade rat-ing scheme:bad,mediocre,acceptable,good, andexcellent, according to the rel-evance, coherence, informativeness, inter-estingness, and the potential to move aconversation forward. To test the valid-ity and usefulness of the produced cor-pus, we compare various unsupervised andsupervised models for response selection.Experimental results confirm that the pro-posed corpus is helpful in training re-sponse selection models

A Deep Reinforcement Learning Chatbot

开放领域的对话机器人 是亚马逊Alexa比赛的结果 不同于历史的大部分基于规则对话系统，该机器人采用以统计机器学习方法为主的模型来构建机器人。

整体采用的是多个领域模型混合的方式。主要介绍了2个模块ResponseModel和ModelSelection。也就是回复和选择。

对话管理器是如何工作的

• 1.各个响应模型先生成各自的回复
• 2.如果有个“优先”回复，直接返回
• 3.如果没有“优先”回复，则根据model selection 策略进行选择

响应模型主要是电影模型，wiki问答，Google知识，讲故事。用了3个现有的机器人服务，一个搜索引擎

SkipThoughtVectorModels，cosine相似度来计算相关

LSTM编码器，来将用户说话和正确应答做成pair

• template based https://gist.github.com/bebraw/273706

• knowledge base based www.evi.com

对话管理器是如何工作的

• 1.各个响应模型先生成各自的回复
• 2.如果有个“优先”回复，直接返回
• 3.如果没有“优先”回复，则根据model selection 策略进行选择

基于搜索的，基于生成的，知识问答和基于模版的混合应答模型

早期的主要是基于专家规则和状态的。现代对话系统更多的是一个混合的架构。

The Design and Implementation of XiaoIce,an Empathetic Social Chatbot

主要的设计目标是维持和人的长期情感联系。平均CPS 23.智商+情商。

empathetic computingframework共情计算框架

Yang Cai. Empathic computing. InAmbient Intelligence in Everyday Life, pages 67–85.Springer Pascale Fung, Dario Bertero, Yan Wan, Anik Dey, Ricky Ho

Yin Chan, Farhad Bin Siddique,Yang Yang, Chien-Sheng Wu, and Ruixi Lin. Towards empathetic human-robot interactions.CoRR, abs/1605.04072, 2016

If a person enjoys its companionship (via conversation), we can call the machine “empathetic”

IQ知识和记忆模型，图片和自然语言理解，推理，生成和预测 EQ情绪识别，理解情感需求

微软小冰

Wen和Bordes介绍了一种基于网络的端到端的任务型对话系统，这个系统将对话系统学习看成是从历史回话到系统回复的映射关系的学习问题，并且应用了一个编码解码器来训练整个系统。

这个思路很有意思，和我之前想构建一个电销员的语料库来做用户回复响应很像。这个很有可能。

End-to-End Task-Completion Neural Dialogue Systems

One of the major drawbacks of modu-larized task-completion dialogue systemsis that each module is trained individu-ally, which presents several challenges.For example, downstream modules are af-fected by earlier modules, and the per-formance of the entire system is not ro-bust to the accumulated errors. This pa-per presents a novel end-to-end learningframework for task-completion dialoguesystems to tackle such issues. Our neu-ral dialogue system can directly interactwith a structured database to assist usersin accessing information and accomplish-ing certain tasks. The reinforcement learn-ing based dialogue manager offers robustcapabilities to handle noises caused byother components of the dialogue system.Our experiments in a movie-ticket book-ing domain show that our end-to-end sys-tem not only outperforms modularized di-alogue system baselines for both objectiveand subjective evaluation, but also is ro-bust to noises as demonstrated by severalsystematic experiments with different er-ror granularity and rates specific to the lan-guage understanding module1

The source code is available at: https://github.com/MiuLab/TC-Bot

基于单轮微博数据的一个生成式的回复生成模型，对75%的输入都能基本正确生成一个有意义的文本。

Survey on Chatbot Design Techniques in Speech Conversation Systems 2015、 没啥意义

more precisely understand the user intent, knowledge bases such as Freebase.

用户意图识别：

https://concept.research.microsoft.com/Home/Introduction

https://developers.google.com/freebase/

we define conversation-turns per session (CPS) as the success metric for social chatbots. The larger the CPS is, the better engaged the social chatbot is.

对话系统的一个综述性paper

• 1 https://www.apple.com/ios/siri/
• 2 https://www.microsoft.com/en-us/cortana/
• 4 https://assistant.google.com/4https://developers.facebook.com/blog/post/2016/04/12/
• 5 https://developer.amazon.com/alexa/
• 6 https://www.msXiaoIce.com/
• https://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/classics/parry/0.html

reading list

DocChat: An Information Retrieval Approach for Chatbot EnginesUsing Unstructured Documents

用BM25来获取备选项。

构建了word-level,phrase-level,sentence-level,document-level,relation-level,type-levelandtopic-level的特征来训练排序模型

最有用的是sentence level的特征。

本文提出一个新的基于信息检索的chatbot引擎，可以用到非结构化的文本来回复。直接用由不同级别的特征组成的一个排序模型来评估用户回复和响应。

STC 只依赖用户上一句输入的短文本对话过程

specch act classification的难点

• 1 同一个词在不同语境中有不同的意思
• 2 说话意图的识别本身也比较难做

歧义，特异，数据源 ambiguity,specificity,source

chat bots

chatbot

decrease melt, increased enrollments

Hubert.ai

Development of a brand new bot!

Does this sound familiar?