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"title": "ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems",
"authors": [
{
"first": "Qi",
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"last": "Zhu",
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{
"first": "Zheng",
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{
"first": "Yan",
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"last": "Fang",
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{
"first": "Xiang",
"middle": [],
"last": "Li",
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{
"first": "Ryuichi",
"middle": [],
"last": "Takanobu",
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{
"first": "Jinchao",
"middle": [],
"last": "Li",
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{
"first": "Baolin",
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{
"first": "Jianfeng",
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{
"first": "Xiaoyan",
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"last": "Zhu",
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{
"first": "Minlie",
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"last": "Huang",
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"email": "aihuang@tsinghua.edu.cn"
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"abstract": "We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab (Lee et al., 2019b), ConvLab-2 inherits ConvLab's framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. The analysis tool presents rich statistics and summarizes common mistakes from simulated dialogues, which facilitates error analysis and system improvement. The interactive tool provides a user interface that allows developers to diagnose an assembled dialogue system by interacting with the system and modifying the output of each system component.",
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"text": "We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab (Lee et al., 2019b), ConvLab-2 inherits ConvLab's framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. The analysis tool presents rich statistics and summarizes common mistakes from simulated dialogues, which facilitates error analysis and system improvement. The interactive tool provides a user interface that allows developers to diagnose an assembled dialogue system by interacting with the system and modifying the output of each system component.",
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"text": "Task-oriented dialogue systems are gaining increasing attention in recent years, resulting in a number of datasets (Henderson et al., 2014; Budzianowski et al., 2018b; Rastogi et al., 2019 ) and a wide variety of models (Wen et al., 2015; Peng et al., 2017; Lei et al., 2018; Wu et al., 2019; . However, very few opensource toolkits provide full support to assembling an end-to-end dialogue system with state-of-the-art models, evaluating the performance in an end-toend fashion, and analyzing the bottleneck both qualitatively and quantitatively. To fill the gap, we have developed ConvLab-2 based on our previous dialogue system platform ConvLab (Lee et al., 2019b) . ConvLab-2 inherits its predecessor's framework and extend it by integrating many recently proposed state-of-the-art dialogue models. In addition, Figure 1 : Framework of ConvLab-2. The top block shows different approaches to build a dialogue system. two powerful tools, namely the analysis tool and the interactive tool, are provided for in-depth error analysis. ConvLab-2 will be the development platform for Multi-domain Task-oriented Dialog Challenge II track in the 9th Dialog System Technology Challenge (DSTC9) 1 .",
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"text": "(Henderson et al., 2014;",
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"text": "Budzianowski et al., 2018b;",
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"text": "Rastogi et al., 2019",
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"text": "(Wen et al., 2015;",
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"text": "Peng et al., 2017;",
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"start": 258,
"end": 275,
"text": "Lei et al., 2018;",
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"start": 276,
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"text": "Wu et al., 2019;",
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"text": "(Lee et al., 2019b)",
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"text": "1",
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"section": "Introduction",
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"text": "As shown in Figure 1 , there are many approaches to building a task-oriented dialogue system, ranging from pipeline methods with multiple components to fully end-to-end models. Previous toolkits focus on either end-to-end models (Miller et al., 2017) or one specific component such as dialogue policy (POL) , while the others toolkits that are designed for developers (Bocklisch et al., 2017; Papangelis et al., 2020) do not have state-of-the-art models integrated. ConvLab (Lee et al., 2019b) is the first toolkit that provides various powerful models for all dialogue components and allows researchers to quickly assemble a complete dialogue system (using a set of recipes). ConvLab-2 inherits the flexible framework of Con-vLab and imports recently proposed models that achieve state-of-the-art performance. In addition, ConvLab-2 supports several large-scale dialogue datasets including CamRest676 (Wen et al., 2017), MultiWOZ (Budzianowski et al., 2018b) , DealOrN-oDeal (Lewis et al., 2017) , and CrossWOZ (Zhu et al., 2020) .",
