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| "paper_id": "2021", |
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| "date_generated": "2023-01-19T13:27:06.417825Z" |
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| "title": "Discourse-Driven Integrated Dialogue Development Environment for Open-Domain Dialogue Systems", |
| "authors": [ |
| { |
| "first": "Denis", |
| "middle": [], |
| "last": "Kuznetsov", |
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| "institution": "Moscow \u2021 AIR Institute", |
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| "email": "kuznetsov.dp@phystech.edu" |
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| { |
| "first": "Dmitry", |
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| "last": "Evseev", |
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| "email": "dmitrii.evseev@phystech.edu" |
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| { |
| "first": "Lidiya", |
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| "last": "Ostyakova", |
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| "institution": "Moscow \u2021 AIR Institute", |
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| "email": "l.ostyakova@yandex.ru" |
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| "first": "Oleg", |
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| "last": "Serikov", |
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| { |
| "first": "Daniel", |
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| "last": "Kornev", |
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| "email": "danielko@deeppavlov.ai" |
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| { |
| "first": "Mikhail", |
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| "last": "Burtsev", |
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| "abstract": "Development environments for spoken dialogue systems are popular today because they enable rapid creation of the dialogue systems in times when usage of the voice AI Assistants is constantly growing. We describe a graphical Discourse-Driven Integrated Dialogue Development Environment (DD-IDDE) for spoken open-domain dialogue systems. The DD-IDDE allows dialogue architects to interactively define dialogue flows of their skills/chatbots with the aid of the discourse-driven recommendation system, enhance these flows in the Python-based DSL, deploy, and then further improve based on the skills/chatbots usage statistics. We show how these skills/chatbots can be specified through a graphical user interface within the VS Code Extension, and then run on top of the Dialog Flow Framework (DFF). An earlier version of this framework has been adopted in one of the Alexa Prize 4 socialbots while the updated version was specifically designed to power the described DD-IDDE solution.", |
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| "text": "Development environments for spoken dialogue systems are popular today because they enable rapid creation of the dialogue systems in times when usage of the voice AI Assistants is constantly growing. We describe a graphical Discourse-Driven Integrated Dialogue Development Environment (DD-IDDE) for spoken open-domain dialogue systems. The DD-IDDE allows dialogue architects to interactively define dialogue flows of their skills/chatbots with the aid of the discourse-driven recommendation system, enhance these flows in the Python-based DSL, deploy, and then further improve based on the skills/chatbots usage statistics. We show how these skills/chatbots can be specified through a graphical user interface within the VS Code Extension, and then run on top of the Dialog Flow Framework (DFF). An earlier version of this framework has been adopted in one of the Alexa Prize 4 socialbots while the updated version was specifically designed to power the described DD-IDDE solution.", |
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| "text": "Conversational AI focused on the development of the dialogue systems is one of the most challenging areas in the artificial intelligence given the subjectivity of the human language interpretation, and is gaining more and more popularity across both academia (Viewer, 2021) and consumer & enterprise markets (Statista, 2020) .", |
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| "text": "Dialogue systems are classified into taskoriented dialogue systems and chat (or non-taskoriented) dialogue systems. Usually they are studied differently, but combining them has been proposed (Lee et al., 2006) , and has been found effective in improving user impressions and the relationships with users (Lucas et al., 2018) . While taskoriented systems are more widespread and used in different business scenarios (Bocklisch et al., 2017) , building systems that can carry multi-turn open-domain conversations is still far from a solved problem (Hu et al., 2021) .", |
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| "text": "While there is a significant effort in using large generative models (Brown et al., 2020) , (Roller et al., 2020) in the open-domain dialogue systems, recent tests of GPT-3 (Marcus and Davis, 2020) , and Conversational AI challenges like Alexa Prize Socialbot Grand Challenge (Ram et al., 2018) highlight the inherent weaknesses of these models, namely inability to maintain context, avoid contradictions, and control the dialogue strategically in the long dialogues. These fallacies experienced by us and other teams during the Amazon Alexa Prize 3 (Gabriel et al., 2020) led us to the importance of continuing research towards the tightly-controlled open-domain dialogue management frameworks like E-STDM (Finch and Choi, 2020) .", |
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| "text": "During the Amazon Alexa Prize 4 (Hu et al., 2021) competition we developed (Baymurzina et al., 2021) a first iteration of such framework called Dialog Flow Framework (DFF) (Kuznetsov and Evseev, 2021) to aid in rapid development of open-domain \"skills\" covering various societal topics in our DREAM Socialbot. Usage showed that while users enjoy controlled conversation in their favorite domains, they often interrupt the conversation with the out-of-domain (Konr\u00e1d et al., 2021) responses leading it into the non-anticipated directions.", |
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| "text": "Feedback of our Alexa Prize 4 team members who wrote DFF-based open-domain \"skills\" shows that predictions of the possible user response could significantly cut the time needed by the developer to design and develop these skills.", |
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| "text": "The key insight into the conversation is that each utterance in the dialogue is also a kind of action being performed by the speaker (Austin, 1962) . These actions are called speech or dialog acts (Jurafsky and Martin, 2009 ). Yet while dialog acts used by some of the Alexa Prize teams e.g., MIDAS (Yu et al., 2019) , are quite sophisticated in understand-ing higher level user intents conceptually similar to those used in task-oriented systems (Bocklisch et al., 2017) , they are single turn-based and don't support the development of the open-domain conversations that are interactional and sequential (Eggins and Slade, 1996) . Indeed, these dialog act taxonomies lack connection to the discourse level of the casual conversation analysis as shown by (Halliday and M., 2004) .", |
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| "text": "In the effort of making it easier for developers to design and develop sequential and interactional multi-turn open-domain dialogue \"skills\" we propose a new graphical Discourse-Driven Integrated Dialogue Development Environment (DD-IDDE) that combines a novel Discourse-Driven Recommendation System and a DSL for the Dialog Flow Framework, encapsulated together into an extension to the popular IDE Visual Studio Code.", |
