• ai人工智能对话了How can chatbots become truly intelligent by combining five different models of conversation? 通过组合五种不同的对话模式，聊天机器人如何才能真正变得真正智能？ Conversational AI is all...

ai人工智能对话了

How can chatbots become truly intelligent by combining five different models of conversation?
通过组合五种不同的对话模式，聊天机器人如何才能真正变得真正智能？
Conversational AI is all about making machines communicate with us in natural language. They are called using various names — chatbots, voice bots, virtual assistants, etc. In reality, they may be slightly different to each other. However one key feature that ties them all together is their ability to understand natural language commands and requests from us-human users.
对话式AI就是让机器以自然语言与我们交流。 它们使用不同的名称来命名-聊天机器人，语音机器人，虚拟助手等。实际上，它们可能彼此略有不同。 但是，将他们紧密联系在一起的一个关键功能是他们能够理解自然语言的命令和人类用户的要求。
In the back-end, these agents will have to deal with carrying out the request and engage in a conversation. Based on how an agent processes the input natural language (NL) request and its mapping to a response, we can create a class of Conversational AI models.
在后端，这些代理将不得不处理执行请求并进行对话。 基于代理如何处理输入自然语言(NL)请求并将其映射到响应，我们可以创建一类会话AI模型。
Interactive FAQ 互动式常见问题 Form filling 表格填写 Question Answering 问题回答 NL interface for databases NL数据库接口 Dialogue Planning 对话策划
互动式常见问题 (Interactive FAQ)
Frequently Asked Questions (FAQ) are usually a common part of business websites where all the frequently asked questions for customers are listed and answered. Instead of having customers go through the list and find answers to their questions, Interactive FAQ model for chatbots allows users to ask questions in their own way, match customer question to the list of questions and then serve the prepared answer for the matched question. This process enables customers to find answers quickly instead of having to go through a long list of questions.
常见问题(FAQ)通常是商业网站的常见部分，其中列出并回答了所有针对客户的常见问题。 聊天机器人的交互式FAQ模型无需让客户浏览列表并找到问题的答案，而是允许用户以自己的方式提出问题，将客户问题与问题列表进行匹配，然后为匹配的问题提供准备好的答案。 此过程使客户能够快速找到答案，而不必经历一长串问题。
Single-vs-Multi turn — In this model, the customer query could be answered immediately within a single turn if it is a simple query. On the other hand, the chatbot may need to ask a few questions to get more info from the user before answering the question.
单对多回合—在此模型中，如果是简单查询，则可以在单回合内立即回答客户查询。 另一方面，聊天机器人可能需要提出一些问题，才能在回答问题之前从用户那里获取更多信息。
Intent-vs-pattern recognition — the question being asked can be identified in many ways. Intent classification is a popular approach. Here, the list of questions for which we know the answers are labelled with intent names (i.e. what is the user intending to say/ask). Each intent is then given a number of example variations of the same question. They are then fed into a machine learning algorithm that learns to classify a new unseen question from the user as one of the intents. Once intent is identified, the answer can be served.
意图与模式识别-可以多种方式识别所要提出的问题。 意图分类是一种流行的方法。 在这里，我们知道答案的问题列表用意图名称标记(即，用户打算说/问什么)。 然后，为每个意图提供相同问题的多个示例变体。 然后将它们输入到机器学习算法中，该算法学习将来自用户的新的未见问题分类为意图之一。 一旦确定了意图，就可以提供答案。

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乔恩·泰森 (
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The other approach is one that has existed since the time chatbots were born (e.g. Eliza). User utterance is pattern matched with pre-defined patterns and pre-defined answers/responses are served. Several tools are available in the market to implement pattern based conversation management (e.g. Pandorabots).
另一种方法是自聊天机器人诞生以来就存在的一种方法(例如Eliza )。 用户话语与预定义的模式匹配，并提供预定义的答案/响应。 市场上有几种工具可以实现基于模式的对话管理(例如Pandorabots )。
Recent advances in deep learning can also be used to build seq2seq models which take a sequence of words as input and output another sequence of words. This approach can be used to build interactive single-turn FAQ models. This model of conversational AI can be used for use-cases like FAQ, troubleshooting, small talk, etc.
深度学习的最新进展也可以用于构建seq2seq模型，该模型将一个单词序列作为输入并输出另一个单词序列。 此方法可用于构建交互式单匝FAQ模型。 这种对话式AI模型可用于FAQ，故障排除，闲聊等用例。
填写表格 (Form-filling)
Form-filling, as the name says, is a model of conversation that involves filling in a form. A user request is mapped to an intent or a pattern that triggers a form that needs to be filled and in order to do so, the chatbot will have to ask a number of questions. Once filled the form can then be used to either do a database search or a database update.
顾名思义，表单填写是一种对话模型，涉及到填写表单。 用户请求被映射到触发需要填写的表单的意图或模式，为此，聊天机器人将不得不提出许多问题。 填写表格后，即可用于数据库搜索或数据库更新。
Take a travel agent chatbot, for instance. It will ask a series of questions to fill in fields like source, destination, date of travel, etc to do a database search for flights. Once you choose a flight, the details of the flight will be added to a larger form to make a booking (i.e. database update). Both search and update needed information that were gathered by asking questions driven by the form. However, the downside is that intents need to be created and the conversation needs to defined meticulously every step of the way to fill in the form, submit and handle the database results.
以旅行社聊天机器人为例。 它将询问一系列问题，以填写来源，目的地，旅行日期等字段，以对航班进行数据库搜索。 选择航班后，航班的详细信息将添加到较大的表格中以进行预订(即数据库更新)。 搜索和更新所需信息都是通过询问表单驱动的问题而收集的。 但是，不利的一面是需要创建意图，并且在填写表单，提交和处理数据库结果的过程的每一步都必须仔细定义对话。
Form-filling and FAQ models are currently the most popular as these take care of the most mundane repeated conversations customers tend to engage in. Platforms like IBM Watson, DialogFlow, etc provide tools to handle these models.
当前，表单填充和FAQ模型最为流行，因为它们可以处理客户倾向于进行的最平凡的重复对话。IBMWatson，DialogFlow等平台提供了处理这些模型的工具。
Open domain question answering has been a sub-field of Natural Language Processing research with the objective of understanding user questions in natural language and extracting answers from a large corpus of text. This as you can clearly see, is a way of reducing the human effort in curating answers to questions that customers ask. It may be nearly impossible to create an exhaustive list of prepared questions and answers. To address this problem, chatbots should use QA models that can extract answers from large corpus of text on the fly.
开放域问答已经成为自然语言处理研究的一个子领域，其目的是理解自然语言中的用户问题并从大量文本中提取答案。 正如您可以清楚地看到的那样，这是减少人力来整理客户提出的问题的答案的一种方法。 创建详尽的准备好的问题和答案列表几乎是不可能的。 为了解决这个问题，聊天机器人应使用QA模型，该模型可以即时从大型文本语料库中提取答案。
QA model for conversation can be used where there is a large body of text that customers could query from and creating intent and curated answers for each question-answer pair is an expensive proposition.
对话的QA模型可以用于客户可以从中查询大量文本的情况，并且为每个问题/答案对创建意图和精选答案是一项昂贵的提议。
Recent advances in transformer based models like BERT, GPT-3 have made robust QA models for conversational AI possible. The following is an example of QA model (by DeepPavlov.ai toolkit) in action.
基于变压器的模型(例如BERT，GPT-3)的最新进展使健壮的QA模型可用于会话AI。 以下是运行中的质量检查模型(由DeepPavlov.ai工具箱提供)的示例。