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"text": "To support end-to-end evaluation, ConvLab-2 provides user simulators for automatic evaluation and integrates Amazon Mechanical Turk for human evaluation, similar to ConvLab. Moreover, it provides an analysis tool and a human-machine interactive tool for diagnosing a dialogue system. Researchers can perform quantitative analysis using the analysis tool. It presents useful statistics extracted from the conversations between the user simulator and the dialogue system. This information helps reveal the weakness of the system and signifies the direction for further improvement. With the interactive tool, researchers can perform qualitative analysis by deploying their dialogue systems and conversing with the systems via the webpage. During the conversation, the intermediate output of each component in a pipeline system, such as the user dialogue acts and belief state, are presented on the webpage. In this way, the performance of the system can be examined, and the prediction errors of those components can be corrected manually, which helps the developers identify the bottleneck component. The interactive tool can also be used to collect real-time humanmachine dialogues and user feedback for further system improvement.",
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"text": "2 ConvLab-2",
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"section": "Introduction",
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"text": "Each speaker in a conversation is regarded as an agent. ConvLab-2 inherits and simplifies Con-vLab's framework to accommodate more complicated dialogue agents (e.g., using multiple models for one component) and more general scenarios (e.g., multi-party conversations). Thanks to the flexibility of the agent definition, researchers can build an agent with different types of configurations, such as a traditional pipeline method (as shown in the first layer of the top block in Figure 1) , a fully end-to-end method (the last layer), and between (other layers) once instantiating corresponding models. Researchers can also freely customize an agent, such as incorporating two dialogue systems into one agent to cope with multiple tasks. Based on the unified agent definition that both dialogue systems and user simulators are treated as agents, ConvLab-2 supports conversation between two agents and can be extended to more general scenarios involving three or more parties.",
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"section": "Dialogue Agent",
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"text": "ConvLab-2 provides the following models for every possible component in a dialogue agent. Note that compared to ConvLab, newly integrated models in ConvLab-2 are marked in bold. Researchers can easily add their models by implementing the interface of the corresponding component. We will keep adding state-of-the-art models to reflect the latest progress in task-oriented dialogue.",
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"section": "Models",
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"text": "The natural language understanding (NLU) component, which is used to parse the other agent's intent, takes an utterance as input and outputs the corresponding dialogue acts. ConvLab-2 provides three models: Semantic Tuple Classifier (STC) (Mairesse et al., 2009) , MILU (Lee et al., 2019b) , and BERTNLU. BERT (Devlin et al., 2019) has shown strong performance in many NLP tasks. Thus, ConvLab-2 proposes a new BERTNLU model. BERTNLU adds two MLPs on top of BERT for intent classification and slot tagging, respectively, and fine-tunes all parameters on the specified tasks. BERTNLU achieves the best performance on MultiWOZ in comparison with other models.",
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"text": "The dialogue state tracking (DST) component updates the belief state, which contains the constraints and requirements of the other agent (such as a user). ConvLab-2 provides a rule-based tracker that takes dialogue acts parsed by the NLU as input.",
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"text": "Word-level DST obtains the belief state directly from the dialogue history. ConvLab-2 integrates four models: MDBT (Ramadan et al., 2018), SUMBT (Lee et al., 2019a) , and TRADE (Wu et al., 2019) . TRADE generates the belief state from utterances using a copy mechanism and achieves state-of-the-art performance on Multi-WOZ.",
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"text": "CrossWOZ (Zhu et al., 2020) is the first large-scale Chinese multi-domain Wizard-of-Oz dataset proposed recently. It contains 6,012 dialogues spanning over five domains. Besides dialogue acts and belief states, the annotations of user states, which indicate the completion of a user goal, are also provided. ConvLab-2 offers a rule-based user simulator and a complete set of models for building a pipeline system on the CrossWOZ dataset. Invalid system dialogue acts: -31%: Inform-Hotel-Parking -28%: Inform-Hotel-Internet Redundant system dialogue acts:",
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"text": "-34%: Inform-Hotel-Stars Missing system dialogue acts:",
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"text": "Recommend-Hotel-Parking-yes -21%: Recommend-Hotel-Parking-none -18%: Inform-Hotel-Parking-none Inform-Hotel-Parking-yes -17%: Inform-Hotel-Parking-none Inform-Hotel-Stars-4 -16%: Inform-Hotel-Internet-none",
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"text": "User dialogue acts that cause loop: -53% Request-Hotel-Phone-? -21% Request-Hotel-Post-? -14% Request-Hotel-Addr-? ",
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"text": "To evaluate a dialogue system quantitatively, ConvLab-2 offers an analysis tool to perform an end-to-end evaluation with a specified user simulator and generate an HTML report which contains rich statistics of simulated dialogues. Charts and tables are used in the test report for better demonstration. Partial results of a demo system in Section 3 are shown in Figure 2 and Table 1 . Currently, the report contains the following pieces of information for each task domain:",
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"text": "\u2022 Common errors of the NLU component, such as the confusion matrix of user dialogue acts. For the example in Table 1 , 34% of the requests for the Postcode in the Hotel domain are misinterpreted as the requests in the Hospital domain.",
