End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions
Authors: Libo Qin
Wenbo Pan
Qiguang Chen
Lizi Liao
Zhou Yu
Yue Zhang
Wanxiang Che
Min Li
Venue: EMNLP 2023 Main (2023)
Keywords: End-to-End Task-Oriented Dialogue (EToD)
Task-oriented Dialogue Systems (ToD)
Pre-trained Models in Dialogue Systems

Abstract: End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, we present a thorough review and provide a unified perspective to summarize existing approaches as well as recent trends to advance the development of EToD research. The contributions of this paper can be summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this research field; (2) New taxonomy: we first introduce a unified perspective for EToD, including (i) Modularly EToD and (ii) Fully EToD; (3) New Frontiers: we discuss some potential frontier areas as well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) Abundant resources: we build a public website, where EToD researchers could directly access the recent progress. We hope this work can serve as a thorough reference for the EToD research community.

End-to-end Task-oriented Dialogue:
A Survey of Tasks, Methods, and Future Directions
Libo Qin', Wenbo Pan?, Qiguang Chen Lizi Liao', Zhou Yu②, Yue Zhang Wanxiang Che Min Li'
School of Computer Science and Engineering, Central South University
Harrbi Institute of Technology, China
RResearch Center for Social Computing and Information Retrieval
"Singapore Management University
DDpparmment of Computer Science, Columbia University
School of Engineering, Westlake University
lbbii,, min. 1i] wesssuueeeee cn, pixe._wenbo@mmal. com, (ggchen, car) hit edu. cn, 1zliao@smu. edu sg, zy2 Sy24618 @col edu, yue woeezzhnn@wwwas.ooo cn
Prepgrrion Path Gradicnt Propagation
Abstract
Pipeline
NLU
+-X
DST
DPL
NLG
End-to-end task-oriented dialogue (EToD) can di- rectly generate responses an end-to-end fashion without modular training, which attracts escalat- ing popularity. The advancement of deep neural networks, especially the successful use of large pre-trained models, has further led to significant progress in EToD research in recent years. In this paper, present thorough review and provide a unified perspective to summarize existing ap- proaches well as recent trends to advance the development of EToD research. The contributions this paper can summarized: (1) First survey: to our knowledge, we take the first step to present a thorough survey of this research field; (2) New taxonomy: we first introduce a unified perspective for EToD, including (i) Modularly EToD and (ii) Fully EToD; (3) New Frontiers: we discuss some potential frontier areas well as the corresponding challenges, hoping to spur breakthrough research in EToD field; (4) Abundant resources: we build a public website where EToD researchers could directly access the recent progress. We hope this work can serve as thorough reference for the EToD research community.
(a) Traditional pipeline Lask-oriented dialogue framework.
Modularly End-to-end
NLU
DST
DPL
NLG
(b) Modularly end-to-end task-oriented dialogue framework.
Fully End-to-end
Unified Sequence-to-sen to-sequnnce Model
Fully end-to-end task-oriented dialogue framework.
Figure 1: Pipeline Task-oriented Dialogue System (a). Modularly End-to-end Task-oriented Dialogue System (b) and Fully End-to-end Task- frientore Dialogue Sys- tem. The dashed box denotes separately trained while the solid line box represents end-to-end training.
try deployment. As shown in Figure (()), con- ventional ToD systems utilize pipeline approach that includes four connected modular components: (1) natural language understanding (NLU) for ex- tracting the intent and key slots of users (Qin et al., 2020a, 2021b): (2) dialogue state tracking (DST) for tracing users' belief state given dialogue history (Balaraman et al., 2021a; Jacqmin et al.. 2022a); (3) dialogue policy learning (DPL) to de- termine the next step to take (Kwan et al., 2022); (4) natural language generation (NLG) for generat- ing dialogue system response (Wen et al., 2015: Li et al., 2020).
1 Introduction
Task-oriented dialogue systems (ToD) can assist users in achieving particular goals with natural language interaction such as booking a restaurant navigation inquiry. This area is seeing grow- ing interest in both academic research and indus-
'W collect the related papers, baseline projects, and leaderboards for the community at https://ttos..et/.
While impressive results have been achieved

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Sequicity (Lei et al., 2018), SEN (Mchri al., 2019), DAMD (Zhang al., 2019).
Modularly EToD w/ PLM (83.1.1)
UniConv (Le et al., 2020), LABES (Zhang et al., 2019), LAVA (Lubis et al., 2020).
NDM (Wen et al., 2017), FSDM (Shu et al., 2019), MOSS (Liang et al., 2019)
HCNs (Williams
2017)
Modularty EToD (53.1)
ARDM (Wn et al., 20216), Hello-GPT2 (Budzianawski and Vulić, 2019).
SoD (Hosseini-All
2020), NeuralPipelline (Ham al., 2020).
MISSA (Li et al., 2019), MinTL (Lin et al., 2020). SOLOIST (Peng et al., 2021),
Modularty EToD of PI.M ($3.1.2)
UBAR (Yang et al., 2020b), AUGPT (Kulhánck et al., 2021).
GALAXY (He et al., 20226), PPTOD (Su et al., 2021). GPT-ACN (Wang et al.. 2022).
BORT al., 2022). MITOD (Lee, 2021), QTOD and SPACE (He et al., 2022a)
al., 2022)
McmN2N (Bordes al., 2017), Mem2Seq (Mudotto
2018). DDMN (Wang et al., 2020), DENet (Qin et al., 2020b), GLMP (Wu al., 2019).
Entity Triplet
BossNet (Raghu et al., 2019), KB-Ttansfommer (E. et al., 2019).
Representation. 133.2.1)
IR-Net (Ma et al., 2021), WMM2Seg (Chen et al., 2019b)
and MCL (Qin et al., 2021a)
DSR (Wen et al., 2018), KB-InfoBet (Dhingra et al., 2017), MLM (Reddy et at., 2018). 2019b), CDNett (Raghu al., 2021) and HM2Seq (Zeng al., 2022)
Fully EToD ($3.2)
Row-level Representation, ($1.2.2)
KB-retricver (Qin
Fg2Seq (He al., 2020b), GraphDialog (Yang al., 2020a), GraphMemDiilog (Wa et al., 202 GPT2KE (Madotto et al., 2021), COMET (Gou et al., 2021) and DialoKG (Rony et al., 2022)
Graph Representation. ($3.2.3)
Figure 2: Taxonomy for End-to-end Task-orient Dialogue (EToD).
previous pipeline ToD approaches. they still suffer from two major drawbacks. (1) Since each mod- ule (i.e., NLU, DST, DPL, and NLG) is trained separately, pipeline ToD approaches cannot lever- age shared knowledge across all modules; (2) As the pipeline ToD solves all sub-tasks in sequential order, the errors accumulated from the previous module are propagated to the latter module, result- ing in an error propagation problem. To solve these issues, dominant models in the literature shift to end-to-end lask-triented dialogue (EToD). criti- cal difference between traditional pipeline ToD and EToD methods is that the latter can train a neural model for all the four components simultaneously (see Fig. 1(b)) or directly generate the system re- sponse via a unified sequence-to-sequence frame-
all types of EToD, which help researchers track the progress of EToD comprehensively. Further- more, we present some potential future directions and summarize the challenges, hoping to provide new insights and facilitate follow-up research in the EToD field.
Our contributions can be summarized as follows:
(1) First survey: To our knowledge, we are the first to present a comprehensive survey for end-to-end task-oriented dialogue system;
(2) New laxonomy: We introduce new taxon- omy for EToD including (1) modularly EToD and (2) fully EToD (as shown in Fig. 2);
(3)
New frontier:::: We discuss some new frontiers and summarize their challenges, which shed light on further research;
work (see Fig. ((c)..
Thanks to the advances of deep learning ap- proaches and the evolution of pre-trained models.
(4) Abundant resources: we make the first at- tempt to organize EToD resources including open-source implementations, corpora, and paper lists at https://etods.nett
recent years have witnessed remarkable success in EToD research. However, despite its success, there remains a lack of a comprehensive review of recent approaches and trends. To bridge this gap, we make the first attempt to present a survey of this research field. According to whether the inter- mediate supervision is required and KB retrieval is differentiable or not, we provide unified tax- onomy of recent works including (1) modularly EToD (Mehri et al., 2019; Le et al., 2020) and (2) fully EToD (Eric and Manning, 2017; Wu et al., 2019; Qin et al., 2020b). Such taxonomy can cover
We hope that this work can serve as quick access to existing works and motivate future research?.
Background
This section describes the definition of modu- larly end-to-end task- dialogue (Modularly
'uue to the page limitation, the detailed related work sec- tion can be found in the Appendix B.