DeepPavlov — TextQA demo
DeepPavlov — TextQA演示

NL数据库接口 (NL Database Interfaces)
The third type of conversational model is one where the user utterance can directly be mapped on to a database query. For instance, let us assume a relational database containing information about customer transactions data. To let customers interact with this database using natural language, form-filling model can be used. However, there are many ways to query a relational database and using form-filling model, you may have to design many conversational forms to fulfill your customer needs. Instead if you can translate your customer requests in natural language to a database query, you can run the query and respond appropriately without the need for creating forms and intents.
第三种类型的会话模型是可以将用户话语直接映射到数据库查询的模型 。 例如，让我们假设一个关系数据库包含有关客户交易数据的信息。 为了让客户使用自然语言与此数据库进行交互，可以使用表单填充模型。 但是，有很多方法可以查询关系数据库并使用表单填充模型，您可能必须设计许多对话表单才能满足客户需求。 相反，如果您可以将自然语言的客户请求转换为数据库查询，则可以运行查询并进行适当响应，而无需创建表单和意图。

Translating NL query into SQL
将NL查询转换为SQL

Query language — Depending on the type of database, the target query language will vary. For instance, for relational databases, NL queries may need to be translated into SQL. For graph databases like Neo4J and RDF triple stores, they may need to be translated into Cypher and SPARQL.
查询语言-根据数据库的类型，目标查询语言将有所不同。 例如，对于关系数据库，可能需要将NL查询转换为SQL。 对于像Neo4J和RDF三重存储这样的图形数据库，可能需要将它们转换为Cypher和SPARQL 。
How? — There are deep learning approaches — Seq2Seq models — that can translate from NL queries into a query language. Recently, GPT-3, the largest pre-trained language models so far, has been used to translate NL to SQL query using few-shot learning.
怎么样？ —有一些深度学习方法— Seq2Seq模型—可以将NL查询转换为查询语言。 最近，到目前为止，最大的预训练语言模型GPT-3已用于通过几次学习将NL转换为SQL查询。
This model allows the customer to create a number of queries about the data in natural language without constraining them to pre-defined forms.
该模型允许客户以自然语言创建有关数据的许多查询，而不必将其约束为预定义的形式。
对话策划 (Dialogue Planning)
The final model in my list is Dialogue Planning. This model uses AI Planning approach to drive conversation. AI Planning is an Artificial Intelligence approach to intelligent problem solving. In a dialogue planning model, we will treat conversation as a planning problem with an initial state and a final goal state. The AI planner’s task is then to find an optimal sequence of steps from the initial to the goal state. In a conversation, these steps will include — asking the customer for answers to specific questions, fetching or updating info from/to a back-end system, etc.
我列表中的最终模型是“对话计划”。 该模型使用AI规划方法来推动对话。 AI Planning是一种用于解决问题的人工智能方法。 在对话计划模型中，我们将对话视为具有初始状态和最终目标状态的计划问题。 AI计划者的任务是找到从初始状态到目标状态的最佳步骤顺序。 在对话中，这些步骤将包括-向客户询问特定问题的答案，从后端系统获取信息或更新信息，等等。
For instance, to book a flight ticket, the agent will come up with a plan to ask a series of questions — destination, date, etc, search for flights, summarise them, help user to choose one, ask further questions — passenger name, age, meals, etc, make a booking and send a confirmation email. While in a form-filling model, the above sequence will have to be authored by hand, in a planning model, only a set of actions will need to be provided. The agent could use the same set of actions to create another sequence to achieve a different goal. To come up with an analogy, it is like the agent is a given a number of LEGO bricks that it can put together in various ways to build different things.
例如，要预订机票，代理商将提出一个计划，询问一系列问题(目的地，日期等)，搜索航班，进行汇总，帮助用户选择一个问题，提出其他问题(乘客姓名，年龄，用餐等，请进行预订并发送确认电子邮件。 在填表模型中，必须手动编写以上序列，而在计划模型中，仅需要提供一组操作。 代理可以使用同一组动作来创建另一个序列以实现不同的目标。 举个比喻，就像代理是给定的许多乐高积木一样，它可以通过各种方式组合在一起来构建不同的事物。