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| "text": "One of the first Development Environments designed for spoken language systems, (Denecke, 2000) , was created in 2000. While it supported an entire dialogue system development lifecycle, it lacked the power of the modern NLP components such as NER, Entity Linking, etc. as well as the means for the development of the open-dialogue skills/chatbots. Today, there is a variety of dialogue development frameworks that have emerged to aid in rapid dialogue system creation. Rule-based approaches used e.g. in (Bocklisch et al., 2017) are common in the task-oriented systems broadly used in businesses. Other systems incorporate support for full development lifecycle of the dialogue systems starting with individual NLP ML models and ending with the complete chatbots (Burtsev et al., 2018) . Some of these systems are also encapsulated in the form of web-based IDEs e.g. (Lawrence et al., 2021) .", |
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| "text": "Domain-specific languages (DSLs) are typically used in dialogue systems to aid in rapid layout of the dialogue logic. One of the oldest DSLs in this space, AIML (Wallace, 2001 ) was created as an XML dialect that supports pattern matching of the user utterance and template-based entity extraction and was used in development of the famous opendomain dialogue system \"A.L.I.C.E.\" (Wallace, 2009) . RASA DSL (Bocklisch et al., 2017) was designed for the development of the task-oriented systems and includes NLU (intent detection and entity extraction) and stories (actions for intents) components. JAICF (AI, 2021), a JavaScript-based state machine-based DSL, was designed to support both task-oriented systems and chatbots.", |
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| "text": "A winner of Alexa Prize 2, Gunrock team enhanced their socialbot's (Yu et al., 2019) internal Dialog Flow framework with the MIDAS dialog act scheme to create an adaptive and unique conversation experience with the user. However it does not enable bot developers with the mechanisms to manage dialogue strategically across multiple conversation turns. Slugbot (Bowden et al., 2019) , another Alexa Prize team, proposed a DRDM dialogue model to control the coherence of the open-domain dialogue using discourse relations. Their approach introduced a combination of dialog acts and four discourse relations from the PDTB 2.0 (Prasad et al., 2008 ) as means to model interaction within individual turns and at a higher level. Nonetheless, PDTB 2.0 is based on the 1-million-word Wall Street Journal corpus which is a written language and is not best suited for the spoken casual conversation analysis.", |
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| "text": "DD-IDDE is powered by the Dialog Flow Framework (DFF) derived from the E-STDM framework which is conceptually similar to both Dialog Flow framework described in (Yu et al., 2019) and the DRDM-dialogue model. It's newly developed DSL was specifically created so that: (1) all conditions, responses and even dialogue flows could be implemented as Python functions, and (2) conditions for state transition could use any of the available annotators like Speech Function Classifier and Predictor and others within the larger DREAM socialbot or derived chatbots and assistants. However, while it is relatively common for dialogue flow frameworks to have a built-in DSL and support pluggable NLU components, DD-IDDE specifically incorporates the support for the novel Discourse-Driven Recommendation System as well as the visual tools aiding in designing open-domain dialogues across multiple conversation steps.", |
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| "text": "Development of the discourse-driven open-domain dialogue \"skills\" and chatbots is an ongoing iterative cycle that includes: (1) design of the \"skill\"/chatbot as the sequence of conditional transitions between states, aided by the discourse moves recommendations, (2) development of the \"skill\"/chatbot, (3) deployment, ending with the (4) analysis of the running solution, followed by the restart of the cycle.", |
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| "text": "Flows, Nodes, Transitions, Conditions and Responses: We split the dialogue graph into the logically related parts called Flows to make it more convenient to work with. Each Flow represents a dialogue subgraph. Typically, flows can be organized to cover different topics in the dialogue graph. Like in (Finch and Choi, 2020) , we identify the states of the dialogue at a certain moment, such states correspond to the Nodes of the dialogue graph. Being in a certain node of the graph, we can go to an another node if the Condition that corresponds to this Transition is true. After each transition, the Responses corresponding to the node are returned.", |
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| "text": "While other solutions often incorporate dialogue/speech acts (DAMSL-SWBD, ISO standard, HCRC etc.) to recognize the higher-level user intentions in a particular context and describe discourse of conversations, they still lack the ability to represent conversational structure and make it impossible to analyze discourse at the dialogue level. In DD-IDDE, we propose implementation of more enhanced intents called Speech Functions that not only represent single turns but also reflect their role in the larger dialogue context.", |
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| "section": "Speech Functions and Discourse Moves:", |
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| "text": "Eggins and Slade in (Eggins and Slade, 1996) introduced an approach connecting dialogue turns and cross-turn discourse structure patterns specific for casual conversation as the higher-level abstraction. At turn level, they extended Halliday's concept of Speech Functions (Halliday and M., 2004) that express pragmatic goals of speakers and can be used as an enhanced alternative to Dialogue and Speech Acts. Advantages of Speech Functions are in specified grammatical criteria for Speech Function identification and the ability to constrain a comprehensive systematic discourse model of dialogues with the help of them.", |
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| "text": "These Speech Functions express different discourse purposes and can compound patterns that are expressed by so-called moves. There are three types of Discourse Moves as defined by Eggins and Slade: (i) Opening moves are used to start a discussion of a new topic in the dialogue. (ii) Sustaining moves develop the current topic of conversation. (iii) Reacting moves denote the transfer of the role of the current speaker. The first subtype of reacting moves are responses (React.Response), which lead dialogues or top-ics of conversation to completion, the second one is rejoinders (React.Rejoinder), which, on the contrary, prolong the conversation.", |
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| "text": "To enable users design dialogue flows with the aid of the Speech Functions and Discourse Moves, we built Speech Function Classifier (4.2) and Speech Function Predictor (4.3).", |
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| "text": "DFF DSL: DFF DSL is Python-based language for definition of a dialogue graph, conditions of transitions between nodes (e.g. checks for Speech Functions), and functions for processing user or dialogue system utterances (e.g. entity extraction). All the elements in DFF DSL can be callable (functions), including dialogue graphs, which makes the DSL flexible and extensible.", |