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EToD ) and fully end-to-end task-triented dia- logue (Fully EToD (2.2), respectively.
Formally, fully EToD can be denoted as:
8 = Fully ETI,,
(4)
2.1 Modularly EToD
Modularly EToD typically generates system re- sponse through sub-components (e.g., dialog state tracking (DST), dialogue policy learning (DPL) and natural language generation NLG)). Unlike tra- ditional ToD which trains each component (e.g., DST, DPL, NLG) separately, modularly EToD trains all components in end-to-end manner where the parameters of all components are op- timized simultaneously.
3 Taxonomy of EToD Research
This section describes the progress of EToD ac- cording to the new taxonomy including modularly EToD (63.1) and Fully EToD ($3.2).
3.1 Modularly EToD
Formally, each dialogue turn consists of a user utterance and system utterance s. For the n-th dialog turn, the agent observes the dialogue his- tory H = (u1, s1), (u2, (2), s2),... (Mn-1, 8n-1), and the corresponding knowledge base (KB) as KBB while it aims to predict system response 8n, denoted as S.
We further divide the modularly EToD into two sub-categories (1) modularly EToD without pre- trained model ($3.1.1) and (2) modularly EToD with a pre-trained model ($3.1.2) according to whether or not pre-trained model is used, which are shown in Fig. 3 (a) and (b).
3.1.1 Modularly EToD without PLM
Modularly EToD first reads the dialogue history to generate a belief state B:
One line of work mainly focuses on optimizing the whole dialogue with supervised learning (SL) while another line considers incorporating rein- forcement learning (RI.) approach for optimizing.
B =
(1)
where B consists of various slot value pairs (e.g.. price: cheap) for each domain.
Supervised Learning. Liu and Lane (2017) first presented an LSTM-based (Hochreiter and Schmid- huber, 1997) model which jointly learns belief tracking and KB retrieval. Wen et al. (2017) also proposed EToD model with modularized de- sign, in which each module transmits its latent rep- resentation instead of predicted labels to the next module. Lei al. (2018) introduced Sequicity, a two-stage CopyNet (Gu et al., 2016), merg- ing belief tracking and response generation in a sequence-to-sequence model. MOSS (Liang et al., 2019) expanded Sequicity with NLU and DPL modules for comprehensive dialogue supervision. Shu et al. (2019) modeled language understand- ing and state tracking tasks jointly using a unified seq2seq approach and separate GRUs for different slot types. Mehri et al. (2019) explicitly incorpo- rated the dialogue structure information into EToD, enhancing the domain generalizability. Zhang et al. (2019) considered multiple appropriate responses under the same context in ToD and improved dia- logue policy diversity by balancing the valid output action distribution. LABES (Zhang al., 2020b) leveraged unlabeled dialogue data (i.e., without be- lief state labels) to achieve semi cemi-supervised learn ing of ToD.
The generated belief state B is used query the corresponding KB obtain the database query results D:
D -
(2)
Then, H. B, and D is used to decide dialogue action A. Finally, modularly EToD generates the final dialogue system response S conditioning on H. B, D and A::
S =
(3)
2.2 Fully End-to-end Task-oriented Dialogue comparison to modularly EToD, Fully EToD (Eric and Manning, 2017) has two crucial differences: (1) modularly EToD leverages the generated beliefs as API to query KB, which is non-differemtiable. In contrast, fully EToD directly encodes KB and uses neural network to query the KB in differentiable manner. (2) Unlike modularly EToD which requires modular annotation (e.g., DST, DPL annotation) for intermediate supervision, fully EToD can directly generate system response given only dialogue history and the corresponding KB;