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Xavi Cabrera在
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Like NL Database Interfaces and QA models, it allows for users to define initial and final states using natural language without being constrained by pre-defined conversational pathways. Instead, using AI planning, new pathways are created using a library of planning operators (or dialogue actions). Dialogue planning is still largely an area of research and non-availability of toolkits makes it hard to implement this model in a production environment.
像NL数据库接口和QA模型一样，它允许用户使用自然语言定义初始和最终状态，而不受预定义的对话路径的约束。 取而代之的是，使用AI规划，使用规划操作员(或对话操作)库创建新途径。 对话计划仍然是一个主要的研究领域，并且由于无法使用工具包，因此很难在生产环境中实施此模型。
Furthermore, planning approaches can be combined with deep reinforcement learning to optimize generated plans based on experience and reward from the environment. This will turn them into learning agents as well.
此外， 可以将计划方法与深度强化学习相结合 ，以基于经验和环境奖励来优化生成的计划。 这也将使他们成为学习代理。
混合助手 (Hybrid Assistants)
Truly intelligent conversational agents will need to combine above models in a meaningful way. Such an assistant will be a hybrid with skills to combine various conversational models based on needs of the customer, relative success and cost of each model competing to solve the same problem. Combining these approaches will come with its own set of problems — need for unified knowledge representation mechanisms, explainability and control, etc. But with problems, solutions will come too.
真正智能的对话代理将需要以有意义的方式组合上述模型。 这样的助手将具有技巧，可以根据客户的需求，相对成功和竞争解决同一问题的每个模型的成本来组合各种对话模型的技能。 将这些方法结合起来会带来自己的一系列问题-需要统一的知识表示机制，可解释性和控制性等。但是遇到问题时，解决方案也将随之而来。
While FAQ and form-filling models are particularly popular now, the need for models like Open QA, NL database interfaces and Dialogue planning are becoming more prominent as not every conversational pathway can be pre-determined, planned and scripted by human content developers. Developments in NLP and machine/deep learning over recent years — transformers like BERT, GPT-3, T5, reinforcement learning like AlphaGo, etc — show promising traits and I believe, will help us achieve our goal to build truly intelligent conversational AI.
尽管FAQ和表单填充模型现在特别流行，但由于并非每种对话路径都可以由人类内容开发人员预先确定，计划和编写脚本，因此对诸如Open QA，NL数据库界面和对话计划之类的模型的需求日益突出。 近年来，NLP和机器/深度学习的发展(如BERT ， GPT-3 ， T5 ，诸如AlphaGo等的强化学习等)显示出令人鼓舞的特质，我相信这将帮助我们实现构建真正智能的对话式AI的目标。
希望您喜欢这篇文章。 请分享您的评论。

翻译自: https://medium.com/analytics-vidhya/models-for-conversational-ai-34312fe1f6d9

ai人工智能对话了

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• ai人工智能对话了The voice interface and conventional system are the practical implementations of AI technology in the industry. This article will explore the basic knowledge and techniques then extend...

ai人工智能对话了

The voice interface and conventional system are the practical implementations of AI technology in the industry. This article will explore the basic knowledge and techniques then extend to the challenges faced in different business use cases.
语音接口和常规系统是业界AI技术的实际实现。 本文将探索基本知识和技术，然后扩展到不同业务用例中面临的挑战。

1.会话系统 (1. Conversational System)
What is the conversational system or a virtual agent? One of the best-known fictional agents is Jarvis from Iron Man. It can think independently and help Tony do almost anything, including running chores, processing massive data sets, making intelligent suggestions, and providing emotional support. The most impressive feature of Jarvis is the chat capability, you can talk to him like an old friend, and he can understand you without ambiguity. The technology behind the scene is conversational AI.
什么是会话系统或虚拟代理？ 最著名的虚构人物之一是钢铁侠的贾维斯(Jarvis)。 它可以独立思考并帮助Tony做几乎所有事情，包括运行杂务，处理海量数据集，提出明智的建议以及提供情感支持。 Jarvis最令人印象深刻的功能是聊天功能，您可以像老朋友一样与他交谈，并且他可以毫无歧义地了解您。 幕后的技术是对话式AI。
The core of Conversational AI is a smartly designed voice user interface(VUI). Compared with the traditional GUI (Graphic User Interface), VUI free user’s hands by allowing them to perform nested queries via simple voice control (not ten clicks on the screen).
对话式AI的核心是设计精巧的语音用户界面(VUI)。 与传统的GUI(图形用户界面)相比，VUI允许用户通过简单的语音控制(而不是在屏幕上单击十次)来执行嵌套查询，从而解放了用户的双手。
However, I have to admit that there’s still a big gap between the perfect virtual agent Jarvis and the existing conversational AI platforms’ capabilities.
但是，我必须承认，理想的虚拟代理Jarvis与现有的对话式AI平台的功能之间仍然存在很大差距。
Human and machine conversations have received tons of tractions from academia and industry over the past decade. In the research lab, we saw the following movement:
在过去的十年中，人机对话在学术界和业界引起了无数的关注。 在研究实验室中，我们看到了以下动作：
Natural language understanding has moved from manual annotation and linguistic analysis to deep learning and sequenced language modeling. 自然语言理解已经从人工注释 和 语言分析转变为深度学习 和 顺序语言建模。 The dialog management system has moved from rule-based policies to supervised learning and reinforcement learning. 对话管理系统已经从基于规则的   政策 监督学习和强化学习 。 The language generation engine has moved from the pre-defined template and syntax parsing to end-to-end language transformer and attention mechanisms. 语言生成引擎已经从预定义的模板 和 语法解析转移到了端到端的语言转换器 和 注意力机制。
In addition, we also saw conversational products spring up in the cross-market domain. All the big players have their signature virtual agent, for instance, Siri for Apple, Alexa for Amazon, Cortana for Microsoft and Dialogflow for Google. (Diagram below are out of date, please use it a reference only)

Recast.AI
Recast.AI的报告

2.会话系统的关键组件 (2. Key Components of a Conversational System)
There are few main components in the conversational platform, 1) ASR: Automatic Speech Recognition, 2) NLU: Natural Language Understanding, 3) Dialog Management, 4)NLG: Natural Language Generation, 5) TTS: Text to Speech. (Additional components could include public API, integration gateway, action fulfillment logic, Language model training stack, versioning, and chat simulation, etc.)
对话平台中的主要组件很少，1)ASR：自动语音识别，2)NLU：自然语言理解，3)对话管理，4)NLG：自然语言生成，5)TTS：文本到语音。 (其他组件可能包括公共API，集成网关，动作执行逻辑，语言模型训练堆栈，版本控制和聊天模拟等)
For simplicity, let’s explore the basics now.
为简单起见，让我们现在探讨基础知识。

