深度学习驱动的对话系统：进展与前景

下载需积分: 50 | PDF格式 | 282KB | 更新于2024-09-08 | 35 浏览量 | 举报

"这篇资源是京东与密歇根州立大学的数据科学实验室合作完成的一份关于对话系统的综述，引用了124篇相关论文，旨在为初学者提供全面的基础知识，并探讨该领域的最新进展和未来研究方向。" 在对话系统领域，近年来的发展受到了深度学习技术的极大推动。深度学习在计算机视觉、自然语言处理和推荐系统等多个大数据应用中展现出强大的能力。对于对话系统而言，深度学习能够利用大量数据学习到有意义的特征表示和响应生成策略，减少了对人工规则的依赖。文章首先将现有的对话系统大致分为两类：任务导向型（Task-Oriented Dialogue Systems）和非任务导向型（Non-Task-Oriented Dialogue Systems）。任务导向型对话系统主要关注于帮助用户完成特定任务，如预订餐厅或查询天气，它们通常需要理解用户的意图并提供准确的操作指导。而非任务导向型对话系统则更侧重于开放式的聊天，旨在模仿人类间的自然交流，提供娱乐或情感支持。在深度学习应用于对话系统方面，文章可能会详细讨论以下内容： 1. **语义理解**：深度学习模型，如循环神经网络（RNN）、长短期记忆网络（LSTM）和Transformer，被用于理解和解析用户的输入，提取关键信息。 2. **对话状态追踪**：深度学习用于跟踪对话过程中的状态，理解用户的需求和上下文，确保对话的连贯性。 3. **响应生成**：生成式对话模型，如seq2seq模型，通过学习大量对话数据，可以自动生成与用户输入相匹配的回应。 4. **对话管理**：深度强化学习（DQN, A3C等）在对话策略学习中的应用，优化系统在多轮交互中的决策过程。 5. **多模态对话**：随着语音识别和图像理解的进步，深度学习也被用于处理包含语音和图像信息的多模态对话。 6. **情感理解与个性建模**：深度学习可以帮助系统理解和响应用户的情绪，甚至适应用户的个性，提供更加人性化的交互体验。此外，文章还可能探讨当前对话系统面临的挑战，如对话系统的泛化能力、对话理解的复杂性、以及如何避免生成重复或无意义的响应。最后，作者会提出未来的研究方向，如持续学习、对话系统中的常识推理、以及对话系统的评估方法等。这篇综述将为读者提供对话系统领域的深度洞察，帮助他们了解最新的技术趋势，并为相关研究提供有价值的参考。

a rule-based agent is emp loyed to warm-start the system

[111]. Then, superv ised learning is conduct ed on t he ac-

tions generated by the rules. In online shopping scenario,

if the d ialogue state is “Recommendation”, then the “Rec-

ommendation” action is triggered, and the sy stem will re-

trieve product s from the product database. If the state is

“Comparison”, then the system will compare target prod-

ucts/brands[111]. The dialogue policy can b e further trained

end-to-end with reinforcement learning to lead the system

making policies toward the ﬁnal performance. [14] applied

deep reinforcement learning on strategic conversation that

simultaneously learned the feature representation and dia-

logue policy, the system outperformed several baselines in-

cluding random, rule-based, and supervised-based methods.

2.1.4 Natural Language Generation

The natural language generation component converts an ab-

stract dialogue action into natural language surface utter-

ances. As noticed in [78], a good generator usually relies on

several factors: adequacy, ﬂuency, readability, an d variation.

Conventional approaches to NLG typically perform sentence

planning. It maps input semantic symbols into the inter-

mediary form representing the utterance such as tree- like

or template structures, and then converts the intermediate

structure into the ﬁnal response through the surface realiza-

tion [90 ; 79].

[94] and [95] introduced neural network-based (NN) approaches

to NLG with a LSTM-based structure similar with RNNLM

[52]. The dialogue act type and its slot-value pairs are trans-

formed into a 1-hot control vector and is given as t he addi-

tional input, which ensures that the generated utterance rep-

resents the intended meaning. [94] used a forward RNN gen-

erator together with a CNN reranker, and backward RNN

reranker. All th e sub-modules are jointly optimized to gen-

erate utterances conditioned by the required dialogue act.