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Now-diffaenniibll KR Retrivall
Difierentable KB Retrieval
Response
Response
Response
Ditlogue Ercoder
State Tracking
Response Generster
Pre-troined Longuage Model
Dialogue Enceder
Responde Genanator
Diologue History
Belief state
Query
Diologue History
Gelief stote
Query
Diologu History xt
Distribution
Spnbolic Eneeterige deor ara
Synboli Kieweddee Boce
Enbedied fnowlsgge
(a) Modularly EToD w/o PLM.
(b) Modularly EToD with PLM.
(c) Fully EToD.
Figure 3: Three categories for EToD, including (a) Modularly EToD without PLM; (b) Modularly EToD with PLM and (c) Fully E.oD. Modularly EToD generates the system response with modularized components and train all components in an end-to sod-to-eut end fashion (see (a) and (b)). Meanwhile, the KB retrieval of modularly EToD is by API call that is -on-differentiable. In contrast, fully EToD can directly generate system response given the dialogue history and KBB which does not require the modularized components (see (c)). Besides, the KB retrieval process in fully EToD is differentiable and can be optimized together with other parameters in EToD.
Reinforcement Learn- ing (RL) has been explored supplement su- pervised learning in dialogue policies optimization. et al. (2018) demonstrated less error propaga- tion using RL-optimized networks than SL settings. SL signals have also been incorporated into RL frameworks, either by modifying rewards (Zhao and Eskenazi, 2016) adding SL cycles (Liu et al.. 2017). Approaches like LAVA (Lubis al., 2020), LaRL (Zhao al., 2019), CoGen (Ye et al., 2022)
Reinforcement Learning.
(2021b) introduced two separate GPT-2 models to learn the user and system utterance distribu- tion effectively. Hosseini-Asl posed S SimpleToD, recasting all ToD subtasks as a single sequence prediction paradigm by optimiz- ing for all tasks in an end-to-end manner. Wang et al. (2022) re-formulated the task-oriented dia- logue system as natural language generation task. UBAR (Yang al., 2020b) followed the similar paradigm with Simp 1 TOD. The core difference that UBAR incorporated all belief states in all dia- logue turns while s SimpleTTe only utilized belief states of the last turn.
(2020) pro-
and HDNO (Wang et al., 2021) have explored the modeling of latent representations. Work on RL- optimized EToD training with human intervention includes HCNs (Williams et al., 2017), human- corrected model predictions (Liu et al., 2018; Liu and Lane, 2018), 8), and determining optimal time for human intervention (Rujendran al., 2019; Wang al., 2019).
Another series of works tried to modify the pre-training objective of autoregressive transform- ers. To this end, Li et al. (2019) replaced sys- tem response ground truth with random distrac- tor at possibility during training and leveraged next utterance classifier to distinguish them. Soloist (Peng al., 2021) proposed aux- iliary task where the target belief state replaced with the belief state from unrelated samples for con- sistency prediction. Kulhánck et al. (2021) further augmented GPT-2 by presenting a new dialogue consistency classification task. The experimental results show that these more challenging training objectives bring significant improvements.
3.1.2 Modularly End-to-end Task-oriented Dialogue with Pre-trained Model
There are two main streams of PLM for modularly EToD including (1) Decoder-only PLM (Radford et al.) and (2) Encoder-Decoder PLM (Lewis et al., 2019; Raffel al., 2020).
Decoder-only PLM. Some works adopted GPT- 2 (Radford et al.) as the backbone of EToD mod- els. Budzianowski and Vulié (2019) first attempted employ pretrained GPT model for EToD, which considers dialogue context, belief state, and database state as raw text input for the GPT model to generate the final system response. Wu et al.
Encoder-decoder PLM. PLMs with encoder- decoder architecture such as BART (Lewis et al., 2019), T5 (Raffel et al., 2020) and UniLM (Dong et al., 2019) are also explored

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MuliWOZ20
Mul1
Model
Inform (%)
Success
BLBU
Combined
Inform (%6
Success (%)
BLBU
Combined
Modalarly End-to-end Task-oriened Dialogue without Pre-trained Model
MD 10-Secuicity (Lei 2018)
66.4
45.3
15.3
71.4
SFN+RT. (Mehri et al,, 2019)
73.8
58.6
16.9
83.0
73.8
58.6
16.9
83.0
DAMD (Zhang et al., 2019)
76.3
60.4
16.6
85.0
76.4
60.4
85.0
Unicony al., 2020)
62.9
LABES (Zhang al., 2020b)
67.1
90.7
LA3A (Labis al., 2020)
81.8
12.0
98.8
Modularly End-to-end Task-oriented Dialogue with Pre-trained Model
mpmpp eTob (Hosseini-Ak Asi 2020)
84.4
70.1
15.0
92.3
85.0
70.5
15.2
93.0
UBAR (Yang al., 2020b)
95.4
80.7
17.0
105.1
95.7
81.8
16.5
105.3
BART (Lin et al., 2020)
84.9
74.9
17.9
97.8
AuGPT (Kulhinek et al., 2021)
81.1
70.1
17.2
93.8
83.5
67.3
17.2
92.6
SCLOISI (Peng et al., 2021)
85.5
72.9
16.5
95.7
85.5
72.9
16.5
95.7
MITOD (Lee, 2021)
91.0
82.6
21.6
108.3
91.0
82.1
21.0
107.5
РPTоO (Su al., 2021)
79.4
18.6
102.9
87.1
79.1
19.2
102.3
SimpleToD-Be (Wang et 2022)
72.1
94.5
GALAXY al., 2022b)
94.4
85.3
110.4
86.2
SPACE3 (He al., 2022a)
86.1
BORT (Sun al., 2022)
93.8
85,8
18.5
108.3
Table 1: Modularly EToD performance on MultiWOZ2.0 O and MultiWOZ2.1. The highest scores are marked with underlines. We adopted reported results from published literature (Zhang al., 2020b, 2019; He et al., 2022b).
Match
Success
BLEU
designed to improve adaptation ability. BORT (Sun et al.. 2022) added a denoising reconstruction task to reconstruct the original context from generated dialogue states. MTToD (Lee, 2021) introduced
Modularly FTol withou Pre-tmined Model
NDV (Wen et al., 2017)
90.4
83.2
21.2
Suqui by by (l.ei et al., 2018)
92.7
85.4
25.3
FSDM (Shu et al., 2019)
93.5
86.2
25.8
NOSS (Liang ct al., 2019)
95.1
86.0
25,9
span prediction pre-training task. SPACE-3 (He et al., 2022a) further improved over GALAXY with UniLM backbone, where five pre-training objec- tives are applied better understand semantic in- formation for task-oriented dialogue. Recently, encoder-decoder PLMs have shown the potential of converting EToD into other task forms like QA (Tian et al., 2022; Xie et al., 2022).
LABES 525 (Zhang et al., 20206)
96.4
82.3
25.6
Modularly EToD with Pre-trineed Model
ARDM (Wu al., 2021b)
SOLOIST (Peng et al., 2021)
87.1
BORT (Sun al., 2022)
89.7
25.9
SPACE3 (He al., 2022a)
97.7
88.2
23.7
Modularly EToD performance on CamRest676 (Wen al., 2017) We adopted reported results from published literature (Zhang et al. (2020b); Sun al. (2022)). Match metric measures whether the entity chosen at the end of each dialogue aligns with the entities specified by the user.
Table 2:
3.1.3 Leaderboard and Takeaway.
Leaderboard: Leaderboard for the widely used datasets: Multiwoz2. 0, MultiwOz2. and Camrest 67 is shown in Table and Table 2. The widely used metrics are BLEU, Inform, Success, and Combined. Detailed descriptions of datasets and metrics are shown in Appendix A.1.
EToD. MinTL (Lin et al., 2020) considered train- ing EToD with PLMs in the Seq2Seq manner, where two different decoders are introduced to track belief state and predict response, respectively. PPTcD (Su al., 2021) recast ToD subtasks into prompts and leveraged the multitask transfer learn- ing of T5 (Raffel al., 2020). Huang et al. (2022) embedded KB information into the language model for implicit knowledge access.
Takeaway: As seen, we have the following ob- servations: (1) PLM Attains Improvement, We ob-
serve that most modularly EToD with PLM outper- forms the modularly EToD without PLM, which in- dicates that knowledge inferred from pre-trained model benefit EToD; (2) Shared Knowledge Leverage. Since each module (i.e., NLU. DST, PL, NL.G) highly related, modularly EToD can en- able the model to fully utilize shared knowledge across all modules.
In addition, another series of works devised unique pre-training objectives for encoder-decoder transformers. GALAXY (He et al., 2022b) intro- duced a dialog act prediction pre-training task for policy optimization. Godel (Peng et al., 2022) leveraged a new phase of grounded pre-training