Simple Dialog System by Catherine Wang

凯瑟琳·王(Catherine Wang)的简单对话系统

2.1. ASR: Automatic speech recognition is a model trained on speaker voice record and transcript, then fine-tuned to recognize the unseen voice queries. Most of the conversational platforms offer this feature as an embedded element. Thus developers can leverage the state of the art ASR on their product(e.g., voice input, voice search, real-time translation, and smart home devices).
2.1。 ASR：自动语音识别是一种针对演讲者语音记录和笔录进行训练的模型，然后进行微调以识别看不见的语音查询。 大多数对话平台都将此功能作为嵌入式元素提供。 因此，开发人员可以在其产品(例如，语音输入，语音搜索，实时翻译和智能家居设备)上利用最新的ASR。
2.2. NLU: Indisputably, the most important part of a conversational system. ASR will only transcribe what you have said, but NLU will understand exactly what do you mean? Natural Language Understanding can be seen as a subset of Natural Language Processing. The relationship can be loosely described as below.
2.2。 NLU：毫无疑问，对话系统中最重要的部分。 ASR只会记录您所说的内容，但是NLU会确切地理解您的意思？ 自然语言理解可以看作自然语言处理的一个子集。 可以如下大致描述该关系。

By
由
SciforceSciforce

Both NLP and NLU are board topics, so instead of going too deep into the topic, I will explain the high-level concept by using practical examples from the virtual agent use case.
NLP和NLU都是董事会的主题，因此，我不会使用这个主题，而是通过使用虚拟代理用例中的实际示例来解释高级概念。
Generally speaking, NLU and NLP were structured around the following problems:
一般而言，NLU和NLP围绕以下问题构建：
Tokenisation and Tagging. They are text preprocessing techniques. Tokenisation is the first step that needs to apply to both the traditional linguistic analysis and deep learning models. It split a sentence into words (or n_grams), and those words will be later used to build the vocabulary or train the word embedding algorithm. Tagging is sometimes optional, and it will label each token (words) into lexical categories. (e.g., ADJ, ADV, NOUN, NN) 令牌化和标记。 它们是文本预处理技术。 令牌化是需要应用于传统语言分析和深度学习模型的第一步。 它将一个句子分解成单词(或n_grams)，这些单词将在以后用于构建词汇表或训练单词嵌入算法。 标记有时是可选的，它会将每个标记(单词)标记为词汇类别。 (例如，ADJ，ADV，NOUN，NN)

Dependency and Syntactic Parsing. A popular technique in linguistic analysis, it parses a sentence into its grammatical structure. Before the age of deep learning, those syntax trees are used to constitute a new sentence or a sequence of words. 依赖性和语法解析。 它是语言分析中的一种流行技术，它将一个句子解析成其语法结构。 在深度学习时代之前，这些语法树用于构成新的句子或单词序列。

From Stanford NLP

从斯坦福大学自然语言处理

Name Entity Recognition. It was used to extract or identify a set of predefined word entities. The output of NER can sometimes look quite similar to POS tagging. The results are also stored in a Python tuple,e.g. (US, ‘GPE’). The main differences are 1) the NER model can be trained by new annotation to pick up domain-specific word entities. 2) NER focuses more on semantic meaning, whereas POC tagging is more on grammar structure. 名称实体识别。 它用于提取或识别一组预定义的单词实体。 NER的输出有时看起来与POS标记非常相似。 结果也存储在Python元组中，例如(US，“ GPE”)。 主要区别在于1)可以通过新注释来训练NER模型，以选择特定领域的单词实体。 2)NER更侧重于语义，而POC标记更侧重于语法结构。 Phrase and Pattern matching. The simplest implementation of phrase matching is using a rules-based regular expression. Don’t get me wrong, the regular expression is still beneficial in the unlabeled dataset. An adequately defined fuzzy pattern can match up to hundreds of similar sentences. However, this rule-based method is hard to maintain and scale-up. A more advanced approach involves using POC tags or dependency labels as the sequence for matching, or using vector distances. 短语和模式匹配。 短语匹配的最简单实现是使用基于规则的正则表达式。 别误会，正则表达式在未标记的数据集中仍然很有用。 适当定义的模糊模式可以匹配多达数百个相似的句子。 但是，这种基于规则的方法很难维护和扩展。 一种更高级的方法涉及使用POC标签或相关性标签作为匹配序列，或使用矢量距离。

Word Vectorization and Embedding. Word embedding marks the dawn of NLP, and it introduces the concept of distributed representation of a word. Before deep learning, linguistics uses the dense representation to capture the structure of the text and use the statistical model to understand the relationship. The drawback of this method is the lack of the capability of representing the contextual meaning and word inference. Word embedding offers a solution to learn the parameters that best represent a word in a particular context from a higher-dimensional space. For practical use, you can find pre-trained word embedding models like Word2Vec, GloVe, or if you need, you can always fine-tune those models on your new set of vocab and training corpus. 词向量化和嵌入。 词嵌入标志着NLP的兴起，它引入了词的分布式表示的概念。 在深度学习之前，语言学使用密集表示法来捕获文本的结构，并使用统计模型来理解这种关系。 这种方法的缺点是缺乏表达上下文含义和单词推断的能力。 单词嵌入提供了一种解决方案，可以从更高维度的空间中学习在特定上下文中最能代表单词的参数。 在实际使用中，你可以找到预先训练字嵌入模型，如Word2Vec ， 手套 ，或者如果你需要，你可以随时调整你的新词汇集和训练语料的那些模型。

Word Embedding By Catherine Wang

凯瑟琳·王的文字嵌入

Sequence Vectorization and Embedding. Similar concept, but instead of vectorizing every single word, sequence embedding focuses on finding the best representation for longer text as a whole. This technique improves specific NLP tasks that need to understand a longer chunk of texts, for instance, text translation, text generation, reading comprehension, natural questions & longer answers, etc. 序列向量化和嵌入。 类似的概念，但是序列向量化不是将每个单词向量化，而是着眼于为整个较长的文本找到最佳的表示形式。 该技术改进了需要理解较长文本的特定NLP任务，例如，文本翻译，文本生成，阅读理解，自然问题和较长答案等。