To address the slot in formation omitting and duplicating

problems in surface realization, [95] used an additional con-

trol cell to gate the dialogue act. [83] extended this ap-

proach by gating the input token vector of LSTM with the

dialogue act. It was then extended to the multi-domain

setting by multiple adaptation steps [96]. [123] adopted

an encoder-decoder LSTM-based structure to incorporate

the question information, semantic slot values, and dialogue

act type to generate correct answers. It used the attention

mechanism to attend to the key information cond itioned on

the current decoding state of the decoder. Encoding the di-

alogue act type embedding, the n eural network-based model

is able to generate variant answers in response to diﬀerent

act types. [20] also presented a natu ral language genera-

tor based on the sequence-to-sequ ence approach that can

be trained to produce natural language strings as well as

deep syntax dependency trees from input dialogue acts. It

was then extended with the preceding user utterance and

responses [19]. It enabled the model entraining (adapting)

to users ways of speaking, which provides contextually ap-

propriate responses.

2.2 End-to-End Methods

Despite a lot of domain-speciﬁc handcrafting in traditional

task oriented dialogue systems, which are diﬀcult to adapt

to new domains [7], [120] further noted that, the conven-

tional pipeline of task-oriented dialogue systems has two

main limitations. One is the credit assignment problem,

where the end user’s feedback is hard to be propagated to

each upstream module. The second issue is process interde-

pendence. The input of a component is dependent on the

output of another component. When ad ap ting one compo-

nent to new environment or retrained with new data, all the

other components need to be adapted accordingly to ensure

a global optimization. Slots and features might change ac-

cordingly. This p rocess requ ires signiﬁcant human eﬀorts.

With the advance of end-to-end neural generative models in

recent years, many attempts have been made to construct an

end-to-end trainable framework for task-oriented dialogue

systems. Note that more details about neural generative

mod els will be discussed when we introduce the non-task-

oriented systems. Instead of the traditional pipeline, the

end-to-end model uses a single module and interacts with

structured external databases. [97] and [7] introduced a

network-based end- to-end trainable task-oriented d ialogue

system, which treated dialogue system learning as the prob-

lem of learning a m ap ping from dialogue histories to system

responses, and applied an encoder-decoder model to train

the whole system. However, the system is t rained in a su-

pervised fashion – not only does it require a lot of training

data, but it may also fail to ﬁnd a good policy robustly due

to t he lack of exploration of dialogue control in the train-

ing data. [120] ﬁrst presented an end-to-end reinforcement

learning app roach to joint ly train dialogue state tracking

and policy learning in the dialogue management in order to

optimize the system actions more robustly. In the conver-

sation, the agent asks the user a series of Yes/No questions

to ﬁnd the correct answer. This approach was shown to be

promising when applied to the task-oriented dialogue prob-

lem of guessing the famous people users think of. [45] trained

the end-to-end system as a task completion neural dialogue

system, where its ﬁnal goal is to complete a task, such as

movie-ticket booking.

Task-oriented systems usually need to query outside kn owl-

edge base. Previous systems achieved this by issuing a sym-

bolic query to the knowledge b ase to retrieve entries based

on their attributes, where semantic parsing on the input is

performed to constru ct a symbolic qu ery representing the

beliefs of the agent about th e user goal[97; 103; 45]. This

approach has two drawbacks: (1) the retrieved results do not

carry any information about u ncertainty in semantic pars-

ing, and (2) the retrieval operation is non diﬀerentiable, and

hence the parser and dialog policy are trained separately.

This makes online end-to-end learning from user feedback

diﬃcult once the system is deployed. [21] augmented ex-

isting recurrent network architectures with a diﬀerentiable

attention-based key-value retrieval mechanism over the en-

tries of a knowledge base, which is inspired by key-value

memory networks[54]. [18] replaced symbolic queries with

an indu ced “soft” posterior distribution over the knowledge

base that indicates which entities the user is interested in.

Integrating the soft retrieval process with a reinforcement

learner. [102] combined an RNN with domain-speciﬁc knowl-

edge en coded as software and system action templates.

3. NON-TASK-ORIENTED DIALOGUE SYS-

TEM

Unlike task-oriented dialogue systems, which aim to com-

plete speciﬁc tasks for user, non-task-oriented dialogue sys-

tems (also known as chatbots) focus on conversing with hu-

剩余12页未读，继续阅读

baidu_33499751

粉丝: 0

深度学习驱动的对话系统：进展与前景

深度对话系统（发表于2021年05月10日）

基于深度学习的开放领域对话系统研究综述.pdf

一篇多轮对话方面的论文

对话系统综述 Neural Approaches to Conversational AI

基于多轮交互的人机对话系统综述.docx

智能对话系统研究综述.pdf

任务型对话系统研究综述.pdf

人机对话前沿综述

口语对话系统对话管理深度综述：进展与挑战

口语对话系统中的对话管理关键技术综述

最新资源