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Embedding Technique a. Entity Triplct Representation
Related Work
Illustration
MemN2N (Bordes et al., 2017), KVRet (Eric and Manning, 2017), Mem2Seq (Madotto et al., 2018), RosaNet (Raghu et al., 2019), GLMP (Wu et al., 2019), DDMN (Wang et al., 2020), DFNet (Qin et al., 2020b), IR-Net (Ma et al., 2021), WMM2Seq (Chen el al. 2019b), MCL (Qin et al., 2021a)
Pandkar
(Prie
Chasp
Pandas
[lac
Narth
Wardy
Booc
West
b. Row-level Representation
KB-InfoBot (Dhingra et al., 2017),
MLM (Reddy et al., 2018). CDNet (Raghu et al., 2021), DSR (Wen et al., 2018),
Fondaar
Narrth
KB-Retriever (Qin et al., 2019b), HM2Seq (Zeng et al., 2022)
Rew2
c. Graph Representation
GraphDialog (Yang et al., 2020a), Fg2seq (He et al., 2020b), DialokG (Rony et al., 2022), GraphMemDialog (Wu et al., 2021a), COMET (Gou al., 2021), MAKER (Wan et al., 2023)
(Price
Chesp
Pondoc
North
(Price
Mordy
Weet
Table 3: Three types of KB Representation in EToD, including (a) entity triple representation; (b) row-level representation and (c) graph representation.
3.2 Fully EToD
responses through a template-fille decoder.
In the following, we describe the recent dominant fully EToD works according to the category of KB representation, which is illustrated in Fig. 3(c).
3.2.2 Row-level Representation.
While triplct representation is a direct approach for representing KB entities, it has the drawback of ignoring the relationship across entities in the same row. To migrate this issue, some works investigated the row-level representation for KB.
3.2.1 Triplet Representation.
Specifically, given a knowledge base (KB), triplet representation stores each KB entity in a (subject, relation, object) representation. For example, all triplets can be formularized as (centric entity of 5th row, slot title of jth column, entity of t row in jth column). (e.g., (Valero, Type, Gas Station)).
In particular, CBB-InfBBo (Dhingra et al., 2017) first utilized posterior distribution over KB rows. Reddy et al. (2018) proposed three-step re- trieval model, which can select relevant KB rows in the first step. Wen al. (2018) used entity similar- ity the criterion for selecting relevant KB rows. Qin et al. (2019b) employed a two-step retrieving procedure by first selecting relevant KB rows and then choosing the relevant KB column. Recently, Zeng et al. (2022) proposed to store KB rows and dialogue history into two separate memories.
The KB entity representation is calculated by the sum of the word embedding of the subject and rela- tion using bag-of-words approaches. It is one of the most widely used approaches for representing K.B. Specifically, Eric and Manning (2017) employed a key-value retrieval mechanism to retrieve KB knowledge triplets. Other works treat KB and dia- logue history equally as triplet memories (Madotto et al., 2018; Wu et al., 2019; Chen et al., 2019b; He et al., 2020a; Qin et al., 2021a). Memory net- works (Sukhbaatar et al., 2015) have been applied to model the dependency between related entity triplets in KB (Bordes et al., 2017; Wang et al., 2020) and improves domain scalability (Qin et al., 2020b; Ma et al., 2021). To improve the response quality with triplet KB representation, Raghu et al. (2019) proposed BOSS-NES to disentangle NLG and KB retrieval and Hong et al. (2020) generated
3.2.3 Graph Representation
Though row-level representation achieves promis- ing performance, they neglect the correlation be- tween KB and dialogue history. To solve this issue, series of works focus on better contextualizing entity embedding in KB by densely connecting enti- ties and corresponding slot titles in dialogue history. This can be done with either graph-based reasoning or attention mechanism, where entity presentations are fully aware of other entities or dialogue con- text. To this end, Yang et al. (2020a) facilitated