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| "text": "DFF Extensions: DFF DSL extensions are modules which can be used in the dialog flow for NLU or NLG. NLU extensions include Speech Function Classifier, Speech Function Predictor and Entity Extraction Module. DFF DSL extensions for NLG are Slot Filling Module (fills the dialogue system's response template with extracted entities), Factoid Response Generation Module (provides content of pages from text databases) and Generic Responses Module (returns short utterances such as \"Yes\" or \"No\", which represent appropriate Speech Function responses to user utterance's Speech Function).", |
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| "text": "We use Dialog Flow Framework (DFF) as a core of the DD-IDDE. Creating conversational agents with the support of the open-domain skills is challenging as was shown, for example, in (Kuratov et al., 2019) . Currently, there are many ready-made solutions (Finch and Choi, 2020) , (Lison and Kennington, 2016) , (Ultes et al., 2017) , (Bocklisch et al., 2017) .", |
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| "text": "The alpha version of DFF was developed during the Alexa Prize 4 Challenge, based on E-STDM (Finch and Choi, 2020) . After the Challenge, all the shortcomings formulated during the competition were taken into account and the DFF DSL was rewritten. DFF (Kuznetsov and Evseev, 2021) is flexible, expressive, minimal, easy to expand through ready-made extensions or own extensions, as well as collect statistics with subsequent analysis.", |
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| "text": "DFF has an expressive Python-based DSL called DFF DSL specially designed to be a minimalistic and extensible scripting markup language when compared to other solutions (e.g., AIML, RiveScript, ChatScript, botml), which supports using specific extensions (e.g., discourse-driven integrated dialogue development, a mechanism to extract and use entities based on their Wikidata, generic responses based on the speech functions ontology and etc).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Core", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "There is a set of community-created extentions that are well documented, and it is straightforward to add own extentions to the framework.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Core", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "To aid in the development of the Speech Function Classifier, we picked the Santa Barbara Corpus of Spoken American English, which consists of 60 transcriptions of the naturally-occurring spoken conversations. Three face-to-face dialogues were preprocessed and then labeled with the Speech Functions into a small dataset including about 1700 manually annotated utterances. Two annotators reached an inter-annotator agreement of \u03ba = 0.71 on 1200 utterances which is considered to be a good result. By limiting taxonomy from 45 to 33 classes, using a hierarchical algorithm based on several Logistic Regression models with different parameters, and a rule-based approach, the last version of Speech Function Classifier achieved an F1-score varying from 52% to 71% depending on the distribution of the Speech Functions in a particular dialogue. The resulting Speech Function Classifier annotator classifies each phrase in the user's utterance as well as each dialogue system's response candidate with Speech Functions. This classification enables the dialogue system to predict the dialogue system's response Speech Functions most expected by the user.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Speech Function Classifier", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "Speech Function Predictor yields probabilities of speech functions that can follow a speech function predicted by Speech Function Classifier. Speech Function Predictor is a model based on the statistics of speech function sequences in the manually annotated data. As the collected data is not fully representative, rules were added covering missing cases of speech functions sequences. Speech Func-tion Predictor annotation to each last phrase of the user's utterances serves as a recommendation for a dialogue system's next response. It is deployed as the HTTP endpoint available for the VS Code extension as the Recommendation API.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Speech Function Predictor", |
| "sec_num": "4.3" |
| }, |
| { |
| "text": "The recommendation system uses Speech Functions used to classify each bot utterance as an input for the Speech Function Predictor to suggest the possible kinds of user responses. The same mechanism is then used to design next bot responses based on the user responses, enabling the rapid design of the sequential discourse-driven opendomain dialogue system.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Discourse-Driven Recommendation System", |
| "sec_num": "4.4" |
| }, |
| { |
| "text": "The DD-IDDE offers a split GUI (Serikov and Babadeev, 2021) that offers both visual design of the current open-domain skill's/chatbot's dialogue flows and a mechanism for working with the codebehind that represents the logic of the aforementioned skill/chatbot. It also provides a mechanism to obtain recommendations for the discourse moves for the selected node(s). Visual dialogue graph editor allows users to sketch DFF scenarios in form of the dialogue graph. Here, the nodes of the graph are System States, the transitions are allowed by users' utterances (see figure 4) . It is powered by the modified Draw.IO (JGraph Ltd, 2021), (Dieterichs and Rouill\u00e9, 2021) editor interface.", |
| "cite_spans": [ |
| { |
| "start": 636, |
| "end": 666, |
| "text": "(Dieterichs and Rouill\u00e9, 2021)", |
| "ref_id": "BIBREF8" |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 566, |
| "end": 575, |
| "text": "figure 4)", |
| "ref_id": "FIGREF2" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Graphical User Interface", |
| "sec_num": "4.5" |
| }, |
| { |
| "text": "Code behind editor is a Python editor, and it shows DFF DSL produced from the dialogue flows designed in the visual editor. It can be extended with the standard Pylint-based recommendations and aids user in fixing errors prior to launching the target skill/chatbot. This GUI is provided in the form of the VS Code Extension.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Graphical User Interface", |
| "sec_num": "4.5" |
| }, |
| { |
| "text": "The DD-IDDE enables users to further improve their skills/chatbots based on the analysis of the user behavior. A DFF Extension was made that allows collecting the particular dialogue flows and individual nodes usage statistics at run-time as well as showing statistics charts in a dashboard.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conversational Analysis", |
| "sec_num": "4.6" |
| }, |
| { |
| "text": "The aforementioned DFF Extension for Conversation Analysis enables DFF open-domain \"skill\" developers to use the collected statistics of frequencies of transitions between nodes to focus developer's attention on the user behavior. Developing With and Without Discourse-Driven Recommendations: Development of DFF DSL-based open-domain \"skill\" is a multi-step cycle that includes initial skill design, development, test, deployment, feedback gathering, until skill quality is improved. The most challenging parts of the skill design are: (1) prediction of the out-of-domain responses that can be not so obvious for developers, and (2) sequential dialogue design. Our initial experiments with the DD-IDDE and its Discourse Moves recommendations showed that time required to address both the out-of-domain responses and to design sequential dialogue was significantly cut compared to the pure DFF DSL skills design.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "General Statistical Recommendation System", |
| "sec_num": "4.7" |
| }, |
| { |