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"text": "\u2022 Frequent invalid, redundant, and missing system dialogue acts predicted by the dialogue policy.",
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"text": "\u2022 The system dialogue acts from which the NLG component generates responses that confuse the user simulator. For the example in Table 1 , it is hard to inform the user that the hotel has free parking.",
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"text": "\u2022 The causes of dialogue loops. Dialogue loop is the situation that the user keeps repeating the same request until the max turn number is reached. This result shows the requests that are hard for the system to handle. The analysis tool also supports the comparison between different dialogue systems that interact with the same user simulator. The above statistics and comparison results can significantly facilitate error analysis and system improvement.",
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"text": "ConvLab-2 provides an interactive tool that enables researchers to converse with a dialogue system through a graphical user interface and modify intermediate results to correct system errors.",
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"text": "As shown in Figure 3 , researchers can customize their dialogue system by selecting the dataset and the model of each component. Then, they can interact with the system via the user interface. During a conversation, the output of each component is displayed on the left side as a JSON formatted string, including the user dialogue acts parsed by the NLU, the belief state tracked by the DST, the system dialogue acts selected by the policy and the final system response generated by the NLG. By showing both the dialogue history and the component outputs, the researchers can get a good understanding of how their system works.",
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"text": "In addition to the fine-grained system output, the interactive tool also supports intermediate output modification. When a component makes a mistake and the dialogue fails to continue, the researchers can correct the JSON output of that component to redirect the conversation by replacing the original output with the correct one. This function is helpful when the researchers are debugging a specific component.",
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"text": "In consideration of the compatibility across platforms, the interactive tool is deployed as a web service that can be accessed via a web browser. To use self-defined models, the researchers have to edit a configuration file, which defines all available models for each component. The researchers can also add their own models into the configuration file easily.",
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"text": "This section demonstrates how to use ConvLab-2 to build, evaluate, and diagnose a traditional pipeline dialogue system developed on the Mul-tiWOZ dataset.",
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"text": "import ... # import necessary modules # Create models for each component # Parameters are omitted for simplicity sys_nlu = BERTNLU(...) sys_dst = RuleDST(...) sys_policy = RulePolicy(...) sys_nlg = TemplateNLG(...) # Assemble a pipeline system named \"sys\" sys_agent = PipelineAgent(sys_nlu, sys_dst, sys_policy, sys_nlg, name=\"sys\") # Build a user simulator similarly but without DST user_nlu = BERTNLU(...) user_policy = RulePolicy(...) user_nlg = TemplateNLG(...) user_agent = PipelineAgent(user_nlu, None, user_policy, user_nlg, name=\"user\") # Create an evaluator and a conversation environment evaluator = MultiWozEvaluator() sess = BiSession(sys_agent, user_agent, evaluator) # Start simulation sess.init_session() sys_utt = \"\" while True: sys_utt, user_utt, sess_over, reward = sess. next_turn(sys_utt) if sess_over: break print(sess.evaluator.task_success()) print(sess.evaluator.inform_F1()) # Use the analysis tool to generate a test report analyzer = Analyzer(user_agent, dataset=\"MultiWOZ\") analyzer.comprehensive_analyze(sys_agent, total_dialog=1000) # Compare multiple systems sys_agent2 = PipelineAgent(MILU(...), sys_dst, sys_policy, sys_nlg, name=\"sys\") analyzer.compare_models(agent_list= [sys_agent, sys_agent2] , model_name=[\"bertnlu\", \"milu\"], total_dialog=1000)",
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"text": "Listing 1: Example code for the demo.",
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"text": "To build such a dialogue system, we need to instantiate a model for each component and assemble them into a complete agent. As shown in the above code, the system consists of a BERTNLU, a rule-based DST, a rule-based system policy, and a template-based NLG. Likewise, we can build a user simulator that consists of a BERTNLU, an agenda-based user policy, and a template-based NLG. Thanks to the flexibility of the framework, the DST of the simulator can be None, which means passing the parsed dialogue acts directly to the policy without the belief state.",
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"text": "For end-to-end evaluation, ConvLab-2 provides a BiSession class, which takes a system, a simulator, and an evaluator as inputs. Then this class can be used to simulate dialogues and calculate end-to-end evaluation metrics. For example, the task success rate of the system is 64.2%, and the inform F1 is 67.0% for 1000 simulated dialogues. In addition to automatic evaluation, ConvLab-2 can perform human evaluation via Amazon Mechanical Turk using the same system agent.",
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"text": "Then the analysis tool can be used to perform a comprehensive evaluation. Equipped with a user simulator, the tool can analyze and compare multiple systems. Some results are shown in Figure 2 and Table 1 . We collected statistics from 1000 simulated dialogues and found that \u2022 The demo system performs the poorest in the Hotel domain but always completes the goal in the Hospital domain.",