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SMID
MuREWOZI
BEFU
REFU
Eumy Ziggler
Repievntiniioe
KVRet (Einc and Manning, 2017)
13.2
16.0
62.9
53.3
41.5
Men22on (Madotto 2018)
CLMP (Wa al.,
148
60.0
69.6
62.6
53.0
6.9
524
35.4
24.4
28.1
Boxatet:: (Raghu et al, 2019)
533
35.9
50.2
34.5
21.6
5.7
25.3
26.2
24.8
23.4
DONRY (Wing et 2020)
17.7
55.6
65.0
47.2
12/4
314
30.6
32.9
30.5
prftet (Qin 20208)
14.4
62.7
73.1
57.6
$7.9
94
35.1
400
28.1
30.6
2000xx
IR-Mtet (Ma et 2021)
163
63.2
10,9
37.5
X02 (Qin et 2021a)
12.2
60.9
70.6
62.6
39.0
Cos-ieall Reperomtitiin
D.S. (Win at 2018)
12.7
51.9
52.1
50.1
32.0
9.1
30.0
33.4
28.0
27.1
XLM (Reday atal, 2018)
15.6
35.5
67.4
54,8
45.1
9.2
27.8
29.8
27.4
25.2
2019h)
13.9
54.5
#N28ag (Zeng et 2022)
14.6
63.1
73.9
61
.6.2
Graph Repreastintiioe
FgPSeg
56.1
Graphbialog (Ying 2020g)
13.7
60.7
72.8
54.2
GraphMenDlalown Laloo ot 2021a)
18.8
68.5
75.9
62.3
56.3
14,9
402
42.8
48.8
26.4
SPT2-EE (Madetto
53.5
COMST (Goz et 2022))
17.3
63.6
27.6.
56.0
Podulerized zed Pre-Tiaining (Qin et 2021b)
18.8
6338
75.0
58.4
39.1
13.6
36.3
41.
362
31.2
Diale8G
2022)
2000
UniFle WAKKR (Win of 2003)
2022)
25.0
71.1
18.8
54.2
Table 4: Fully EToD performance on SMD and Multiwoz2. 1. Ent.F1, Fch.F1, Wca.F1, Nav.Fl, Res.F1, Att Fl.and HotFF stand for abbreviation of Entity FI, Schedule FI, Weather FI, Navigation FI, Restaurant Fl and Hotel FI, respectively. We adopted reported results from published literature (Qin et al., 2020b; Wu al., 2021a; Wang et al., 2020; Gou et al., 2021)
entity contextualization by applying truph-baser multi-hop reasoning on the entity graph. Wu al. (2021a) Ia) proposed a graph-based memory network to yield context-aware representations. Another series of works leveraged transformer architecture to learn better entity representation, where the de- pendencies between dialogue history and KB were learned via self-attention (He et al., 2020b; Gou et al., 2021; Rony et al., 2022: Qin et al., 2023b; Wan et al., 2023).
4 Future Directions
This section will discuss new frontiers for EToD, hoping to facilitate follow-up research in this field.
4.1 LLM for EToD
Recently, Large Language Models (LLMs) have gained considerable attention for their impressive performance across various Natural Language Pro- cessing (NLP) benchmarks (Touvron al., 2023; OpenAI, 2023; Driess et al., 2023). These models are capable to execute predetermined instructions and interface with external resources, such as APIs (Patil et al., 2023) and knowledge databases. This positions LLMs as promising candidates for end- to-end dialogue systems (EToD). Existing research has also explored to apply LLMs in task-oriented dialogue (ToD) scenarios, using both few-shot and zero-shot learning paradigms (Pan al., 2023; Heck al., 2023; Hudevcek and Dusek, 2023; Parikh et al., 2023).
3.2.4 Leaderboard and Takeaway
Leaderboard:
comprehensive leader-
board for widely used dataset: SMD and Multiiwoz2.1 1 is shown in Table The widely used metrics for fully EToD are BLEU and Fl. Detailed information of datasets and metrics are shown in Appendix A.2.
Takeaway: Compaunderline modular EToD, fully EToD brings two major advantages. (1) Hu- man Annotation Efforts Underlineuction. Modu- larly EToD still requires modular annotation data for intermediate supervision. In contrast, fully EToD only requires the hialogue-responee pairs, which can greatly underlineuce human annotation efforts; (2) KB Retrieval End-to-end Training. Un- like the non-differentiable KB retrieval in mod- ularly EToD, fully EToD can optimize the KB retrieval process in a fully end-to-end paradigm, which can enhance the KB retrieval ability.
However, several critical challenges remain to be addressed in EToD in future research. We sum- marize the main challenges as follows:
1. Safety and Risk Mitigation: LLMs like chat- bots can sometimes generate harmful bi- ased responses (OpenAI, 2023), posing seri- ous safety concerns. It is crucial improve their controllability and interpretability. One promising approach is integrating human feed-

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back during training (Bai et al., 2022; Chung ct al., 2022).
4.3 Pre-training Paradigm for Fully EToD
Pre-trained Language Models have shown remark- able success in open-domain dialogues. ((Bao al.. 2021; Shuster et al., 2022)). However, there is rel- atively little research addressing how to pre-train fully EToD. We argue that the main reason for hindering the development of pre-training fully EToD the lack of large amounts of knowledge- grounded dialogue for pre-training.
2. Complex Conversations Management:
LLMs have limitations in managing complex, multi-turn dialogues (Heck et al., 2023; Pan et al., 2023). EToDs often require advanced context modeling and reasoning abilities, which is an area ripe for improvement.
3. Domain Adaptation: For task-oriented di- alogue, LLMs need to gain specifie domain knowledge. However, simply suppling knowl- edge with finetuning or prompting may lead to problems like catastrophic forgetting or bi- ased attention (Liu et al., 2023). Finding a balanced approach for knowledge adaptation remains a challenge.
We summarize the core challenges for pre- training fully EToD: (1) Data Scarcity: Since the annotated KB-grounded dialogues are scarce, how automatically augment large amount of training data is a promising direction; (2) Task dask-specifal fic Pre-training: Un- like the traditional general-purpose mask language modeling pre-trainin training objective, the unique chal- lenge for task-oriented dialogue system is how to make KB retrieval. Therefore, how to inject KB retrieval ability in the pre-training stage is worth exploring.
In addition to these challenges, there are also emerging opportunities that could further enhance the capabilities LLMs in EToD systems. These opportunities are summarized below:
4.4 Knowledge Transfer
1. Meta-learning & Personalization: LLMs can adapt quickly with limited examples. This paves the way for personalized dialogues through meta-learning algorithms.
With the development of traditional pipeline task- oriented dialogue systems, there exist various pow- erful modularized ToD models, such as NLU (Qin et al., 2019a; Zhang et al., 2020a), DST (Dai et al., 2021; Guo et al., 2022; Chen et al., 2022), DPL (Chen et al., 2019a; Kwan et al., 2022) and NLG (Wen al., 2015; Li et al., 2020). A natural and interesting research question is how to transfer the dialogue knowledge from well-trained modu- larized ToD models to modularly or fully EToD.
2. Multi-agent Collaboration & Self-learning from Interactions: The strong language mod- eling capabilities of LLMs make self-learning from real-world user interactions more fea- sible (Park et al., 2023). This can advance collaborative, task-solving dialogue agents
The main challenge for knowledge transfer is Knowledge Preservation How to balance the knowledge learned from previous modularized dialogue models and current data is interesting direction explore.
4.2 Multi-KB Settings
Recent EToD models are limited to single-KI KB set- tings where dialogue is supported by single KB, which is far from the real-world scenario. There- fore, endowing EToD with the ability of reasoning over multiple KBs for each dialogue plays a vital role in real-world deployment. To this end, Qin et al. (2023a) take the first meaningful step to the multi-KB EToD.
4.5 Reasoning Interpretability
Current fully EToD models perform knowledge base (KB) retrieval via a differentiable attention mechanism. While appealing, such a black-box re- trieval method makes it difficult to analyze the pro- cess of KB retrieval, which can seriously hurt the user's 's trust. Inspired by Wei et al. (2022); Zhang et al. (2022), employing chain of thought in KB reasoning in fully EToD is promising direction improve the interpretability of KB retrieval.
The main challenges for multi-KB settings are as follows: (1) Multiple KBs Reasoning: How to reason across multiple KBs to retrieve rele- vant knowledge entries for dialogue generation is unique challenge; (2) KB Scalibility: When the number of KBs becomes larger in real-world scenarios, how to effectively represent all the KBs in a single model is non-trivial.
The main challenge for the direction is design of reasoning steps: how to propose an ap-