| "text": "In this paper, we introduced a new Discourse-Driven Integrated Dialogue Development Environment (DD-IDDE) focused on aiding users in building open-domain skills/chatbots based on the discourse-driven recommendations. Our DD-IDDE makes it possible for novice users to rapidly design their scenario-driven skills in the visual editor with the discourse move recommendations, and then expand their skills with the custom Python code. In the future versions we plan to further expand the DD-IDDE to enable seamless transitions between the manually edited DFF DSL and its visual graph representation, add deeper integration of the statistics into the main interface of the VS Code extension, as well as add other extentions to the underlying DFF framework.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions and Future Directions", |
| "sec_num": null |
| }, |
| { |
| "text": "Having installed the DD-IDDE VS Code Extension 1 , user creates a new .drawio file in the common/dff_markup_scenarios with the name of the target skill scenario, e.g., pet_scenario.drawio -see 1.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A.1 Designing", |
| "sec_num": null |
| }, |
| { |
| "text": "User then proceeds with defining the Speech Function of the starting node by double-clicking on it -see 2.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A.1 Designing", |
| "sec_num": null |
| }, |
| { |
| "text": "Once the first node has been set up, user can use the Speech Function Recommendations to pick the next discourse moves by clicking on the \"Show Suggestions\" menu item -see 3.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A.1 Designing", |
| "sec_num": null |
| }, |
| { |
| "text": "User then continues adding nodes based on their scenario, with the aid of the Speech Function recommendations -see 4.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A.1 Designing", |
| "sec_num": null |
| }, |
| { |
| "text": "Once the designed skill is sufficiently outlined, user proceeds with the use of the Show Markup functionality that generates the code based on the designed skill -see 5.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A.2 Developing", |
| "sec_num": null |
| }, |
| { |
| "text": "With the generated DFF DSL code, user can now add the additional code-behind functionality such as the use of the regular expressions, custom intents, and the like -see 6.", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "A.2 Developing", |
| "sec_num": null |
| }, |
| { |
| "text": "In the Conversational Analysis dashboard user can observe the frequency of nodes' visits of the individual dialogue flows, rates of transitions within dialogues, as well as the frequency of the user responses. User can also collect statistics of annotations of user and bot utterances, custom data coming from other DFF extentions, e.g., classification of user utterances with the speech functions, user and bot emotions etc. The dashboard is described in more detail in appendix D. The dashboard allows users to select as many dialogues as one dialog and show user behavior on it. On this dialog graph is shown for one dialog 12 and on this one for many 8.", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A.3 Analyzing", |
| "sec_num": null |
| }, |
| { |
| "text": "Interface Illustrations VS Code Extension GUI is shown in figures 1, 2, 3, 4, 5, 6 DFF as well as many extentions for it are distributed with an open source license, like many other frameworks (e.g., Emora STDM, AIML, RiveScript, ChatScript, botml, OpenDial, PyDial). DFF is distributed with Apache 2.0 license. DFF DSL is a Python-based DSL for designing scenario-driven skills/chatbots. The main element of a skill in DFF DSL format is a dialogflow (a set of nodes and transitions between them, which refers to a particular subtopic). A dialogflow is a Python dictionary where keys are node names and elements describe response in this node and transitions to other nodes in the same dialogflow or in other dialogflows (Fig. 13) . The main features of a dialogflow in DFF DSL format:", |
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| "ref_spans": [ |
| { |
| "start": 721, |
| "end": 730, |
| "text": "(Fig. 13)", |
| "ref_id": "FIGREF0" |
| } |
| ], |
| "eq_spans": [], |
| "section": "A.4 VS Code Extension Graphical User", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 a condition for transition between nodes can be one of predefined conditions in DFF DSL (for example, a regular expression or a function which checks the speech function in the user utterance, etc.)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A.4 VS Code Extension Graphical User", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 a response can be a string or a function", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A.4 VS Code Extension Graphical User", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 processing of user utterance and response with custom functions (which can be used, for example, for slot extraction and slot filling (Fig. 14) )", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 136, |
| "end": 145, |
| "text": "(Fig. 14)", |
| "ref_id": "FIGREF0" |
| } |
| ], |
| "eq_spans": [], |
| "section": "A.4 VS Code Extension Graphical User", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 extensions for using facts databases (for example, Wikipedia) for response generation (Fig. 16 ) or generic responses to user utterance speech function", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 88, |
| "end": 96, |
| "text": "(Fig. 16", |
| "ref_id": "FIGREF0" |
| } |
| ], |
| "eq_spans": [], |
| "section": "A.4 VS Code Extension Graphical User", |
| "sec_num": null |
| }, |
| { |
| "text": "The solution, like many others (e.g., AIML), is based on the state machine. However, unlike others, DFF aids in the development of the open-domain skills/chatbots with the help of the discourse moves recommendation making it easy to design these skills and chatbots. It also incorporates a mechanism to extract and use entities based on their Wikidata types, as well as to provide generic responses based on the speech functions ontology.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A.4 VS Code Extension Graphical User", |
| "sec_num": null |
| }, |
| { |
| "text": "DFF was designed to be extensible as a modular system. With the external extentions, DFF can be significantly expanded as in complex frameworks (Finch and Choi, 2020) , (Bocklisch et al., 2017) . There is a set of community-created extentions that are well documented, and it is straightforward to add own extentions to the framework. DFF DSL is automatically validated before starting the bot, making it easy to identify issues beforehand..", |
| "cite_spans": [ |
| { |
| "start": 144, |
| "end": 166, |
| "text": "(Finch and Choi, 2020)", |
| "ref_id": "BIBREF10" |
| }, |
| { |
| "start": 169, |
| "end": 193, |
| "text": "(Bocklisch et al., 2017)", |
| "ref_id": "BIBREF3" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A.4 VS Code Extension Graphical User", |
| "sec_num": null |
| }, |
| { |
| "text": "The target node for transition can be:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "B.2 Transitions", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 the node name from the current dialogflow;", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "B.2 Transitions", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 the name of another dialogflow and the node in this dialogflow;", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "B.2 Transitions", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 special keywords (\"forward\" -to the next node in the current dialogflow, \"back\" -to the previous node, \"repeat\" -to make cycle transition, \"previous\" -to the previous active node).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "B.2 Transitions", |
| "sec_num": null |
| }, |
| { |
| "text": "Transitions between nodes can be local (which can be used only in the dialogflow where they were defined) or global (can be used in any other dialogflow).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "B.2 Transitions", |
| "sec_num": null |
| }, |
| { |