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"text": "\u2022 The sub-task in the Hotel domain is more likely to cause dialogue loops than in other domains. More than half of the loops in the Hotel domain are caused by the user request for the phone number.",
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"text": "\u2022 One of the most common errors of the NLU component is misinterpreting the domain of user dialogue acts. For example, the user request for the Postcode, address, and phone number in the Hotel domain is often parsed as in other domains.",
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"text": "\u2022 In the Hotel domain, the dialogue acts whose slots are Parking are much harder to be perceived than other dialogue acts.",
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"sec_num": "3"
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"text": "The researchers can further diagnose their system by observing fine-grained output and rescuing a failed dialogue using our provided interactive tool. An example is shown in Figure 3 , in which at first the BERTNLU falsely identified the domain as Restaurant. After correcting the domain to Hotel manually, a Recall NLU button appears. By clicking the button, the dialogue system reruns this turn by skipping the NLU module and directly use the corrected NLU output. Combined with the observations from the analysis tool, alleviating the domain confusion problem of the NLU component may significantly improve system performance.",
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"text": "ConvLab-2 is publicly available on https:// github.com/thu-coai/ConvLab-2. Resources such as datasets, trained models, tutorials, and demo video are also released. We will keep track of new datasets and state-of-the-art models. Contributions from the community are always welcome.",
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"section": "Code and Resources",
"sec_num": "4"
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"text": "We present ConvLab-2, an open-source toolkit for building, evaluating, and diagnosing a taskoriented dialogue system. Based on ConvLab (Lee et al., 2019b) , ConvLab-2 integrates more powerful models, supports more datasets, and develops an analysis tool and an interactive tool for comprehensive end-to-end evaluation. For demonstration, we give an example of using ConvLab-2 to build, evaluate, and diagnose a system on the MultiWOZ dataset. We hope that ConvLab-2 is instrumental in promoting the research on task-oriented dialogue.",
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"sec_num": "5"
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"text": "https://sites.google.com/dstc. community/dstc9/home",
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"text": "This work was jointly supported by the NSFC projects (Key project with No. 61936010 and regular project with No. 61876096), and the National Key R&D Program of China (Grant No. 2018YFC0830200). We thank THUNUS NExT Joint-Lab for the support.",
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"BIBREF0": {
"ref_id": "b0",
"title": "Rasa: Open source language understanding and dialogue management",
"authors": [
{
"first": "Tom",
"middle": [],
"last": "Bocklisch",
"suffix": ""
},
{
"first": "Joey",
"middle": [],
"last": "Faulkner",
"suffix": ""
},
{
"first": "Nick",
"middle": [],
"last": "Pawlowski",
"suffix": ""
},
{
"first": "Alan",
"middle": [],
"last": "Nichol",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tom Bocklisch, Joey Faulkner, Nick Pawlowski, and Alan Nichol. 2017. Rasa: Open source language un- derstanding and dialogue management.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Towards end-toend multi-domain dialogue modelling",
"authors": [
{
"first": "Pawel",
"middle": [],
"last": "Budzianowski",
"suffix": ""
},
{
"first": "I\u00f1igo",
"middle": [],
"last": "Casanueva",
"suffix": ""
},
{
"first": "Bo-Hsiang",
"middle": [],
"last": "Tseng",
"suffix": ""
},
{
"first": "Milica",
"middle": [],
"last": "Gasic",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Pawel Budzianowski, I\u00f1igo Casanueva, Bo-Hsiang Tseng, and Milica Gasic. 2018a. Towards end-to- end multi-domain dialogue modelling.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "MultiWOZ -a large-scale multi-domain wizard-of-Oz dataset for task-oriented dialogue modelling",
"authors": [
{
"first": "Pawe\u0142",
"middle": [],
"last": "Budzianowski",
"suffix": ""
},
{
"first": "Tsung-Hsien",
"middle": [],
"last": "Wen",
"suffix": ""
},
{
"first": "Bo-Hsiang",
"middle": [],
"last": "Tseng",
"suffix": ""
},
{
"first": "I\u00f1igo",
"middle": [],
"last": "Casanueva",
"suffix": ""
},
{
"first": "Stefan",
"middle": [],
"last": "Ultes",
"suffix": ""
},
{
"first": "Milica",
"middle": [],
"last": "Osman Ramadan",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ga\u0161i\u0107",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "5016--5026",
"other_ids": {
"DOI": [
"10.18653/v1/D18-1547"
]
},
"num": null,
"urls": [],
"raw_text": "Pawe\u0142 Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, I\u00f1igo Casanueva, Stefan Ultes, Osman Ra- madan, and Milica Ga\u0161i\u0107. 2018b. MultiWOZ -a large-scale multi-domain wizard-of-Oz dataset for task-oriented dialogue modelling. In Proceedings of the 2018 Conference on Empirical Methods in Nat- ural Language Processing, pages 5016-5026, Brus- sels, Belgium. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Semantically conditioned dialog response generation via hierarchical disentangled self-attention",
"authors": [
{
"first": "Wenhu",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Jianshu",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Pengda",
"middle": [],
"last": "Qin",
"suffix": ""
},
{
"first": "Xifeng",
"middle": [],
"last": "Yan",
"suffix": ""
},
{
"first": "William",
"middle": [
"Yang"