Sequence Modeling by Catherine Wang

凯瑟琳·王(Catherine Wang)的序列建模

Sentiment Analysis. The task of analyzing if an expression is positive or negative (can be understood as binary classification, 1-positive, 0-negative)? One of the most common tasks in NLP, in the use of conversational AI, sentiment analysis could provide a benchmark for the virtual customer agent to identify customers’ emotions and intention then provides a different emotional response suggestion. 情绪分析。 分析表达式是正还是负(可以理解为二进制分类，1阳性，0阴性)的任务？ NLP中最常见的任务之一是，使用对话式AI，情感分析可以为虚拟客户代理提供基准，以识别客户的情感和意图，然后提供不同的情感响应建议。 Topic Modeling. It leverages unsupervised ML techniques to find the groups of the topics in a broad set of unlabeled documents. It helps us to understand the theme of a collection of unseen corpus quickly. In the use case of conversational AI, topic modeling acts as the first filter that triage the user queries into higher-level topics then mapped to more granular intents and actions. 主题建模。 它利用无监督的ML技术在大量未标记的文档中找到主题组。 它有助于我们快速理解未见语料库的主题。 在对话式AI的用例中，主题建模充当第一个过滤器，将用户查询分类为更高级别的主题，然后映射到更精细的意图和操作。 Text Classification and Intent Matching. Both of those tasks use supervised learning, and the quality of the model would largely depend on how you prep the training data. Compared with Topic Modeling, text classification and intent matching are more granular and deterministic. You can understand the relationship with the image shown below. When facing unseen customer queries, your conversational AI system will use topic modeling to filter your query to a broad topic and then use text classification and intent matching to map it to a specific action. 文本分类和意图匹配。 这些任务都使用监督学习，并且模型的质量在很大程度上取决于您准备训练数据的方式。 与主题建模相比，文本分类和意图匹配更加精确和确定。 您可以通过下面显示的图像了解这种关系。 当遇到看不见的客户查询时，您的会话式AI系统将使用主题建模将查询过滤到较宽的主题，然后使用文本分类和意图匹配将其映射到特定操作。

Intent Matching by Catherine Wang

凯瑟琳·王的意图匹配

Language Modeling. A trendy topic in deep learning and NLP. All the state-of-the-art models you have heard of are based on this concept (the BERT family: ALBERT, RoBERTa; the multitask learners and few-shot learning: GPT-2). To let the machine understand the human language better, scientists trained it to build vocabulary and statistical models to predict the likelihood of each word in the context. 语言建模。 深度学习和NLP中的一个热门话题。 您听说过的所有最新模型都基于此概念(BERT系列：ALBERT，RoBERTa；多任务学习器和一次性学习器：GPT-2)。 为了让机器更好地理解人类语言，科学家对其进行了培训，以建立词汇和统计模型以预测上下文中每个单词的可能性。

Language Model by Catherine Wang

王凯瑟琳的语言模型

Multi-Turn Dialog System. This is an advanced topic in NLU and conversational AI. It refers to the techniques that track and identity change of a topic/intent in a conversational system. How we can better pick up the information in each dialog and draw a comprehensive logic behind the user’s compound intent. 多转对话系统。 这是NLU和对话式AI中的高级主题。 它指的是在对话系统中跟踪和标识主题/意图变化的技术。 我们如何更好地获取每个对话框中的信息，并在用户的复合意图背后绘制全面的逻辑。

Modeling Multi-turn Conversation with Deep Utterance Aggregation
使用深度话语聚合为多回合会话建模

In the use case of conversational AI, NLU is aiming to resolute the language confusion, ambiguity, generalize verbal understanding, identify domains and intentions from humans to machine dialog, then extract critical semantic information.
在对话式AI的使用案例中，NLU旨在消除语言的混乱，歧义，概括语言理解，识别人与机器对话的领域和意图，然后提取关键的语义信息。
Apart from using the key technologies I mentioned above, the AI system needs to find a useful semantic representation of user queries. The most successful one is “Frame Semantics,” which uses Domain, Intent, Entity, and Slot to formulate semantic results.
除了使用我上面提到的关键技术外，AI系统还需要找到用户查询的有用语义表示。 最成功的一种是“框架语义”，它使用域，意图，实体和插槽来表达语义结果。
Domain: Can be linked to topic modeling, it groups the queries and knowledge resources into different business categories, goals, and corresponding services. For example, “Pre-sale”, “Post-sale”, or “Order and Transaction”. 域 ：可以链接到主题建模，将查询和知识资源分为不同的业务类别，目标和相应的服务。 例如，“预售”，“售后”或“订单和交易”。 Intent: Can be linked to intent matching and classification. It refers to particular tasks or business processes within a domain. It usually is written in a verb-object phrase. e.g. “search for songs”, “play the music”, or “favorite the playlist” in the music player domain. 意图 ：可以链接到意图匹配和分类。 它指的是域中的特定任务或业务流程。 它通常用动词-宾语短语写成。 例如音乐播放器域中的“搜索歌曲”，“播放音乐”或“收藏播放列表”。 Entity and Slot: Can be used as parameters to extract critical information from domain and intent. e.g. “song name”, “ singer”. 实体和广告位 ：可以用作从域和意图中提取关键信息的参数。 例如“歌曲名称”，“歌手”。

A sentence “ What is the weather for Melbourne tomorrow? ” can be transposed into the blow structure,
一句话“明天墨尔本天气如何？ 可以转换为打击结构
- Domain: “ Weather”
-域 ：“天气”
- Intent: “ Check the Weather”
-目的 ：“检查天气”
- Entity and Slot: (“City”: “Melbourne”, “Date”: “Tomorrow”)
-实体和位置 ：(“城市”：“墨尔本”，“日期”：“明天”)