| "text": "The approach of dividing DFF into modules allows users to incorporate both lightweight annotators, classifiers, and so on, as well as modules based on neural models that require a large amount of resources in their solutions.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C DFF: Extensions", |
| "sec_num": null |
| }, |
| { |
| "text": "In production ready systems, it is necessary to use keywords and a matching pattern for the agent to work, as well as the selection of entities, the presence of ontologies, the identification of intents, the classification of sentiments, and much more.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C DFF: Extensions", |
| "sec_num": null |
| }, |
| { |
| "text": "Generic responses are short utterances, generated by the bot in response to a particular speech function in user utterance. Generic responses can be useful if the user is proactive and responds with long and detailed utterances, the bot in this case will say somewhat like \"yes\", \"I agree\". Examples of generic responses:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.1 Generic Responses Module", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 \"No\" in response to \"React.Respond.Confront.Reply.Contradict\" and \"React.Respond.Reply.Contradict\" speech functions;", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.1 Generic Responses Module", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 \"I don't know.\" to \"React.Respond.Confront.Reply.Disawow\" and \"React.Respond.Reply.Disawow\";", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.1 Generic Responses Module", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 \"I don't agree with you\" to \"React.Respond.Confront.Reply.Disagree\" and \"React.Respond.Reply.Disagree\";", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.1 Generic Responses Module", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 \"Yes\" to \"React.Respond.Support.Reply.Affirm\", \"React.Respond.Support.Reply.Agree\" and \"React.Respond.Reply.Agree\".", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.1 Generic Responses Module", |
| "sec_num": null |
| }, |
| { |
| "text": "Generic responses are implemented as an extention for DFF DSL with one cycled node of dialogflow and a response function which extracts a speech function from the user utterance and generates a suitable generic response.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.1 Generic Responses Module", |
| "sec_num": null |
| }, |
| { |
| "text": "Entity Extraction Module is used to find entities of particular types in the user utterance and store them in shared memory. Entities can be extracted by:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.2 Other Extentions", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 Wikidata Entity Type: user can specify a Wikidata ID for the given entity type to find entities of this type in the given utterance, e.g. to find a city user should specify \"wiki:Q515\" (which corresponds to \"city\" in Wikipedia)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.2 Other Extentions", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 Entity Tag from the set of (\"PERSON\", \"LO-CATION\", \"ORGANIZATION\", etc.): user can specify one of these tags to find entities of this type, e.g., to find a location user should specify \"tag:LOCATION\" Slot Filling Module is used to fill extracted entities in the slots of the response.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.2 Other Extentions", |
| "sec_num": null |
| }, |
| { |
| "text": "Factoid Response Generation Module provides content of the page from the databases like Wikipedia, wikiHow, etc. mentioned as the argument of the extension function.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.2 Other Extentions", |
| "sec_num": null |
| }, |
| { |
| "text": "Generic Responses Module returns short utterances (for example, \"Yes\", \"No\", \"I agree\" etc.) that represent selected speech functions as appropriate response to the selected user utterance's speech functions.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "C.2 Other Extentions", |
| "sec_num": null |
| }, |
| { |
| "text": "The statistics DFF extention 2 comes with a dashboard that displays various statistics. Using them, users can evaluate the user's use of script branches. The dashboard is shown in figures 7, 8, 9, 10, 12 The abscissa axis shows the order of a step in the dialog, the ordinate axis shows the node in which the user is located for this step. ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "D DFF: Conversational Analysis Dashboard", |
| "sec_num": null |
| }, |
| { |
| "text": "https://github.com/deepmipt/vscode-dff/ releases/tag/0.2.1", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "https://github.com/kudep/dff-node-stats/", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "Authors are deeply grateful to the Alexa Prize organizers for their feedback and advice during the Alexa Prize Grand Challenge 4. Authors also thanks our contributor Dmitry Babadeev who became an owner of the VS Code extension encapsulating the DD-IDDE, and all members of Neural Networks and Deep Learning Lab for their support and advice during the project development. Oleg Serikov is partially supported by the framework of the HSE University Basic Research Program.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Acknowledgements", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "How to do things with words", |
| "authors": [ |
| { |
| "first": "Austin", |
| "middle": [], |
| "last": "John Langshaw", |
| "suffix": "" |
| } |
| ], |
| "year": 1962, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "John Langshaw Austin. 1962. How to do things with words. William James Lectures. Oxford University Press.", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
| "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 |
| }, |
| "BIBREF4": { |
| "ref_id": "b4", |
| "title": "Slugbot: Developing a computational model and framework of a novel dialogue genre", |
| "authors": [ |
| { |
| "first": "Kevin", |
| "middle": [ |
| "K" |
| ], |
| "last": "Bowden", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiaqi", |
| "middle": [], |
| "last": "Wu", |
| "suffix": "" |
| }, |
| { |
| "first": "Wen", |
| "middle": [], |
| "last": "Cui", |
| "suffix": "" |
| }, |
| { |
| "first": "Juraj", |
| "middle": [], |
| "last": "Juraska", |
| "suffix": "" |
| }, |
| { |
| "first": "Vrindavan", |
| "middle": [], |
| "last": "Harrison", |
| "suffix": "" |
| }, |
| { |
| "first": "Brian", |
| "middle": [], |
| "last": "Schwarzmann", |
| "suffix": "" |
| }, |
| { |
| "first": "Nick", |
| "middle": [], |
| "last": "Santer", |
| "suffix": "" |
| }, |
| { |
| "first": "Marilyn", |
| "middle": [ |
| "A" |
| ], |
| "last": "Walker", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Kevin K. Bowden, JiaQi Wu, Wen Cui, Juraj Juraska, Vrindavan Harrison, Brian Schwarzmann, Nick San- ter, and Marilyn A. Walker. 2019. Slugbot: Develop- ing a computational model and framework of a novel dialogue genre. CoRR, abs/1907.10658.", |
| "links": null |
| }, |
| "BIBREF6": { |
| "ref_id": "b6", |
| "title": "Deeppavlov: Open-source library for dialogue systems", |
| "authors": [ |
| { |
| "first": "Mikhail", |
| "middle": [], |
| "last": "Burtsev", |
| "suffix": "" |
| }, |
| { |
| "first": "Alexander", |
| "middle": [], |
| "last": "Seliverstov", |
| "suffix": "" |
| }, |
| { |
| "first": "Rafael", |
| "middle": [], |
| "last": "Airapetyan", |
| "suffix": "" |
| }, |
| { |
| "first": "Mikhail", |
| "middle": [], |
| "last": "Arkhipov", |
| "suffix": "" |
| }, |
| { |
| "first": "Dilyara", |
| "middle": [], |
| "last": "Baymurzina", |
| "suffix": "" |
| }, |
| { |
| "first": "Nickolay", |
| "middle": [], |
| "last": "Bushkov", |
| "suffix": "" |
| }, |
| { |
| "first": "Olga", |
| "middle": [], |
| "last": "Gureenkova", |
| "suffix": "" |
| }, |
| { |
| "first": "Taras", |
| "middle": [], |
