[2017 - 2018 ACL] 对话系统论文研究点整理

（论文编号及摘要见 [2017 ACL] 对话系统. [2018 ACL Long] 对话系统. 论文标题[]中最后的数字表示截止2019.1.21 google被引次数）

1. Domain Adaptation:

challenges:

　　(a) data shifts (syn -> live user data; stale -> current) cause distribution mismatch bet train and eval. -> 2017.1

　　(b) reestimate a global model from scratch each time a new domain with potentially new intents and slots is added. -> 2017.4

　papers:

　2017.1 adversarial training

　　　　 [Adversarial Adaptation of Synthetic or Stale Data. Young-Bum Kim. 14]

2017.4 model(k + 1) = weighted_combination[model(1),...,model(k)]

　　　　[Domain Attention with an Ensemble of Experts. Young-Bum Kim. 17]

2. NLG:　

　challenges:

　　(a) integrate LM + Affect. -> 2017.2

　　(b) refering expression misunderstand -> 2017.5

　　(d) multi-turn: lose relationships among utterances or important contextual information. -> 2017.11

　　(e) automatically evaluating the quality of dialogue responses for unstructured domains: biased and correlate very poorly with human judgements of response quality. -> 2017.12

　　(f) deep latent variable models used in open-domain: highly randomized, leading to uncontrollable generated responses. -> 2017.14

　　(g) does not employ knowledge to guide the generation -> tends to generate short, general, and meaningless responses. -> 2018.L1

　　(h) encoder-decoder dialog model is limited because it cannot output interpretable actions as in traditional systems, which hinders(阻碍) humans from understanding its generation process. -> 2018.L6

　　(i) translate natural language questions ->structured queries: further improvement hard. -> 2018.L8

　papers:

　2017.2 language model + affect info

　　　　[Affect-LM: A Neural Language Model for Customizable Affective Text Generation. 27]

　2017.5 refering expression misunderstand correction - alg: contrastive focus

　　　　[Generating Contrastive Referring Expressions. 0]

　2017.8 open-domain - Framework - conditional variaional autoencoders

　　　　pre: word-level decoder

　　　　cur: discourse-level encoder

　　　　[Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders. CMU. 69]　　　　

　2017.11 muli-turn response selection - sequential matching network (SMN)

　　　　pre: concatenates utterances in context

　　　　　　matches a response with a highly abstract context vector

　　　　　　=> lose relationships among utterances or important contextual information　

　　　　current: matches a response with each utterance on multiple levels of granularity

　　　　　　　　distills important matching information -> vector -> conv + pooling

　　　　　　　　accumulate vector -> RNN (models relationships among utterances)

　　　　　　　　final matching score (calcu with hid of rnn)

　　　　[Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots. 北航. 南开.微软. 48]

　2017.12 auto eval Metric - ADEM

　　　　[Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses. 47]

　2017.14 Framework - generation based on specific attributes(manually + auto detected) - both speakers diag states modeled -> personal features

　　　　[A Conditional Variational Framework for Dialog Generation. 20]　　

　2017.16 Open-domain - Engine - generation (info retrieval + Seq2Seq) - AliMe chat

　　　　[AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine. 27]

　2018.L1 knowledge guide generation - neural knowledge diffusion (NKD) model - both fact + chi-chats

　　　　　match the relevant facts for the input utterance + diffuse them to similar entities

　　　　　[Knowledge Diffusion for Neural Dialogue Generation. 3]

　2018.L6 encoder-decoder model - interprete- unsup discrete sent representation learning

　　　　　DI-VAE + DI-VST - discover interpretable semantics via either auto encoding or context predicting

　　　　　[Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation. 8]

　2018.L8 Framework - DialSQL + human intelligence

　　　　　 identify potential error of SQL -> ask for validation -> feedback to revise query

　　　　　[ DialSQL: Dialogue Based Structured Query Generation. 4]

3. Task + Non-task hybrid

　2017.3 whether to have a chat - dataset

　　　　[Chat Detection in Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systems. McGill University. Montreal. 7]　

4. E2E

　challenges:

　　　(a) data-intensive -> 2017.6

　　　(b) task - interact with KB -> pre: issuing a symbolic query to the KB to retrieve entries based on their attributes. -> 2017.13

　　　　disadvantages:

　　　　　　(1) such symbolic operations break the differentiability(可辨性) of the system

　　　　　　(2) prevent end-to-end training of neural dialogue agents

　　　 (b) incorporating knowledge bases. -> 2018.L7

　papers:

　2017.6 Framework - HCNs : RNN + knowledge(software/sys action templates) - reduce train data - opt (sup + RL) - bAbI dialog dataset - 2 commercial diag sys

　　　　[Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning. Microsoft Research. 87]

　2017.13 KB-InfoBot - E2E - task -multi-turn - interact with KB - present a agent

　　　　replacing symbolic queries -> induced "soft" posterior distribution over the KB

　　　　integrate soft retrival process + RL

　　　　[Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access. CMU. MS. 国立台北. 82]

　2018.L4 multimodel info (sup + RL) - user adaptive - reduce diag length + improve success rate

　　　　　[Sentiment Adaptive End-to-End Dialog Systems. 2]

　2018.L7 Mem2Seq - first neural generative model: combines [ multi-hop attention over memories + idea of pointer network]

　　　　　[Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems. UST.8]

5. NLU

　challenges:

　　(a) no systematic comparison to analyze how to use context effectively. -> 2017.15

　papers:

　2017.7 identity discussion points + discourse relations

　　　　[Joint Modeling of Content and Discourse Relations in Dialogues. 7]　

　2017.15 context utiliztion eval -empirical study and compare models - variant: weights context vectors by context-query relevance

　　　　[How to Make Contexts More Useful? An Empirical Study to Context-Aware Neural Conversation Models. 18]

6. Dialogue state tracking

　 challenges:

　　(a) have difficulty scaling to larger, more complex dialogue domains. -> 2017.10

　　　　(1) Spoken Language Understanding models that require large amounts of annotated training data

　　　　(2) hand-crafted lexicons for capturing some of the linguistic variation in users' language.

　　(b) handling unknown slot values -> Pre: assume predefined candidate lists and thus are not designed to output unknown values. especially in E2E, SLU is absent. -> 2018.L10

　papers:

　2017.10 Framework - Neural Belief Tracking (NBT) - representation learning (compose pre-trained word vector -> utterances and context)

　　　　　[Neural Belief Tracker: Data-Driven Dialogue State Tracking. 63]

　2018.L9 Global-Locally Self-Attentive Dialogue State Tracker (GLAD)

　　　　　global modules: shares parameters between estimators for different types (called slots) of dialogue states

　　　　　local modules: learn slot-specific features

　　　　　[Global-Locally Self-Attentive Encoder for Dialogue State Tracking. 0]

　2018.L10 E2E + pointer nerwork (PtrNet)

　　　　　[An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking. 2]

7. Framework

　challenges:

　　　　(a) pipeline: introduces architectural complexity and fragility. -> 2018.L2

　papers:

　2018.L2 Seq2Seq + opt (sup / RL) - Task

　　　　design text spans named belief spans -> track dialogue believes -> allow task-oriented sys be modeled in Seq2Seq

　　　　Two Stage CopyNet instantiation -> reduce para, train time + better than pipeline on large dataset + OOV

　　　　[Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures. 新加坡国立. 复旦. 京东. 9]　　　　

8. RL

　challenges:

　　　　(a) Training a task-completion dialogue agent via reinforcement learning (RL) is costly: requires many interactions with real users.

　　　　(b) use a user simulator: lacks the language complexity + biases

　papers:

　2018.L3 RL - policy learning - Deep Dyna-Q

　　　　　first deep RL framework that integrates planning for task-completion dialogue policy learning

　　　　　world model update with real user experience + agent opt using real and simulated experience

　　　　　[Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning. 3]　

8. Chi-chat

　challenges:

　　(a) lack specificity

　　(b) do not display a consistent personality. -> 2018.L5

　papers:

　　2018.L5 add profile info[i. given + ii.partner] train to engage ii with personal topics -> used to predict profile

　　　　　　[Personalizing Dialogue Agents: I have a dog, do you have pets too? 31.]　　　　　　　　

9. Others:

　challenges:

　　(a) open-ended dialogue state. -> 2017.9

　papers:

　2017.9 Symmetric Collaborative Dialogue - two agents to achieve a common goal

　　　　[Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings. 21]　