],
"last": "Wang",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "3696--3709",
"other_ids": {
"DOI": [
"10.18653/v1/P19-1360"
]
},
"num": null,
"urls": [],
"raw_text": "Wenhu Chen, Jianshu Chen, Pengda Qin, Xifeng Yan, and William Yang Wang. 2019. Semantically con- ditioned dialog response generation via hierarchical disentangled self-attention. In Proceedings of the 57th Annual Meeting of the Association for Com- putational Linguistics, pages 3696-3709, Florence, Italy. Association for Computational Linguistics.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "BERT: Pre-training of deep bidirectional transformers for language understanding",
"authors": [
{
"first": "Jacob",
"middle": [],
"last": "Devlin",
"suffix": ""
},
{
"first": "Ming-Wei",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "Kenton",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Kristina",
"middle": [],
"last": "Toutanova",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "4171--4186",
"other_ids": {
"DOI": [
"10.18653/v1/N19-1423"
]
},
"num": null,
"urls": [],
"raw_text": "Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Associ- ation for Computational Linguistics.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Multiwoz 2.1: Multi-domain dialogue state corrections and state tracking baselines",
"authors": [
{
"first": "Mihail",
"middle": [],
"last": "Eric",
"suffix": ""
},
{
"first": "Rahul",
"middle": [],
"last": "Goel",
"suffix": ""
},
{
"first": "Shachi",
"middle": [],
"last": "Paul",
"suffix": ""
},
{
"first": "Abhishek",
"middle": [],
"last": "Sethi",
"suffix": ""
},
{
"first": "Sanchit",
"middle": [],
"last": "Agarwal",
"suffix": ""
},
{
"first": "Shuyag",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Dilek",
"middle": [],
"last": "Hakkani-Tur",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1907.01669"
]
},
"num": null,
"urls": [],
"raw_text": "Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyag Gao, and Dilek Hakkani- Tur. 2019. Multiwoz 2.1: Multi-domain dialogue state corrections and state tracking baselines. arXiv preprint arXiv:1907.01669.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Neural approaches to conversational ai. Foundations and Trends R in Information Retrieval",
"authors": [
{
"first": "Jianfeng",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Michel",
"middle": [],
"last": "Galley",
"suffix": ""
},
{
"first": "Lihong",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "13",
"issue": "",
"pages": "127--298",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jianfeng Gao, Michel Galley, and Lihong Li. 2019. Neural approaches to conversational ai. Founda- tions and Trends R in Information Retrieval, 13(2- 3):127-298.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "User modeling for task oriented dialogues",
"authors": [
{
"first": "Izzeddin",
"middle": [],
"last": "G\u00fcr",
"suffix": ""
},
{
"first": "Dilek",
"middle": [],
"last": "Hakkani-T\u00fcr",
"suffix": ""
},
{
"first": "Gokhan",
"middle": [],
"last": "T\u00fcr",
"suffix": ""
},
{
"first": "Pararth",
"middle": [],
"last": "Shah",
"suffix": ""
}
],
"year": 2018,
"venue": "2018 IEEE Spoken Language Technology Workshop (SLT)",
"volume": "",
"issue": "",
"pages": "900--906",
"other_ids": {
"DOI": [
"10.1109/SLT.2018.8639652"
]
},
"num": null,
"urls": [],
"raw_text": "Izzeddin G\u00fcr, Dilek Hakkani-T\u00fcr, Gokhan T\u00fcr, and Pararth Shah. 2018. User modeling for task oriented dialogues. In 2018 IEEE Spoken Language Technol- ogy Workshop (SLT), pages 900-906. IEEE.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "The second dialog state tracking challenge",
"authors": [
{
"first": "Matthew",
"middle": [],
"last": "Henderson",
"suffix": ""
},
{
"first": "Blaise",
"middle": [],
"last": "Thomson",
"suffix": ""
},
{
"first": "Jason",
"middle": [
"D"
],
"last": "Williams",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)",
"volume": "",
"issue": "",
"pages": "263--272",
"other_ids": {
"DOI": [
"10.3115/v1/W14-4337"
]
},
"num": null,
"urls": [],
"raw_text": "Matthew Henderson, Blaise Thomson, and Jason D. Williams. 2014. The second dialog state tracking challenge. In Proceedings of the 15th Annual Meet- ing of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 263-272, Philadelphia, PA, U.S.A. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "SUMBT: Slot-utterance matching for universal and scalable belief tracking",
"authors": [
{
"first": "Hwaran",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Jinsik",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Tae-Yoon",
"middle": [],
"last": "Kim",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 57th",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.18653/v1/P19-1546"
]
},
"num": null,
"urls": [],
"raw_text": "Hwaran Lee, Jinsik Lee, and Tae-Yoon Kim. 2019a. SUMBT: Slot-utterance matching for universal and scalable belief tracking. In Proceedings of the 57th",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Annual Meeting of the Association for Computational Linguistics",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "5478--5483",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Annual Meeting of the Association for Computa- tional Linguistics, pages 5478-5483, Florence, Italy. Association for Computational Linguistics.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "ConvLab: Multi-domain end-to-end dialog system platform",
"authors": [
{
"first": "Sungjin",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Qi",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Ryuichi",
"middle": [],
"last": "Takanobu",
"suffix": ""
},
{
"first": "Zheng",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Yaoqin",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Xiang",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Jinchao",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Baolin",
"middle": [],
"last": "Peng",
"suffix": ""
},
{
"first": "Xiujun",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Minlie",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Jianfeng",