| "last": "Khakhulin", |
| "suffix": "" |
| }, |
| { |
| "first": "Yuri", |
| "middle": [], |
| "last": "Kuratov", |
| "suffix": "" |
| }, |
| { |
| "first": "Denis", |
| "middle": [], |
| "last": "Kuznetsov", |
| "suffix": "" |
| } |
| ], |
| "year": 2018, |
| "venue": "Proceedings of ACL 2018, System Demonstrations", |
| "volume": "", |
| "issue": "", |
| "pages": "122--127", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Mikhail Burtsev, Alexander Seliverstov, Rafael Airapetyan, Mikhail Arkhipov, Dilyara Baymurz- ina, Nickolay Bushkov, Olga Gureenkova, Taras Khakhulin, Yuri Kuratov, Denis Kuznetsov, et al. 2018. Deeppavlov: Open-source library for dia- logue systems. In Proceedings of ACL 2018, System Demonstrations, pages 122-127.", |
| "links": null |
| }, |
| "BIBREF7": { |
| "ref_id": "b7", |
| "title": "An integrated development environment for spoken dialogue systems", |
| "authors": [ |
| { |
| "first": "Matthias", |
| "middle": [], |
| "last": "Denecke", |
| "suffix": "" |
| } |
| ], |
| "year": 2000, |
| "venue": "Proceedings of the COLING-2000 Workshop on Using Toolsets and Architectures To Build NLP Systems", |
| "volume": "", |
| "issue": "", |
| "pages": "51--60", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Matthias Denecke. 2000. An integrated development environment for spoken dialogue systems. In Pro- ceedings of the COLING-2000 Workshop on Using Toolsets and Architectures To Build NLP Systems, pages 51-60, Centre Universitaire, Luxembourg. In- ternational Committee on Computational Linguis- tics.", |
| "links": null |
| }, |
| "BIBREF8": { |
| "ref_id": "b8", |
| "title": "Draw.io vs code integration", |
| "authors": [ |
| { |
| "first": "Henning", |
| "middle": [], |
| "last": "Dieterichs", |
| "suffix": "" |
| }, |
| { |
| "first": "Vincent", |
| "middle": [], |
| "last": "Rouill\u00e9", |
| "suffix": "" |
| } |
| ], |
| "year": 2021, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Henning Dieterichs and Vincent Rouill\u00e9. 2021. Draw.io vs code integration. https: //github.com/hediet/vscode-drawio.", |
| "links": null |
| }, |
| "BIBREF9": { |
| "ref_id": "b9", |
| "title": "Analysing casual conversation", |
| "authors": [ |
| { |
| "first": "S", |
| "middle": [], |
| "last": "Eggins", |
| "suffix": "" |
| }, |
| { |
| "first": "D", |
| "middle": [], |
| "last": "Slade", |
| "suffix": "" |
| } |
| ], |
| "year": 1996, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "S. Eggins and D. Slade. 1996. Analysing casual con- versation.", |
| "links": null |
| }, |
| "BIBREF10": { |
| "ref_id": "b10", |
| "title": "Emora stdm: A versatile framework for innovative dialogue system development", |
| "authors": [ |
| { |
| "first": "James", |
| "middle": [ |
| "D" |
| ], |
| "last": "Finch", |
| "suffix": "" |
| }, |
| { |
| "first": "Jinho", |
| "middle": [ |
| "D" |
| ], |
| "last": "Choi", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "James D. Finch and Jinho D. Choi. 2020. Emora stdm: A versatile framework for innovative dialogue sys- tem development.", |
| "links": null |
| }, |
| "BIBREF11": { |
| "ref_id": "b11", |
| "title": "Arindam Mandal, and Dilek Z. Hakkani-T\u00fcr. 2020. Further advances in open domain dialog systems in the third alexa prize socialbot grand challenge", |
| "authors": [ |
| { |
| "first": "Raefer", |
| "middle": [], |
| "last": "Gabriel", |
| "suffix": "" |
| }, |
| { |
| "first": "Yang", |
| "middle": [], |
| "last": "Liu", |
| "suffix": "" |
| }, |
| { |
| "first": "Anna", |
| "middle": [], |
| "last": "Gottardi", |
| "suffix": "" |
| }, |
| { |
| "first": "Mihail", |
| "middle": [], |
| "last": "Eric", |
| "suffix": "" |
| }, |
| { |
| "first": "Anju", |
| "middle": [], |
| "last": "Khatri", |
| "suffix": "" |
| }, |
| { |
| "first": "Anjali", |
| "middle": [], |
| "last": "Chadha", |
| "suffix": "" |
| }, |
| { |
| "first": "Qinlang", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "" |
| }, |
| { |
| "first": "Behnam", |
| "middle": [], |
| "last": "Hedayatnia", |
| "suffix": "" |
| }, |
| { |
| "first": "Pankaj", |
| "middle": [], |
| "last": "Rajan", |
| "suffix": "" |
| }, |
| { |
| "first": "Ali", |
| "middle": [], |
| "last": "Binici", |
| "suffix": "" |
| }, |
| { |
| "first": "Shui", |
| "middle": [], |
| "last": "Hu", |
| "suffix": "" |
| }, |
| { |
| "first": "Karthik", |
| "middle": [], |
| "last": "Gopalakrishnan", |
| "suffix": "" |
| }, |
| { |
| "first": "Seokhwan", |
| "middle": [], |
| "last": "Kim", |
| "suffix": "" |
| } |
| ], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Raefer Gabriel, Yang Liu, Anna Gottardi, Mihail Eric, Anju Khatri, Anjali Chadha, Qinlang Chen, Behnam Hedayatnia, Pankaj Rajan, Ali Binici, Shui Hu, Karthik Gopalakrishnan, Seokhwan Kim, Lauren Stubel, Kate Bland, Arindam Mandal, and Dilek Z. Hakkani-T\u00fcr. 2020. Further advances in open do- main dialog systems in the third alexa prize socialbot grand challenge.", |
| "links": null |
| }, |
| "BIBREF12": { |
| "ref_id": "b12", |
| "title": "An introduction to functional grammar", |
| "authors": [ |
| { |
| "first": "M", |
| "middle": [ |
| "A K" |
| ], |
| "last": "Halliday", |
| "suffix": "" |
| }, |
| { |
| "first": "Christian", |
| "middle": [], |
| "last": "Matthiessen", |
| "suffix": "" |
| }, |
| { |
| "first": "M", |
| "middle": [ |
| "I M" |
| ], |
| "last": "", |
| "suffix": "" |
| } |
| ], |
| "year": 2004, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "M. A. K. Halliday and Christian Matthiessen M. I. M. 2004. An introduction to functional grammar / M.A.K. Halliday, 3rd ed. / rev. by christian m.i.m. matthiessen. edition. Hodder Arnold London.", |
| "links": null |
| }, |
| "BIBREF13": { |
| "ref_id": "b13", |
| "title": "Arindam Mandal, Reza Ghanadan, and Dilek Hakkani-Tur. 2021. Further advances in open domain dialog systems in the fourth alexa prize socialbot grand challenge", |
| "authors": [ |
| { |
| "first": "Shui", |
| "middle": [], |
| "last": "Hu", |
| "suffix": "" |
| }, |
| { |
| "first": "Yang", |
| "middle": [], |
| "last": "Liu", |
| "suffix": "" |
| }, |
| { |
| "first": "Anna", |
| "middle": [], |
| "last": "Gottardi", |
| "suffix": "" |
| }, |
| { |
| "first": "Behnam", |
| "middle": [], |
| "last": "Hedayatnia", |
| "suffix": "" |
| }, |
| { |
| "first": "Anju", |
| "middle": [], |
| "last": "Khatri", |
| "suffix": "" |
| }, |
| { |
| "first": "Anjali", |
| "middle": [], |
| "last": "Chadha", |
| "suffix": "" |
| }, |
| { |
| "first": "Qinlang", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "" |
| }, |
| { |
| "first": "Pankaj", |
| "middle": [], |
| "last": "Rajan", |
| "suffix": "" |
| }, |
| { |
| "first": "Ali", |
| "middle": [], |
| "last": "Binici", |
| "suffix": "" |
| }, |
| { |
| "first": "Varun", |
| "middle": [], |
| "last": "Somani", |
| "suffix": "" |
| }, |
| { |
| "first": "Yao", |
| "middle": [], |
| "last": "Lu", |
| "suffix": "" |
| }, |
| { |
| "first": "Prerna", |
| "middle": [], |
| "last": "Dwivedi", |
| "suffix": "" |
| }, |
| { |
| "first": "Lucy", |
| "middle": [], |
| "last": "Hu", |
| "suffix": "" |
| }, |
| { |
| "first": "Hangjie", |
| "middle": [], |
| "last": "Shi", |
| "suffix": "" |
| }, |
| { |
| "first": "Sattvik", |
| "middle": [], |
| "last": "Sahai", |
| "suffix": "" |
| }, |
| { |
| "first": "Mihail", |
| "middle": [], |
| "last": "Eric", |
| "suffix": "" |
| }, |
| { |
| "first": "Karthik", |
| "middle": [], |
| "last": "Gopalakrishnan", |
| "suffix": "" |
| }, |
| { |
| "first": "Seokhwan", |
| "middle": [], |
| "last": "Kim", |
| "suffix": "" |
| }, |
| { |
| "first": "Spandana", |
| "middle": [], |
| "last": "Gella", |
| "suffix": "" |
| }, |
| { |
| "first": "Alexandros", |
| "middle": [], |
| "last": "Papangelis", |
| "suffix": "" |
| }, |
| { |
| "first": "Patrick", |
| "middle": [], |
| "last": "Lange", |
| "suffix": "" |
| }, |
| { |
| "first": "Di Jin Nicole", |
| "middle": [], |
| "last": "Chartier", |
| "suffix": "" |
| }, |
| { |
| "first": "Mahdi", |