Then the follow-up actions will be fulfilled by parsing the above-structured data.
然后，将通过解析上述结构的数据来执行后续操作。
2.3. Dialog Management: another critical part of the Conversational AI system. It controls the flow of the dialog between user and agent. In the simplest version, a DM engine will remember the history dialog context, tracks the state of the current dialog, then applies dialog policy.
2.3。 对话管理：对话式AI系统的另一个关键部分。 它控制用户和代理之间的对话流程。 在最简单的版本中，DM引擎将记住历史对话框上下文，跟踪当前对话框的状态，然后应用对话框策略。
Dialog Context: During the session of a user-agent conversation, all the back and forth dialogs will be remembered in the context. Critical information like domain, intent, entity, and slot will be saved in a message queue for in-memory search and retrieve. After the conversation, the dialog context can be preserved in the database for further analysis. 对话框上下文：在用户代理会话期间，所有来回对话框都将在上下文中记住。 诸如域，意图，实体和插槽之类的关键信息将保存在消息队列中，以便在内存中进行搜索和检索。 对话后，对话框上下文可以保留在数据库中以供进一步分析。 Dialog State Tracking: Dialog state tracker will remember the logic flow in the conversation. It will make the agent more intelligent and flexible by tacking the logic tuning point in different dialogs, then suggesting a response based on the long term memory. 对话状态跟踪 ：对话状态跟踪器将记住对话中的逻辑流程。 通过在不同对话框中添加逻辑调整点，然后根据长期记忆提出响应，这将使代理变得更加智能和灵活。 Dialog Policy: Based on the context and logic flow of the conversation, the agent needs to prioritize services, trigger certain events, and request fulfillment. The fulfillment actions could include retrieving user information from the database, searching for content in the knowledge base system, or triggering third-party API. 对话策略：基于对话的上下文和逻辑流，代理需要确定服务的优先级，触发某些事件并执行请求。 履行动作可以包括从数据库检索用户信息，在知识库系统中搜索内容或触发第三方API。
For example:
例如：

Q: I want to order pizza delivery (intent=order_pizza, entity_time=null, entity_address = null, entity_type=null). A: what type of pizza do you want to order? (slot=type, slot=date, slot=address)
问 ：我要订购比萨饼递送 (意向= order_pizza，entity_time = null，entity_address = null，entity_type = null)。 A： 您要订购哪种比萨饼？ (广告位=类型，广告位=日期，广告位=地址)
Q: Margherita.(intent=order_pizza, entity_time=null, entity_address = null, entity_type=Margherita) A: What time you want your pizza to be diliver? (slot=date, slot=address)
问 ：玛格丽特。 (intent = order_pizza，entity_time = null，entity_address = null，entity_type = Margherita) 答 ：您希望什么时候分送比萨饼？ (广告位=日期，广告位=地址)
Q: ASAP.(intent=order_pizza, entity_time=ASAP , entity_address = null, entity_type=Margherita) A: Is there anything else you would want to order with your {Margherita} Pizza? (follow_up_intent: additional_prodcut)
问 ：尽快。 (intent = order_pizza，entity_time = ASAP，entity_address = null，entity_type = Margherita) 答 ：您还想与{Margherita}披萨一起点餐吗？ (follow_up_intent：Additional_prodcut)
Q: A bottole of Coke . (intent=order_pizza, entity_time=ASAP , entity_address = null, type=Margherita, additional =coke) A: What is the address for us to delier your pizza? ( slot=address)
问： 可乐的底部。 (intent = order_pizza，entity_time = ASAP，entity_address = null，type = Margherita，其他= coke) A：我们为您的披萨送餐的地址是什么？ (插槽=地址)
Q: xx.xxx . (intent=order_pizza, entity_time=ASAP , entity_address = xx.xxx, type=Margherita, additional =coke) A: Thanks, so you ordered {type} Pizza with {*additional} and deliver to {entity_address} {ASAP}. (fulfillment: update_order, call_delivery_services)
问： xx.xxx 。 (意图= order_pizza，entity_time = ASAP，entity_address = xx.xxx ，type = Margherita，其他= coke) A： 谢谢，所以您订购了{ type }比萨饼和{* Additional }， 并交付给{ entity_address } { ASAP }。 (实现：update_order，call_delivery_services)

As you can see, slot and entity need to be filled during the conversation, and parent intent can trigger follwo_up intent, then action fulfillment will be activated base on the state of the conversation.
如您所见，在对话期间需要填充插槽和实体，并且父意图可以触发follwo_up意图，然后将根据对话的状态激活操作执行。
2.4. NLG: The natural language generation engine has different implementation and technology stack based on the type of chat system. For a task-oriented close domain conversation system, NLG is implemented via the response template with inter-replaceable parameters from “slot” and “entities” extracted from the conversation session. For an open domain chat system, text generation would be based on information retrieval, machine comprehension, knowledge graph, etc.
2.4。 NLG ：自然语言生成引擎根据聊天系统的类型具有不同的实现和技术堆栈。 对于面向任务的近域对话系统，NLG是通过响应模板实现的，该模板具有从对话会话中提取的“ 时隙 ”和“ 实体 ”中可互换的参数。 对于开放域聊天系统，文本生成将基于信息检索，机器理解，知识图等。
2.5. TTS: Text to speech engine is performing the task exactly opposite to ASR. It transforms the plain text to voice record and plays it back with the synthetic voice to the end-user.
2.5。 TTS ：文本到语音引擎正在执行与ASR完全相反的任务。 它将纯文本转换为语音记录，并将其与合成语音一起回放给最终用户。
Based on the above discussion, the below image offers a more comprehensive and realistic view of the Conversational AI system.
基于以上讨论，下图提供了会话式AI系统的更全面，更真实的视图。

Conversational AI System by Catherine Wang

对话式人工智能系统Catherine Wang

3.语音用户界面和用户体验设计 (3. Voice User Interface and User Experience Design)
GUI (Graphic User Interface) is dominating the human-machine interaction. It’s the game-changer in the PC world, and catalyst the massive adoption of digital devices in everyday life. Now we are facing the screen and interact with them all the time.
GUI(图形用户界面)主导着人机交互。 它是PC世界中的游戏规则改变者，并催化了日常生活中数字设备的大量采用。 现在我们面对屏幕并一直与他们互动。
But in the next decade, with the advances of AI, human and machine interaction will be shifting to voice. Voice User Interface will be the new entry point of smart and IoT devices. For example, when you say “Hey Google,” Googe home will be awake and start a conversation with you. In this case, the voice will become the new mouse and figure.
但是在接下来的十年中，随着AI的发展，人机交互将转向语音。 语音用户界面将成为智能和物联网设备的新切入点。 例如，当您说“ Hey Google”时，Googe home将会醒来并与您开始对话。 在这种情况下，声音将成为新的鼠标和图形。