"middle": [],
"last": "Gao",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
"volume": "",
"issue": "",
"pages": "64--69",
"other_ids": {
"DOI": [
"10.18653/v1/P19-3011"
]
},
"num": null,
"urls": [],
"raw_text": "Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, and Jianfeng Gao. 2019b. ConvLab: Multi-domain end-to-end dialog system platform. In Proceedings of the 57th Annual Meet- ing of the Association for Computational Linguis- tics: System Demonstrations, pages 64-69, Flo- rence, Italy. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Sequicity: Simplifying task-oriented dialogue systems with single sequence-to-sequence architectures",
"authors": [
{
"first": "Wenqiang",
"middle": [],
"last": "Lei",
"suffix": ""
},
{
"first": "Xisen",
"middle": [],
"last": "Jin",
"suffix": ""
},
{
"first": "Min-Yen",
"middle": [],
"last": "Kan",
"suffix": ""
},
{
"first": "Zhaochun",
"middle": [],
"last": "Ren",
"suffix": ""
},
{
"first": "Xiangnan",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Dawei",
"middle": [],
"last": "Yin",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "1437--1447",
"other_ids": {
"DOI": [
"10.18653/v1/P18-1133"
]
},
"num": null,
"urls": [],
"raw_text": "Wenqiang Lei, Xisen Jin, Min-Yen Kan, Zhaochun Ren, Xiangnan He, and Dawei Yin. 2018. Sequicity: Simplifying task-oriented dialogue systems with sin- gle sequence-to-sequence architectures. In Proceed- ings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa- pers), pages 1437-1447, Melbourne, Australia. As- sociation for Computational Linguistics.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Deal or no deal? end-toend learning of negotiation dialogues",
"authors": [
{
"first": "Mike",
"middle": [],
"last": "Lewis",
"suffix": ""
},
{
"first": "Denis",
"middle": [],
"last": "Yarats",
"suffix": ""
},
{
"first": "Yann",
"middle": [],
"last": "Dauphin",
"suffix": ""
},
{
"first": "Devi",
"middle": [],
"last": "Parikh",
"suffix": ""
},
{
"first": "Dhruv",
"middle": [],
"last": "Batra",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2443--2453",
"other_ids": {
"DOI": [
"10.18653/v1/D17-1259"
]
},
"num": null,
"urls": [],
"raw_text": "Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, and Dhruv Batra. 2017. Deal or no deal? end-to- end learning of negotiation dialogues. In Proceed- ings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2443-2453, Copenhagen, Denmark. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Spoken language understanding from unaligned data using discriminative classification models",
"authors": [
{
"first": "F",
"middle": [],
"last": "Mairesse",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Gasic",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Jurcicek",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Keizer",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Thomson",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Young",
"suffix": ""
}
],
"year": 2009,
"venue": "IEEE International Conference on Acoustics, Speech and Signal Processing",
"volume": "",
"issue": "",
"pages": "4749--4752",
"other_ids": {
"DOI": [
"10.1109/ICASSP.2009.4960692"
]
},
"num": null,
"urls": [],
"raw_text": "F. Mairesse, M. Gasic, F. Jurcicek, S. Keizer, B. Thom- son, K. Yu, and S. Young. 2009. Spoken language understanding from unaligned data using discrimi- native classification models. In 2009 IEEE Interna- tional Conference on Acoustics, Speech and Signal Processing, pages 4749-4752.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "ParlAI: A dialog research software platform",
"authors": [
{
"first": "Alexander",
"middle": [],
"last": "Miller",
"suffix": ""
},
{
"first": "Will",
"middle": [],
"last": "Feng",
"suffix": ""
},
{
"first": "Dhruv",
"middle": [],
"last": "Batra",
"suffix": ""
},
{
"first": "Antoine",
"middle": [],
"last": "Bordes",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Fisch",
"suffix": ""
},
{
"first": "Jiasen",
"middle": [],
"last": "Lu",
"suffix": ""
},
{
"first": "Devi",
"middle": [],
"last": "Parikh",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
"volume": "",
"issue": "",
"pages": "79--84",
"other_ids": {
"DOI": [
"10.18653/v1/D17-2014"
]
},
"num": null,
"urls": [],
"raw_text": "Alexander Miller, Will Feng, Dhruv Batra, Antoine Bordes, Adam Fisch, Jiasen Lu, Devi Parikh, and Jason Weston. 2017. ParlAI: A dialog research soft- ware platform. In Proceedings of the 2017 Con- ference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 79-84, Copenhagen, Denmark. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Plato dialogue system: A flexible conversational ai research platform",
"authors": [
{
"first": "Alexandros",
"middle": [],
"last": "Papangelis",
"suffix": ""
},
{
"first": "Mahdi",
"middle": [],
"last": "Namazifar",
"suffix": ""
},
{
"first": "Chandra",
"middle": [],
"last": "Khatri",
"suffix": ""
},
{
"first": "Yi-Chia",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Piero",
"middle": [],
"last": "Molino",
"suffix": ""
},
{
"first": "Gokhan",
"middle": [],
"last": "Tur",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alexandros Papangelis, Mahdi Namazifar, Chandra Khatri, Yi-Chia Wang, Piero Molino, and Gokhan Tur. 2020. Plato dialogue system: A flexible conver- sational ai research platform.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Composite task-completion dialogue policy learning via hierarchical deep reinforcement learning",
"authors": [
{
"first": "Baolin",
"middle": [],
"last": "Peng",
"suffix": ""
},
{
"first": "Xiujun",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Lihong",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Jianfeng",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Asli",
"middle": [],
"last": "Celikyilmaz",
"suffix": ""
},
{
"first": "Sungjin",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Kam-Fai",
"middle": [],
"last": "Wong",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2231--2240",
"other_ids": {
"DOI": [
"10.18653/v1/D17-1237"
]
},
"num": null,
"urls": [],