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propriate chain of thought (e.g., when to retrieve rows and when to retrieve columns) express the KB retrieval process is non-trivial.
Benchmark Scale Limited: Current multi- modal dataset such as MMConv (Liao al., 2021) and SIMMC 2.0 (Kottur et al., 2021) are slightly limited in size and diversity, which hinders the de- velopment multi-modal EToD. Therefore, build- ing large benchmark plays vital role for promot- ing multi-modal EToD.
4.6 Cross-lingual EToD
Current success heavily relies on large amounts of annotated data that is only available for high- resource language (i.e., English), which makes it difficult to scale to other low-resource languages. Actually, with the acceleration of globalization, task-oriented dialogue systems like Google Home and Apple Siri required serve diverse user base worldwide, across various languages, which cannot be achieved by the previous monolingual dialogue. Therefore, zero-shot cross- loos-lln di- rection that can transfer knowledge from high- resource language to low-resource languages is a promising direction to solve the problem. To this end, Lin et al. (2021) and Ding et al. (2022) in- troduced BiToD and Glo benchmarks to promote cross-lingual task-oriented dialogue.
5 Conclusion
We made a first attempt to summarize the progress of end-to-end task-oriented dialogue systems (EToD) by introducing a new perspective of recent work, including modularly EToD and fully EToD. In addition, we discussed some new trends as well as their challenges in this research field, hoping to attract more breakthroughs future research.
A Acknowledgements
This work was supported by the National Natu- ral Science Foundation of China (NSFC) via grant 62306342, 62236004 and 61976072 and sponsored by CCF-Baidu Open Fund. This work was also supported by the Science and Technology innova- tion Program of Hunan Province under Grant No. 202 IRC4080 We are grateful for resources from the High Performance Computing Center of Cen- tral South University.
The main challenge for zero-shot cross- lingual EToD includes: (1) Knowledge base Alignment: A unique challenge for cross- lingual EToD is the knowledge base (KB) align- ment. How to effectively align the KB struc- ture information across different languages is an interesting research question to investigate; (2) Unified Cross-lingual Model: Since different modules (e.g., DST, DPL. and NLG) have heterogeneous structural information, how to build a unified cross-lingual model to align dialogue in- formation across heterogeneous input in all lan- guages is a challenge.
Limitation
This study presented comprehensive review and unified perspective on existing approaches and re- cent trends in end-to-end task-oriented dialogue systems (EToD). We have also created the first public resources website to help researchers stay updated on the progress of EToD. However, the current version primarily focuses on high-level comparisons of different approaches, such as over- all system performance, rather than fine-grained analysis. In the future, intend to include more in-depth comparative analyses gain a better un- derstanding of the advantages and disadvantages of various models, such as comparing KB retrieval results and performance across different domains.
4.7 Multi-modal EToD
Current dialogue systems mainly handle plain text input. Actually, we experience the world with multiple modalities (e.g., language and image). Therefore, building multi-modal EToD system that is able handle multiple modalities an im- portant direction to investigate. Unlike the tradi- tional single-modal dialogue system which can be supported by the corresponding KB, multi-modal EToD requires both the KB and image features to yield an appropriate response.
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A Datasets and Metrics
Multiwoz2.14 .. Qin et al. (2020b) introduces an extension of Multiwoz2. where they anno- tate the corresponding KB for each dialogue.
A.1 Datasets and Metries for Modularly EToD
A.2.2 Metries
A.1.1 Dataset
Fully EToD adopts BLEU and Ent ity F1 to eval- uate the fluent generation and KB retricval ability, respectively.
Three commonly used datasets for modularly EToD CamRest Multiwoz2. O, and Multiwoz2.1.
BLEU has been described in Section A.I.I.
67mRest (Wen et al., 2017) is relatively small-scale restaurant domain dataset, which con- sists of 408/136//33 dialogues for training/valida- tion/test.
Entity F1 Eric and Manning (2017) is used to measure the difference between entities in the system and gold responses by micro-averagee the precision and recall.
Multiwoz2... (Budzianowski al., 2018) one the most widely used ToD dataset. It con- tains over 8,000 dialogue sessions and 7 different domains including: restaurant, hotel, attraction, taxi, train, hospital and police domain.
B Related Work
Modular task-oriented dialogues typically consist of spoken language understanding (SLU), dialogue state tracking (DST), dialogue manager (DM) and natural language generation (NLG), which have achieved significant success. Recently, numerous surveys summaries the recent progress of modu- lar task-oriented dialogue systems. Specifically, Louvan and Magnini (2020); Larson and Leach (2022) and Qin et al. (2021c) summarize the re- cent progress of neural-based models for SLU. On DST, Balaraman et al. (2021b) and Jacqmin et al. (2022b) review the recent neural approaches and highlight the need for greater exploration on generalizability within the field. In terms of dia- logue management, Dai al. (2020) concentrates on challenges like model scalability, data scarcity, and improving training efficiency. For natural lan- guage generation (NLG). Santhanam and Shaikh (2019) provides a comprehensive overview of the past, present, and future directions of NLG. Finally, Chen et al. (2017), Zhang et al. (2020c) and Ni et al. (2023) provide overarching review of the dia- logue system whole, emphasising the impact of deep learning technologies.
Multiwoz2.1 (Eric et al., 2019) is an im- proved version of Multiwoz2 C, where incor- rect slot annotations and dialogue acts were fixed.
A.1.2 Metrics
The widely used metrics for modularly EToD are BLEU, Inform, Success, and Combined.
BLEU (Papineni et al., 2002) is used to measure the fluency of generated response by calculating n-gram overlaps between the generated response and the gold response.
Inform and Success (Budzianowski al., 2018). Inform measures whether the system pro- vides an appropriate entity and Success mea- sures whether the system answers requested attributes.
Combined (Budzianowski et al., 2018) is comprehensive metric considering BLEU, Inform, and Success, which can be calculated by: Combined (Inform + success) x 0.5 BLEU).
Compared the existing work, we focus on the end-to-end task-oriented dialogue system. To the best of our knowledge, this is the first com- prehensive survey of the end-to-end task-oriented dialogue system. We hope that this survey can attract more breakthroughs on future research.
A.2 Datasets and Metrics for Fully EToD
A.2.1 Dataset
SMD (Eric and Manning, 2017) and Multi fultiwoz2.1 (Qin et al., 2020b) are two popular datasets for evaluating fully EToD.
SMD Eric and Manning (2017) proposed Stan- ford Multi-turn Multi-domain Task-oriented Dia- logue Dataset, which includes three domains: navi- gation, weather, and calendar.