unsplash
脱颖而出

In the GUI design, all the user interactions are pre-defined and guided by a series of clicks or swaps on the screen. But the VUI system, firstly, user’s behaviors are unpredictable and can diverge from the main storyline. Secondly, in the open conversation, a user might change the topic anytime and the user’s request might have compound intents that need to be fulfilled. Lastly, the voice interaction requires constant attention from users and agents because both parties need to remember what they said in the previous turns.
在GUI设计中，所有用户交互都是预先定义的，并在屏幕上进行一系列单击或交换来引导。 但是，VUI系统首先是用户的行为无法预测，并且可能与主要故事情节有所不同。 其次，在公开对话中，用户可以随时更改主题，并且用户的请求可能具有需要实现的复合意图。 最后，语音交互需要用户和代理不断关注，因为双方都需要记住他们在前几轮中所说的话。
The most successful Conversational AI system would consider voice and graphics complementary in their UI and UX design. A mature system should combine both traits to offer end-user a richer and immersive experience.
最成功的会话式AI系统在其UI和UX设计中将语音和图形视为互补。 一个成熟的系统应结合这两个特征，为最终用户提供更丰富和身临其境的体验。
Modeling Multi-turn Conversation with Deep Utterance Aggregation. arXiv:1806.09102 [cs.CL] 使用深度话语聚合为多回合会话建模。 arXiv：1806.09102 [cs.CL]

About me, I am a 👧🏻 who is living in Melbourne, Australia. I studied computer science and applied statistics. I am passionate about general-purpose technology. Working in a Global Consulting firm as an AI Engineer lead👩🏻‍🔬, helping the organization to integrate AI solutions and harness its innovation power. See more about me on LinkedIn.
关于我，我是a ，住在澳大利亚墨尔本。 我学习了计算机科学和应用统计学。 我对通用技术充满热情。 在全球咨询公司担任AI工程师领导 👩🏻‍🔬，帮助组织集成AI解决方案并利用其创新能力。 在LinkedIn上查看有关我的更多信息。

翻译自: https://towardsdatascience.com/conversational-ai-key-technologies-and-challenges-part-1-a08345fc2160

ai人工智能对话了

展开全文
• ai人工智能对话了In a fast-moving world, customers require efficiency and promptness when talking to any company. Here is where chatbots and Intelligent Virtual Assistants (IVAs) come into play. 在...

ai人工智能对话了

In a fast-moving world, customers require efficiency and promptness when talking to any company. Here is where chatbots and Intelligent Virtual Assistants (IVAs) come into play.
在瞬息万变的世界中，与任何公司交谈时，客户都需要效率和及时性。 这是聊天机器人和智能虚拟助手 (IVA)发挥作用的地方。
Thanks to their ability to engage into more advanced conversations, unlike rule-based chatbots, AI-powered systems are equipped with a multitude of features to assist and even entertain the users in their day-to-day activities. In addition to their customizable features, their self-learning ability and scalability have lead virtual assistants to gain popularity across various global enterprises.
与基于规则的聊天机器人不同，由于其能够参与更高级的对话，因此， 基于 AI的系统配备了多种功能，可以帮助甚至娱乐用户的日常活动。 除了可自定义的功能外，它们的自学习能力和可扩展性还使虚拟助手在各种全球企业中获得了普及。
According to Grand View Research, the global intelligent virtual assistant market size was valued at USD 3.7 billion in 2019, growing at a Compound Annual Growth Rate (CAGR) of 34.0% over the forecast period. The need for effectiveness across service-based companies and the integration of AI digital assistants among various devices, such as computers, tablets and smartphones, is anticipated to boost the market.
根据Grand View Research的数据，2019年全球智能虚拟助手市场规模为37亿美元， 在预测期内以34.0％的复合年增长率(CAGR)增长 。 预计跨服务型公司对效率的需求以及在各种设备(例如计算机，平板电脑和智能手机)之间集成AI数字助理的需求将推动市场的发展。
机器人在2020年能做什么？ (What can bots do in 2020?)
There is certainly no doubt that recent advancements in technology have significantly improved the performance of chatbots and IVAs. But, however flawless they may seem at first sight, we could all agree on the fact that bots are still terrible conversationalists.
毫无疑问，最新的技术进步已大大改善了聊天机器人和IVA的性能。 但是，尽管它们乍看之下似乎无懈可击，但我们都可以同意机器人仍然是可怕的会话主义者这一事实。
基于规则的聊天机器人。 人工智能驱动的聊天机器人。 (Rule-based chatbots. AI-driven chatbots.)
The basic rule-based chatbots are only accessible within chats and work on a single-turn exchange. In a nutshell, they react to questions asked by the user, detect the main intent, and return a single pre-defined answer accordingly. They are able to handle basic routine queries, for instance: FAQs, reservations, online orders or appointment scheduling (survey bots, meeting planners, foreign language tutors, travel & hospitality bots). Nevertheless, as soon as the user asks a question out of the bot’s learned set of knowledge, it will automatically lead to failure.
基于规则的基本聊天机器人只能在聊天中访问，并且只能在单回合上进行工作。 简而言之，他们会回答用户提出的问题，检测主要意图，并相应地返回一个预定义的答案。 他们能够处理基本的常规查询，例如： 常见问题解答，预订，在线订单或约会安排 ( 调查机器人 ， 会议计划者 ， 外语辅导员 ， 旅行和接待机器人 )。 但是，只要用户从机器人学到的知识集中提出问题，就会自动导致失败。
On the other hand, we distinguish the AI-powered chatbots, that rely on core Machine Learning technologies like Natural Language Processing (NLP) and Information Retrieval (IR) techniques. By applying such methods, tech giants like Facebook and Google have released open-domain multi-turn chatbots (see Meena and Blender), that are able to reproduce more human-like conversations. However, the implementation of open-domain bots remains incredibly challenging due to many direct limitations of deep-learning.
搅拌器 (BlenderBot)
In April 2020, Facebook AI developed and open-sourced BlenderBot, the first chatbot to blend a diverse set of conversational skills — including empathy, knowledge, and personality — together in one system.
2020年4月， Facebook AI 开发并开源了BlenderBot ， 这是第一个将机器人的对话技能(包括同情心，知识和个性)融合到一个系统中的聊天机器人 。
For all the great progress it represents for conversational AI, Blender is still far from reaching the level of humans. One of the challenges lies in its tendency to make up facts — because sentences are being generated from statistical correlations, and not from a knowledge database. As a consequence, it can string together an in-depth and coherent description of a well-known superstar, for example, but with entirely false information. The team intends to experiment further with integrating a knowledge database into the chatbot’s response generation system.
尽管它为对话式AI带来了巨大的进步，但Blender仍远没有达到人类的水平。 挑战之一在于其趋于事实化的趋势-因为句子是从统计关联而不是从知识数据库生成的。 结果，它可以将对知名超级巨星的深入而连贯的描述串在一起，例如带有完全错误的信息。 该团队打算进一步尝试将知识数据库集成到聊天机器人的响应生成系统中。