"raw_text": "Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee, and Kam-Fai Wong. 2017. Composite task-completion dialogue policy learning via hierarchical deep reinforcement learn- ing. In Proceedings of the 2017 Conference on Em- pirical Methods in Natural Language Processing, pages 2231-2240, Copenhagen, Denmark. Associa- tion for Computational Linguistics.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Large-scale multi-domain belief tracking with knowledge sharing",
"authors": [
{
"first": "Pawe\u0142",
"middle": [],
"last": "Osman Ramadan",
"suffix": ""
},
{
"first": "Milica",
"middle": [],
"last": "Budzianowski",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ga\u0161i\u0107",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics",
"volume": "2",
"issue": "",
"pages": "432--437",
"other_ids": {
"DOI": [
"10.18653/v1/P18-2069"
]
},
"num": null,
"urls": [],
"raw_text": "Osman Ramadan, Pawe\u0142 Budzianowski, and Milica Ga\u0161i\u0107. 2018. Large-scale multi-domain belief track- ing with knowledge sharing. In Proceedings of the 56th Annual Meeting of the Association for Com- putational Linguistics (Volume 2: Short Papers), pages 432-437, Melbourne, Australia. Association for Computational Linguistics.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset",
"authors": [
{
"first": "Abhinav",
"middle": [],
"last": "Rastogi",
"suffix": ""
},
{
"first": "Xiaoxue",
"middle": [],
"last": "Zang",
"suffix": ""
},
{
"first": "Srinivas",
"middle": [],
"last": "Sunkara",
"suffix": ""
},
{
"first": "Raghav",
"middle": [],
"last": "Gupta",
"suffix": ""
},
{
"first": "Pranav",
"middle": [],
"last": "Khaitan",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1909.05855"
]
},
"num": null,
"urls": [],
"raw_text": "Abhinav Rastogi, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, and Pranav Khaitan. 2019. Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset. arXiv preprint arXiv:1909.05855.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Agenda-based user simulation for bootstrapping a POMDP dialogue system",
"authors": [
{
"first": "Jost",
"middle": [],
"last": "Schatzmann",
"suffix": ""
},
{
"first": "Blaise",
"middle": [],
"last": "Thomson",
"suffix": ""
},
{
"first": "Karl",
"middle": [],
"last": "Weilhammer",
"suffix": ""
},
{
"first": "Hui",
"middle": [],
"last": "Ye",
"suffix": ""
},
{
"first": "Steve",
"middle": [],
"last": "Young",
"suffix": ""
}
],
"year": 2007,
"venue": "Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers",
"volume": "",
"issue": "",
"pages": "149--152",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jost Schatzmann, Blaise Thomson, Karl Weilhammer, Hui Ye, and Steve Young. 2007. Agenda-based user simulation for bootstrapping a POMDP dia- logue system. In Human Language Technologies 2007: The Conference of the North American Chap- ter of the Association for Computational Linguis- tics; Companion Volume, Short Papers, pages 149- 152, Rochester, New York. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Proximal policy optimization algorithms",
"authors": [
{
"first": "John",
"middle": [],
"last": "Schulman",
"suffix": ""
},
{
"first": "Filip",
"middle": [],
"last": "Wolski",
"suffix": ""
},
{
"first": "Prafulla",
"middle": [],
"last": "Dhariwal",
"suffix": ""
},
{
"first": "Alec",
"middle": [],
"last": "Radford",
"suffix": ""
},
{
"first": "Oleg",
"middle": [],
"last": "Klimov",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1707.06347"
]
},
"num": null,
"urls": [],
"raw_text": "John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Guided dialog policy learning: Reward estimation for multi-domain task-oriented dialog",
"authors": [
{
"first": "Ryuichi",
"middle": [],
"last": "Takanobu",
"suffix": ""
},
{
"first": "Hanlin",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Minlie",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
"volume": "",
"issue": "",
"pages": "100--110",
"other_ids": {
"DOI": [
"10.18653/v1/D19-1010"
]
},
"num": null,
"urls": [],
"raw_text": "Ryuichi Takanobu, Hanlin Zhu, and Minlie Huang. 2019. Guided dialog policy learning: Reward es- timation for multi-domain task-oriented dialog. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Lan- guage Processing (EMNLP-IJCNLP), pages 100- 110, Hong Kong, China. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "PyDial: A multi-domain statistical dialogue system toolkit",
"authors": [
{
"first": "Stefan",
"middle": [],
"last": "Ultes",
"suffix": ""
},
{
"first": "Lina",
"middle": [
"M"
],
"last": "Rojas-Barahona",
"suffix": ""
},
{
"first": "Pei-Hao",
"middle": [],
"last": "Su",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Vandyke",
"suffix": ""
},
{
"first": "Dongho",
"middle": [],
"last": "Kim",
"suffix": ""
},
{
"first": "I\u00f1igo",
"middle": [],
"last": "Casanueva",
"suffix": ""
},
{
"first": "Pawe\u0142",
"middle": [],
"last": "Budzianowski",
"suffix": ""
},
{
"first": "Nikola",
"middle": [],
"last": "Mrk\u0161i\u0107",
"suffix": ""
},
{
"first": "Tsung-Hsien",
"middle": [],
"last": "Wen",
"suffix": ""
},
{
"first": "Milica",
"middle": [],
"last": "Ga\u0161i\u0107",
"suffix": ""
},
{
"first": "Steve",
"middle": [],
"last": "Young",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of ACL 2017, System Demonstrations",
"volume": "",
"issue": "",
"pages": "73--78",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Stefan Ultes, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke, Dongho Kim, I\u00f1igo Casanueva, Pawe\u0142 Budzianowski, Nikola Mrk\u0161i\u0107, Tsung-Hsien Wen, Milica Ga\u0161i\u0107, and Steve Young. 2017. PyDial: A multi-domain statistical dialogue system toolkit. In Proceedings of ACL 2017, System Demonstra- tions, pages 73-78, Vancouver, Canada. Association for Computational Linguistics.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Semantically conditioned LSTM-based natural language generation for spoken dialogue systems",
"authors": [
{
"first": "Milica",
"middle": [],
"last": "Tsung-Hsien Wen",
"suffix": ""
},
{
"first": "Nikola",
"middle": [],
"last": "Ga\u0161i\u0107",
"suffix": ""
},
{