| "middle": [], |
| "last": "Namazifar", |
| "suffix": "" |
| }, |
| { |
| "first": "Aishwarya", |
| "middle": [], |
| "last": "Padmakumar", |
| "suffix": "" |
| }, |
| { |
| "first": "Sarik", |
| "middle": [], |
| "last": "Ghazarian", |
| "suffix": "" |
| }, |
| { |
| "first": "Shereen", |
| "middle": [], |
| "last": "Oraby", |
| "suffix": "" |
| }, |
| { |
| "first": "Anjali", |
| "middle": [], |
| "last": "Narayan-Chen", |
| "suffix": "" |
| }, |
| { |
| "first": "Yuheng", |
| "middle": [], |
| "last": "Du", |
| "suffix": "" |
| } |
| ], |
| "year": null, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Shui Hu, Yang Liu, Anna Gottardi, Behnam He- dayatnia, Anju Khatri, Anjali Chadha, Qinlang Chen, Pankaj Rajan, Ali Binici, Varun Somani, Yao Lu, Prerna Dwivedi, Lucy Hu, Hangjie Shi, Sattvik Sahai, Mihail Eric, Karthik Gopalakrishnan, Seokhwan Kim, Spandana Gella, Alexandros Papangelis, Patrick Lange, Di Jin Nicole Chartier, Mahdi Namazifar, Aishwarya Padmakumar, Sarik Ghazarian, Shereen Oraby, Anjali Narayan-Chen, Yuheng Du, Lauren Stubell, Savanna Stiff, Kate Bland, Arindam Mandal, Reza Ghanadan, and Dilek Hakkani-Tur. 2021. Further advances in open do- main dialog systems in the fourth alexa prize social- bot grand challenge. https://d7qzviu3xw2xc. cloudfront.net/alexa/alexaprize/docs/ sgc4/Alexa-Prize-Technical-Paper-2021_ Final.pdf.", |
| "links": null |
| }, |
| "BIBREF14": { |
| "ref_id": "b14", |
| "title": "Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition", |
| "authors": [ |
| { |
| "first": "Dan", |
| "middle": [], |
| "last": "Jurafsky", |
| "suffix": "" |
| }, |
| { |
| "first": "James", |
| "middle": [ |
| "H" |
| ], |
| "last": "Martin", |
| "suffix": "" |
| } |
| ], |
| "year": 2009, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Dan Jurafsky and James H. Martin. 2009. Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition. Pearson Prentice Hall, Upper Saddle River, N.J.", |
| "links": null |
| }, |
| "BIBREF15": { |
| "ref_id": "b15", |
| "title": "Alquist 4.0: Towards social intelligence using generative models and dialogue personalization", |
| "authors": [ |
| { |
| "first": "Jakub", |
| "middle": [], |
| "last": "Konr\u00e1d", |
| "suffix": "" |
| }, |
| { |
| "first": "Jan", |
| "middle": [], |
| "last": "Pichl", |
| "suffix": "" |
| }, |
| { |
| "first": "Petr", |
| "middle": [], |
| "last": "Marek", |
| "suffix": "" |
| }, |
| { |
| "first": "Petr", |
| "middle": [], |
| "last": "Lorenc", |
| "suffix": "" |
| }, |
| { |
| "first": "Ondrej", |
| "middle": [], |
| "last": "Van Duy Ta", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Kobza", |
| "suffix": "" |
| } |
| ], |
| "year": 2021, |
| "venue": "4th Proceedings of Alexa Prize", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Jakub Konr\u00e1d, Jan Pichl, Petr Marek, Petr Lorenc, Van Duy Ta, and Ondrej Kobza. 2021. Alquist 4.0: Towards social intelligence using generative models and dialogue personalization. 4th Proceedings of Alexa Prize.", |
| "links": null |
| }, |
| "BIBREF16": { |
| "ref_id": "b16", |
| "title": "Dream technical report for the alexa prize", |
| "authors": [ |
| { |
| "first": "Yuri", |
| "middle": [], |
| "last": "Kuratov", |
| "suffix": "" |
| }, |
| { |
| "first": "Idris", |
| "middle": [], |
| "last": "Yusupov", |
| "suffix": "" |
| }, |
| { |
| "first": "Dilyara", |
| "middle": [], |
| "last": "Baymurzina", |
| "suffix": "" |
| }, |
| { |
| "first": "Denis", |
| "middle": [], |
| "last": "Kuznetsov", |
| "suffix": "" |
| }, |
| { |
| "first": "Daniil", |
| "middle": [], |
| "last": "Cherniavskii", |
| "suffix": "" |
| }, |
| { |
| "first": "Alexander", |
| "middle": [], |
| "last": "Dmitrievskiy", |
| "suffix": "" |
| }, |
| { |
| "first": "Elena", |
| "middle": [], |
| "last": "Ermakova", |
| "suffix": "" |
| }, |
| { |
| "first": "Fedor", |
| "middle": [], |
| "last": "Ignatov", |
| "suffix": "" |
| }, |
| { |
| "first": "Dmitry", |
| "middle": [], |
| "last": "Karpov", |
| "suffix": "" |
| }, |
| { |
| "first": "Daniel", |
| "middle": [], |
| "last": "Kornev", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Yuri Kuratov, Idris Yusupov, Dilyara Baymurzina, Denis Kuznetsov, Daniil Cherniavskii, Alexan- der Dmitrievskiy, Elena Ermakova, Fedor Ignatov, Dmitry Karpov, Daniel Kornev, et al. 2019. Dream technical report for the alexa prize 2019. 3rd Pro- ceedings of Alexa Prize.", |
| "links": null |
| }, |
| "BIBREF17": { |
| "ref_id": "b17", |
| "title": "Dialog flow framework @ deepmipt github", |
| "authors": [ |
| { |
| "first": "Denis", |
| "middle": [], |
| "last": "Kuznetsov", |
| "suffix": "" |
| }, |
| { |
| "first": "Dmitry", |
| "middle": [], |
| "last": "Evseev", |
| "suffix": "" |
| } |
| ], |
| "year": 2021, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Denis Kuznetsov and Dmitry Evseev. 2021. Di- alog flow framework @ deepmipt github. https://github.com/deepmipt/dialog_ flow_framework/.", |
| "links": null |
| }, |
| "BIBREF18": { |
| "ref_id": "b18", |
| "title": "Visual skill building", |
| "authors": [ |
| { |
| "first": "Andrew", |
| "middle": [], |
| "last": "Lawrence", |
| "suffix": "" |
| }, |
| { |
| "first": "Braden", |
| "middle": [], |
| "last": "Ream", |
| "suffix": "" |
| }, |
| { |
| "first": "Michael", |
| "middle": [], |
| "last": "Hood", |
| "suffix": "" |
| }, |
| { |
| "first": "Tyler", |
| "middle": [], |
| "last": "Han", |
| "suffix": "" |
| } |
| ], |
| "year": 2021, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Andrew Lawrence, Braden Ream, Michael Hood, and Tyler Han. 2021. Visual skill building. https:// www.voiceflow.com/.", |
| "links": null |
| }, |
| "BIBREF19": { |
| "ref_id": "b19", |
| "title": "Chat and goal-oriented dialog together: a unified example-based architecture for multi-domain dialog management", |
| "authors": [ |
| { |
| "first": "Cheongjae", |
| "middle": [], |
| "last": "Lee", |
| "suffix": "" |
| }, |
| { |
| "first": "Sangkeun", |
| "middle": [], |
| "last": "Jung", |
| "suffix": "" |
| }, |
| { |
| "first": "Minwoo", |
| "middle": [], |
| "last": "Jeong", |
| "suffix": "" |
| }, |
| { |
| "first": "Gary Geunbae", |
| "middle": [], |
| "last": "Lee", |
| "suffix": "" |
| } |
| ], |
| "year": 2006, |
| "venue": "IEEE Spoken Language Technology Workshop", |
| "volume": "", |
| "issue": "", |
| "pages": "194--197", |
| "other_ids": { |
| "DOI": [ |
| "10.1109/SLT.2006.326788" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Cheongjae Lee, Sangkeun Jung, Minwoo Jeong, and Gary Geunbae Lee. 2006. Chat and goal-oriented di- alog together: a unified example-based architecture for multi-domain dialog management. In 2006 IEEE Spoken Language Technology Workshop, pages 194- 197.", |
| "links": null |
| }, |
| "BIBREF20": { |
| "ref_id": "b20", |
| "title": "Opendial: A toolkit for developing spoken dialogue systems with probabilistic rules", |
| "authors": [ |
| { |
| "first": "Pierre", |
| "middle": [], |
| "last": "Lison", |
| "suffix": "" |
| }, |
| { |
| "first": "Casey", |
| "middle": [], |
| "last": "Kennington", |
| "suffix": "" |
| } |
| ], |
| "year": 2016, |
| "venue": "Proceedings of ACL-2016 system demonstrations", |
| "volume": "", |
| "issue": "", |
| "pages": "67--72", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Pierre Lison and Casey Kennington. 2016. Opendial: A toolkit for developing spoken dialogue systems with probabilistic rules. In Proceedings of ACL- 2016 system demonstrations, pages 67-72.", |
| "links": null |
| }, |
| "BIBREF21": { |
| "ref_id": "b21", |
| "title": "Getting to know each other: The role of social dialogue in recovery from errors in social robots", |
| "authors": [ |
| { |
| "first": "M", |
| "middle": [], |
| "last": "Gale", |
| "suffix": "" |
| }, |
| { |
| "first": "Jill", |
| "middle": [], |
| "last": "Lucas", |
| "suffix": "" |
| }, |
| { |
| "first": "David", |
| "middle": [], |
| "last": "Boberg", |
| "suffix": "" |
| }, |
| { |
| "first": "Ron", |
| "middle": [], |
| "last": "Traum", |