Reviews:
[{"review_id": "OHPCFD1mc7", "reviewer": "Reviewer_ynBS", "summary": "", "questions": "Have authors done a survey of the recent evaluation protocols for modular and full end-to-end TOD systems?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your acknowledgements and interests on our work. We will carefully address your question as below:\n\n**Question1**: adding detailed discussion on the evaluation protocols for end-to-end TOD is better. This includes automatic metrics, human evaluation and recent LLM based metrics like BERTScore and its variant.\n\n**Answer1**: Thank you for your constructive comments. We totally agree that a detailed discussion on the evaluation protocols for end-to-end TOD is important. To this end, we have summarized the widely used automatic evaluation metrics for both modular and full end-to-end TOD systems, which is illustrated in the appendix due to the page limitation. We will follow your suggestions to add more discussions on human evaluation and recent LLM based metrics in the next version.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This work is important as it is the first survey of the field. The paper has clear taxonomy, good coverage, leaderboards and good discussion on future directions. All the relevant data is made publicly available. Overall, this work can provide visibility, transparency and traction to the field.", "reasons_to_reject": "The paper lacks detailed discussion on the evaluation protocols for end-to-end TOD. This includes automatic metrics, human evaluation and recent LLM based metrics like BERTScore and its variant.", "questions_to_authors": "Have authors done a survey of the recent evaluation protocols for modular and full end-to-end TOD systems?", "paper_topic_and_main_contributions": "Goal:  To systematically survey end-to-end task oriented dialog (TOD) systems\n\nDetails\n1. The paper is the first survey of end-to-end TOD systems.\n2. The taxonomy (modular e2e v/s fully e2e) is clear and important.\n3. Authors provide a public website containing all resources which can be beneficial for the TOD community.\n4. Leaderboards and collation of results can help progress of the field", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "LTDSZcggyy", "reviewer": "Reviewer_TKmZ", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your acknowledgements and interests on our work. We will carefully address your questions as below:\n\n**Question1**: Future Directions section should discuss the opportunities and challenges brought by large language models to this field.\n\n**Answer1**: Thank you for your insightful feedback. This is a very good suggestion to improve our work. We will follow your suggestion to add the opportunities and challenges brought by large language models (LLM) for end-to-end task-oriented dialogue system in the next version. For example, some challenges and opportunities on task-oriented dialogue systems brought by LLMs are listed as follows:\n\n**Challenges**:\n\n1.\tSafety and Risk Mitigation: As highlighted in the GPT4 technical report [1], LLMs like chatbots can sometimes generate harmful or biased responses, posing serious safety concerns. It is crucial to improve their controllability and interpretability. One promising approach is integrating human feedback during training [2].\n\n2.\tComplex Conversations: LLMs still have limitations in managing complex, multi-turn dialogues [3]. These often require advanced context modeling and reasoning abilities, which is an area ripe for improvement.\n\n3.\tDomain Knowledge Acquisition: For task-oriented dialogue, LLMs need to gain specialist knowledge. However, brutal-force domain knowledge injection may lead to problems like catastrophic forgetting or biased attention [4]. Finding a balanced approach to knowledge acquisition remains a challenge.\n\n**Opportunities**:\n\n1.\tMultimodal Ability: Recent advancements have enabled the pairing of LLMs with vision models [5,6]. This presents an opportunity to create richer, multimodal dialogues, as LLMs can process visual signals in addition to text.\n\n2.\tMeta-learning & Personalization: LLMs have shown a remarkable ability to adapt quickly from a few examples, as noted in a recent survey [7]. Utilizing meta-learning algorithms, LLMs could offer personalized dialogue based on user preferences.\n\n3.\tMulti-agent Collaboration & Self-learning from interactions: LLMs' strong language modeling makes self-learning from real user interactions more feasible [8]. This could emulate real-world collaborative task-solving and elevate the level of dialogue agent intelligence.\n\n\n[1] OpenAI. GPT-4 Technical Report. ArXiv abs/2303.08774.\n\n[2] Anthropic. Claude: an AI assistant focused on being helpful, harmless, and honest.\n\n[3] Pan et al. A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding. ArXiv abs/2304.04256.\n\n[4] Liu et al. Lost in the Middle: How Language Models Use Long Contexts. ArXiv abs/2307.03172.\n\n[5] Driess et al. PaLM-E: An Embodied Multimodal Language Model. Presented at the International Conference on Machine Learning.\n\n[6] Peng et al. Kosmos-2: Grounding Multimodal Large Language Models to the World. ArXiv abs/2306.14824.\n\n[7] Zhao et al. A Survey of Large Language Models. ArXiv abs/2303.18223.\n\n[8] Park et al. Generative Agents: Interactive Simulacra of Human Behavior. ArXiv abs/2304.03442.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Please refer to the “Paper Topic And Main Contributions” part. ", "reasons_to_reject": "Please refer to the “Paper Topic And Main Contributions” part. ", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper is well-written, with clear and attractive formatting, and strong readability. The literature review on end-to-end task-oriented dialogue is detailed, and the summary and analysis are comprehensive. \n\nOne shortcoming is that the Future Directions section does not discuss the opportunities and challenges brought by large language models to this field.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "The last sentence of the title in Figure 4 is missing a period."}, "extra_text": {}}, {"review_id": "c29IgPlAnU", "reviewer": "Reviewer_334w", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your acknowledgments and interest on our work! We notice your only main concerns are the (1) ***Lack of Discussion on Integration with Large Pre-trained Models*** and (2) ***Omissions of Recent Progress***. However, we sincerely think there might be some misconceptions. In the following, **we will clarify your concerns and we would greatly appreciate it if you can re-consider the work considering our clarification**.\n\n**Question1**: Lack of Discussion on Integration with Large Pre-trained Models.\n\n**Answer1**: Thank you for your valuable comment. We sincerely think that the main contribution of our work is making the first attempt to provide a comprehensive survey for end-to-end task-oriented dialogue, including new taxonomy, new frontiers and available resources, which are acknowledged by you, Reviewer ynBS and Reviewer TKmZ. Adding discussion on integration with large pre-trained models will help to enrich our work and we can easily provide more analysis in the Future Directions section in the next version.