"first": "Pei-Hao",
"middle": [],
"last": "Mrk\u0161i\u0107",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Su",
"suffix": ""
},
{
"first": "Steve",
"middle": [],
"last": "Vandyke",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Young",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1711--1721",
"other_ids": {
"DOI": [
"10.18653/v1/D15-1199"
]
},
"num": null,
"urls": [],
"raw_text": "Tsung-Hsien Wen, Milica Ga\u0161i\u0107, Nikola Mrk\u0161i\u0107, Pei- Hao Su, David Vandyke, and Steve Young. 2015. Semantically conditioned LSTM-based natural lan- guage generation for spoken dialogue systems. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1711-1721, Lisbon, Portugal. Association for Com- putational Linguistics.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "A networkbased end-to-end trainable task-oriented dialogue system",
"authors": [
{
"first": "David",
"middle": [],
"last": "Tsung-Hsien Wen",
"suffix": ""
},
{
"first": "Nikola",
"middle": [],
"last": "Vandyke",
"suffix": ""
},
{
"first": "Milica",
"middle": [],
"last": "Mrk\u0161i\u0107",
"suffix": ""
},
{
"first": "Lina",
"middle": [
"M"
],
"last": "Ga\u0161i\u0107",
"suffix": ""
},
{
"first": "Pei-Hao",
"middle": [],
"last": "Rojas-Barahona",
"suffix": ""
},
{
"first": "Stefan",
"middle": [],
"last": "Su",
"suffix": ""
},
{
"first": "Steve",
"middle": [],
"last": "Ultes",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Young",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "438--449",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tsung-Hsien Wen, David Vandyke, Nikola Mrk\u0161i\u0107, Milica Ga\u0161i\u0107, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes, and Steve Young. 2017. A network- based end-to-end trainable task-oriented dialogue system. In Proceedings of the 15th Conference of the European Chapter of the Association for Compu- tational Linguistics: Volume 1, Long Papers, pages 438-449, Valencia, Spain. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Simple statistical gradientfollowing algorithms for connectionist reinforcement learning",
"authors": [
{
"first": "Ronald",
"middle": [
"J"
],
"last": "Williams",
"suffix": ""
}
],
"year": 1992,
"venue": "Machine Learning",
"volume": "8",
"issue": "",
"pages": "229--256",
"other_ids": {
"DOI": [
"10.1007/BF00992696"
]
},
"num": null,
"urls": [],
"raw_text": "Ronald J. Williams. 1992. Simple statistical gradient- following algorithms for connectionist reinforce- ment learning. Machine Learning, 8(3):229-256.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Transferable multi-domain state generator for task-oriented dialogue systems",
"authors": [
{
"first": "Chien-Sheng",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Andrea",
"middle": [],
"last": "Madotto",
"suffix": ""
},
{
"first": "Ehsan",
"middle": [],
"last": "Hosseini-Asl",
"suffix": ""
},
{
"first": "Caiming",
"middle": [],
"last": "Xiong",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Socher",
"suffix": ""
},
{
"first": "Pascale",
"middle": [],
"last": "Fung",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "808--819",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chien-Sheng Wu, Andrea Madotto, Ehsan Hosseini- Asl, Caiming Xiong, Richard Socher, and Pascale Fung. 2019. Transferable multi-domain state gener- ator for task-oriented dialogue systems. In Proceed- ings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 808-819, Flo- rence, Italy. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context",
"authors": [
{
"first": "Yichi",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Zhijian",
"middle": [],
"last": "Ou",
"suffix": ""
},
{
"first": "Zhou",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the AAAI Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yichi Zhang, Zhijian Ou, and Zhou Yu. 2019. Task- Oriented Dialog Systems that Consider Multiple Ap- propriate Responses under the Same Context. In Proceedings of the AAAI Conference on Artificial In- telligence.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Rethinking action spaces for reinforcement learning in end-to-end dialog agents with latent variable models",
"authors": [
{
"first": "Tiancheng",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Kaige",
"middle": [],
"last": "Xie",
"suffix": ""
},
{
"first": "Maxine",
"middle": [],
"last": "Eskenazi",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "1208--1218",
"other_ids": {
"DOI": [
"10.18653/v1/N19-1123"
]
},
"num": null,
"urls": [],
"raw_text": "Tiancheng Zhao, Kaige Xie, and Maxine Eskenazi. 2019. Rethinking action spaces for reinforcement learning in end-to-end dialog agents with latent vari- able models. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1208-1218, Minneapolis, Minnesota. Associ- ation for Computational Linguistics.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "CrossWOZ: A large-scale chinese cross-domain task-oriented dialogue dataset",
"authors": [
{
"first": "Qi",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Kaili",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Zheng",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Xiaoyan",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Minlie",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2020,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang. 2020. CrossWOZ: A large-scale chinese cross-domain task-oriented dialogue dataset. Transactions of the Association for Computational Linguistics.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"text": "Performance of the demo system in Section 3. Left: Success rate and inform F1 for each domain. Right: Proportions of the dialogue loop in each domain.Overall results: Success Rate: 60.8%; inform F1: 44.5%Most confusing user dialogue acts: Request-Hotel-Post-?-34%: Request-Hospital-Post-? -32%: Request-Attraction-Post-",
"num": null,
"uris": null
},
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"text": "The interface of the Interactive Tool.",
"num": null,
"uris": null
},
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