聊天机器人会被IVA取代吗？ (Will chatbots be replaced by IVAs?)
Most probably, yes. But what about our “state-of-the-artists” Meena and BlenderBot? They seem to be pretty smart chatbots, don’t they?
很有可能，是的。 但是，我们的“最先进的” Meena和BlenderBot呢？ 他们似乎是非常聪明的聊天机器人，不是吗？
As enterprises across industries seek for ways to boost their customer experience, IVAs are highly likely to gather momentum over chatbots. You must now be wondering why, and the answer is relatively straightforward. Besides having the power of leveraging AI to drive transformations to the core of the business, IVAs are able to adapt and engage in more human-like conversations and enhance the user experience.
随着各行各业的企业寻求增加客户体验的方法， IVA很有可能在聊天机器人上积聚动力 。 您现在必须想知道为什么 ，答案是相对简单的。 除了具有利用AI推动业务核心转型的力量外，IVA还能够适应和参与更多类似于人类的对话，并改善用户体验。
While the so-called Voice Revolution is taking place, some organizations believe that avatars simulating real persons would lead to even more successful assistants. How successful? Remains to be seen, literally.
在所谓的“ 语音革命”发生时，一些组织认为，模拟真实人物的化身会带来更多成功的助手。 有多成功？ 从字面上看 ，还有待观察 。
糟糕！ Meena和BlenderBot只能... CHAT。 (Oops! Meena and BlenderBot can only… CHAT.)
If you’re reading this, you have most probably talked at least once to either Alexa, Google Assistant, Siri, Cortana, or Bixby. And if you haven’t yet, you must be curious why voice-enabled AIs have become so popular in the past years. Let’s take a closer look!
Conversational interactions facilitated by digital assistants and high-quality Voice User Interfaces (VUIs) are set to be the real game-changer in the coming years. As Automatic Speech Recognition advances, a great demand of voice search will lead smart speakers and in-car systems to go hand in hand with IVAs.
在未来几年中，由数字助理和高质量语音用户界面(VUI)促进的对话交互将成为真正的游戏规则改变者。 随着自动语音识别的发展，语音搜索的巨大需求将导致智能扬声器和车载系统与IVA紧密结合 。
Furthermore, voice technology is becoming increasingly important in the field of education. Supported by IBM Watson Machine Learning Accelerator solutions, DeepZen has developed deep learning and neural networks to recognize emotion in text and produce human-like speech. The organization believes that voice technology can help students with spelling and the practice of times tables, as well as teaching them about AI and the world of the future.
此外，语音技术在教育领域变得越来越重要 。 在IBM Watson Machine Learning Accelerator解决方案的支持下， DeepZen开发了深度学习和神经网络，以识别文本中的情感并产生类似于人的语音。 该组织认为， 语音技术可以帮助学生进行拼写和时间表练习，并向他们传授有关AI和未来世界的知识。

“Voice assistants are gaining popularity in education as more and more teaching apps are being developed.” — DeepZen
随着越来越多的教学应用程序的开发，语音助手在教育中越来越受欢迎。 ” — DeepZen

我从来没有……看过虚拟助手。 (Never have I ever… SEEN virtual assistants.)
A different approach is coming from Samsung’s subsidiary STAR Labs, which has officially unveiled its “artificial human” project, Neon, at CES 2020. Neon is basically about creating digital avatars — computer-animated human likenesses — still unknown to the public up until today. The company explains that “Scenarios shown at our CES Booth and in our promotional content are fictionalized and simulated for illustrative purposes only.”
三星的子公司STAR Labs采取了另一种方法，该公司已在2020年国际消费电子展上正式推出了其“人造人”项目Neon。Neon基本上是要创建数字化身-计算机动画的人像-直到今天仍然是公众所不知道的。 该公司解释说：“我们在CES展台和促销内容中显示的场景都是虚构和模拟的，仅供参考 。”

NEON “artificial humans”

NEON“人造人”

Taking things further, Replika has already integrated a beta version of 3D avatars, leading to many controversial reactions from the users’ part. While many are excited about visually interacting with their replika and the technology behind it, others have decided to go back to the older version. Ever since the update, Replika’s Twitter page has been hosting comments of users feeling “uncomfortable” and “scared” regarding the new “terribly creepy” avatars.
SO, whether these avatars are part of successful digital assistants’ road ahead still remains an open question.
因此，这些化身是否是成功的数字助理前进道路的一部分，仍然是一个悬而未决的问题。
带回家的消息。 (Take-home message.)
The number of organizations using virtual assistants is expected to skyrocket in the coming years, given the fast-paced evolution of NLP technologies, the rise of voice search, and, respectively, the development of e-commerce and e-learning. In other words, the previously mentioned emerging trends lead us to believe that old traditional rule-based chatbots are very likely to be substituted by IVAs.
鉴于NLP技术的快速发展 ，语音搜索的兴起以及电子商务和电子学习的发展，使用虚拟助手的组织的数量预计将在未来几年激增。 换句话说，前面提到的新兴趋势使我们相信， 传统的基于规则的传统聊天机器人很可能会被IVA取代。
Mind that in spite of it all, challenges and concerns of conversational AI development are numerous and bots remain presumably flawed.
请注意，尽管如此， 对话式AI开发仍面临许多挑战和担忧，并且机器人可能仍然存在缺陷。

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