\n\nWe will follow your suggestion to add a detailed discussion on integration with large pre-trained models for end-to-end task-oriented dialogue in the next version. For example, some challenges and opportunities on task-oriented dialogue systems brought by LLMs are listed as follows:\n\n**Challenges**:\n\n1. Safety and Risk Mitigation: As highlighted in the GPT4 technical report [1], LLMs like chatbots can sometimes generate harmful or biased responses, posing serious safety concerns. It is crucial to improve their controllability and interpretability. One promising approach is integrating human feedback during training [2].\n\n2. Complex Conversations: LLMs still have limitations in managing complex, multi-turn dialogues [3]. These often require advanced context modeling and reasoning abilities, which is an area ripe for improvement.\n\n3. Domain Knowledge Acquisition: For task-oriented dialogue, LLMs need to gain specialist knowledge. However, brutal-force domain knowledge injection may lead to problems like catastrophic forgetting or biased attention [4]. Finding a balanced approach to knowledge acquisition remains a challenge.\n\n**Opportunities**:\n\n1. Multimodal Ability: Recent advancements have enabled the pairing of LLMs with vision models [5,6]. This presents an opportunity to create richer, multimodal dialogues, as LLMs can process visual signals in addition to text.\n\n2. Meta-learning & Personalization: LLMs have shown a remarkable ability to adapt quickly from a few examples, as noted in a recent survey [7]. Utilizing meta-learning algorithms, LLMs could offer personalized dialogue based on user preferences.\n\n3. Multi-agent Collaboration & Self-learning from interactions: LLMs' strong language modeling makes self-learning from real user interactions more feasible [8]. This could emulate real-world collaborative task-solving and elevate the level of dialogue agent intelligence.\n\n[1] OpenAI. GPT-4 Technical Report. ArXiv abs/2303.08774.\n\n[2] Anthropic. Claude: an AI assistant focused on being helpful, harmless, and honest.\n\n[3] Pan et al. A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding. ArXiv abs/2304.04256.\n\n[4] Liu et al. Lost in the Middle: How Language Models Use Long Contexts. ArXiv abs/2307.03172.\n\n[5] Driess et al. PaLM-E: An Embodied Multimodal Language Model. Presented at the International Conference on Machine Learning.\n\n[6] Peng et al. Kosmos-2: Grounding Multimodal Large Language Models to the World. ArXiv abs/2306.14824.\n\n[7] Zhao et al. A Survey of Large Language Models. ArXiv abs/2303.18223.\n\n[8] Park et al. Generative Agents: Interactive Simulacra of Human Behavior. ArXiv abs/2304.03442.\n\n**Question 2**: Omissions of Recent Progress: Some noteworthy recent developments in EToD, such as QToD (Q-TOD: A Query-driven Task-oriented Dialogue System) and Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog, are not included in the paper.\n\n**Answer 2**: Thanks for your kind mention. **We sincerely believe this is a misunderstanding**. Actually, the mentioned work (1) Q-TOD: A Query-driven Task-oriented Dialogue System (Q-ToD) [1] and (2) Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog (MAKER) [2] have been included in our paper.\n\nFor the QToD work, we categorized it under Modularly EToD w/ PLM in Figure 2. Moreover, in section 3.1.2, we specifically highlighted and discussed its innovative paradigm of shaping the ToD task into QA formats. This represents our understanding and appreciation of its significance in task-oriented dialogue systems.\n\nFor the MAKER paper, we grouped it under Graph Representation in Table 3. We also highlighted MAKER's latest SOTA results on two ToD leaderboards in Table 4. Finally, in section 3.2.3, we have further discussed the general characteristics of such works, demonstrating our deep understanding of its value and relevance to the field.\n\nWe will add more discussion in the next version.\n\n[1] Xin Tian, Yingzhan Lin, Mengfei Song, Fan Wang, Huang He, Shuqi Sun, and Hua Wu. 2022. Q-TOD: A Query-driven Task-oriented Dialogue System.\n\n[2] Fanqi Wan, Weizhou Shen, Ke Yang, Xiaojun Quan, and Wei Bi. 2023. Multi-grained knowledge retrieval for end-to-end task-oriented dialog.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper provides a thorough overview of the progress made in the field of task-oriented dialogue and examines emerging frontier areas while summarizing the associated challenges.\n\nThe writing is accessible and straightforward, making the content easily understandable to readers.", "reasons_to_reject": "Lack of Discussion on Integration with Large Pre-trained Models: In the era of large pre-trained models, the paper fails to address the integration of task-oriented dialogue with these models, which could have significant implications for EToD research.\n\nOmissions of Recent Progress: Some noteworthy recent developments in EToD, such as QToD (Q-TOD: A Query-driven Task-oriented Dialogue System) and Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog, are not included in the paper.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper presents a comprehensive review and introduces a unified perspective on existing EToD approaches and recent trends. It aims to advance the development of EToD research by highlighting its contributions, including being the survey in the field,  discussing potential frontier areas and challenges, and providing abundant resources through a public website for EToD researchers. Overall, the paper contributes to the EToD research community by presenting a  survey and introducing a new taxonomy. \nStrengths:\nThe paper provides a thorough overview of the progress made in the field of task-oriented dialogue and examines emerging frontier areas while summarizing the associated challenges.\nThe writing is accessible and straightforward, making the content easily understandable to readers.\nWeaknesses:\nLack of Discussion on Integration with Large Pre-trained Models: In the era of large pre-trained models, the paper fails to address the integration of task-oriented dialogue with these models, which could have significant implications for EToD research.\nOmissions of Recent Progress: Some noteworthy recent developments in EToD, such as QToD (Q-TOD: A Query-driven Task-oriented Dialogue System) and Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog, are not included in the paper.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Q-TOD: A Query-driven Task-oriented Dialogue System\n\nMulti-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog, are not included in the paper."}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a survey on end-to-end task-oriented dialogue systems. The survey stands out due to the following features:

1. It is the first survey to comprehensively summarize efforts in this field.
2. It introduces a new taxonomy: modular ETOD and Fully ETOD.
3. The inclusion of resources such as a public website and leadership board.
4. A discussion on frontier trends in the field.

The soundness scores were recorded as (4, 4, 3). All reviewers found the paper well-written and easy to follow. The retrospection and analysis of prior work are both detailed and comprehensive. The fresh taxonomy is lucid, and the provision of resources like a public website and leadership board can potentially enhance visibility, transparency, and traction in the domain.

The excitement scores were (4, 3, 3). Despite its strengths, the work has a few areas of improvement:

1. The challenges associated with evaluations are not sufficiently addressed.
2. While Large Language Models (LLMs) are gaining prominence in NLP research, their significance isn't thoroughly discussed.

In conclusion, this work is **Sound and Moderately Exciting**.
Decision: Accept-Main