| "suffix": "" |
| }, |
| { |
| "first": "Jonathan", |
| "middle": [], |
| "last": "Artstein", |
| "suffix": "" |
| }, |
| { |
| "first": "Alesia", |
| "middle": [], |
| "last": "Gratch", |
| "suffix": "" |
| }, |
| { |
| "first": "Emmanuel", |
| "middle": [], |
| "last": "Gainer", |
| "suffix": "" |
| }, |
| { |
| "first": "Anton", |
| "middle": [], |
| "last": "Johnson", |
| "suffix": "" |
| }, |
| { |
| "first": "Mikio", |
| "middle": [], |
| "last": "Leuski", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Nakano", |
| "suffix": "" |
| } |
| ], |
| "year": 2018, |
| "venue": "Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI '18", |
| "volume": "", |
| "issue": "", |
| "pages": "344--351", |
| "other_ids": { |
| "DOI": [ |
| "10.1145/3171221.3171258" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Gale M. Lucas, Jill Boberg, David Traum, Ron Art- stein, Jonathan Gratch, Alesia Gainer, Emmanuel Johnson, Anton Leuski, and Mikio Nakano. 2018. Getting to know each other: The role of social di- alogue in recovery from errors in social robots. In Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI '18, page 344-351, New York, NY, USA. Association for Computing Machinery.", |
| "links": null |
| }, |
| "BIBREF22": { |
| "ref_id": "b22", |
| "title": "Experiments testing gpt-3's ability at commonsense reasoning: results", |
| "authors": [ |
| { |
| "first": "Gary", |
| "middle": [], |
| "last": "Marcus", |
| "suffix": "" |
| }, |
| { |
| "first": "Ernest", |
| "middle": [], |
| "last": "Davis", |
| "suffix": "" |
| } |
| ], |
| "year": 2020, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Gary Marcus and Ernest Davis. 2020. Experiments testing gpt-3's ability at commonsense reasoning: re- sults. https://cs.nyu.edu/~davise/papers/ GPT3CompleteTests.html.", |
| "links": null |
| }, |
| "BIBREF23": { |
| "ref_id": "b23", |
| "title": "The Penn Discourse TreeBank 2.0", |
| "authors": [ |
| { |
| "first": "Rashmi", |
| "middle": [], |
| "last": "Prasad", |
| "suffix": "" |
| }, |
| { |
| "first": "Nikhil", |
| "middle": [], |
| "last": "Dinesh", |
| "suffix": "" |
| }, |
| { |
| "first": "Alan", |
| "middle": [], |
| "last": "Lee", |
| "suffix": "" |
| }, |
| { |
| "first": "Eleni", |
| "middle": [], |
| "last": "Miltsakaki", |
| "suffix": "" |
| }, |
| { |
| "first": "Livio", |
| "middle": [], |
| "last": "Robaldo", |
| "suffix": "" |
| }, |
| { |
| "first": "Aravind", |
| "middle": [], |
| "last": "Joshi", |
| "suffix": "" |
| }, |
| { |
| "first": "Bonnie", |
| "middle": [], |
| "last": "Webber", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Rashmi Prasad, Nikhil Dinesh, Alan Lee, Eleni Milt- sakaki, Livio Robaldo, Aravind Joshi, and Bon- nie Webber. 2008. The Penn Discourse TreeBank 2.0. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Lan- guage Resources Association (ELRA).", |
| "links": null |
| }, |
| "BIBREF24": { |
| "ref_id": "b24", |
| "title": "Sk Jayadevan, Gene Hwang, and Art Pettigrue", |
| "authors": [ |
| { |
| "first": "Ashwin", |
| "middle": [], |
| "last": "Ram", |
| "suffix": "" |
| }, |
| { |
| "first": "Rohit", |
| "middle": [], |
| "last": "Prasad", |
| "suffix": "" |
| }, |
| { |
| "first": "Chandra", |
| "middle": [], |
| "last": "Khatri", |
| "suffix": "" |
| }, |
| { |
| "first": "Anu", |
| "middle": [], |
| "last": "Venkatesh", |
| "suffix": "" |
| }, |
| { |
| "first": "Raefer", |
| "middle": [], |
| "last": "Gabriel", |
| "suffix": "" |
| }, |
| { |
| "first": "Qing", |
| "middle": [], |
| "last": "Liu", |
| "suffix": "" |
| }, |
| { |
| "first": "Jeff", |
| "middle": [], |
| "last": "Nunn", |
| "suffix": "" |
| }, |
| { |
| "first": "Behnam", |
| "middle": [], |
| "last": "Hedayatnia", |
| "suffix": "" |
| }, |
| { |
| "first": "Ming", |
| "middle": [], |
| "last": "Cheng", |
| "suffix": "" |
| }, |
| { |
| "first": "Ashish", |
| "middle": [], |
| "last": "Nagar", |
| "suffix": "" |
| }, |
| { |
| "first": "Eric", |
| "middle": [], |
| "last": "King", |
| "suffix": "" |
| } |
| ], |
| "year": 2018, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Ashwin Ram, Rohit Prasad, Chandra Khatri, Anu Venkatesh, Raefer Gabriel, Qing Liu, Jeff Nunn, Behnam Hedayatnia, Ming Cheng, Ashish Nagar, Eric King, Kate Bland, Amanda Wartick, Yi Pan, Han Song, Sk Jayadevan, Gene Hwang, and Art Pet- tigrue. 2018. Conversational ai: The science behind the alexa prize.", |
| "links": null |
| }, |
| "BIBREF26": { |
| "ref_id": "b26", |
| "title": "Ddidde alpha version 0.2.1", |
| "authors": [ |
| { |
| "first": "Oleg", |
| "middle": [], |
| "last": "Serikov", |
| "suffix": "" |
| }, |
| { |
| "first": "Dmitry", |
| "middle": [], |
| "last": "Babadeev", |
| "suffix": "" |
| } |
| ], |
| "year": 2021, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Oleg Serikov and Dmitry Babadeev. 2021. Dd- idde alpha version 0.2.1. https://github.com/", |
| "links": null |
| }, |
| "BIBREF27": { |
| "ref_id": "b27", |
| "title": "Number of digital voice assistants in use worldwide from", |
| "authors": [ |
| { |
| "first": "", |
| "middle": [], |
| "last": "Statista", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Statista. 2020. Number of digital voice assistants in use worldwide from 2019 to 2024 (in billions)*. https: //www.statista.com/statistics/973815/ worldwide-digital-voice-assistant-in-use/.", |
| "links": null |
| }, |
| "BIBREF28": { |
| "ref_id": "b28", |
| "title": "Pydial: A multidomain statistical dialogue system toolkit", |
| "authors": [ |
| { |
| "first": "Stefan", |
| "middle": [], |
| "last": "Ultes", |
| "suffix": "" |
| }, |
| { |
| "first": "Lina M Rojas", |
| "middle": [], |
| "last": "Barahona", |
| "suffix": "" |
| }, |
| { |
| "first": "Pei-Hao", |
| "middle": [], |
| "last": "Su", |
| "suffix": "" |
| }, |
| { |
| "first": "David", |
| "middle": [], |
| "last": "Vandyke", |
| "suffix": "" |
| }, |
| { |
| "first": "Dongho", |
| "middle": [], |
| "last": "Kim", |
| "suffix": "" |
| }, |
| { |
| "first": "Inigo", |
| "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": "Gasic", |
| "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, Inigo Casanueva, Pawe\u0142 Budzianowski, Nikola Mrk\u0161i\u0107, Tsung-Hsien Wen, Milica Gasic, et al. 2017. Pydial: A multi- domain statistical dialogue system toolkit. In Pro- ceedings of ACL 2017, System Demonstrations, pages 73-78.", |
| "links": null |
| }, |
| "BIBREF29": { |
| "ref_id": "b29", |
| "title": "Chatbot, natural language processing popularity", |
| "authors": [], |
| "year": 2021, |
| "venue": "", |
| "volume": "2", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Google Books Ngram Viewer. 2021. Chat- bot, natural language processing popularity. https://books.google.com/ngrams/graph? content=chatbot%2C+natural+language+ processing&year_start=2000.", |
| "links": null |
| }, |
| "BIBREF30": { |
| "ref_id": "b30", |
| "title": "Artificial intelligence markup language (aiml)", |
| "authors": [ |
| { |
| "first": "Richard", |
| "middle": [], |
| "last": "Wallace", |
| "suffix": "" |
| } |
| ], |
| "year": 2001, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Richard Wallace. 2001. Artificial intelligence markup language (aiml). https://web. archive.org/web/20060505035935/http: //aitools.org/aiml/spec/.", |
| "links": null |
| }, |
| "BIBREF31": { |
| "ref_id": "b31", |
| "title": "The Anatomy of", |
| "authors": [ |
| { |
| "first": "Richard", |
| "middle": [ |
| "S" |
| ], |
| "last": "Wallace", |
| "suffix": "" |
| } |
| ], |
| "year": 2009, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "181--210", |
| "other_ids": { |
| "DOI": [ |
| "10.1007/978-1-4020-6710-5_13" |
| ] |
| }, |
| "num": null, |
| "urls": [], |
| "raw_text": "Richard S. Wallace. 2009. The Anatomy of A.L.I.C.E., pages 181-210. Springer Netherlands, Dordrecht.", |
| "links": null |
| }, |
| "BIBREF32": { |
| "ref_id": "b32", |
| "title": "Gunrock: A social bot for complex and engaging long conversations", |
| "authors": [ |
| { |
| "first": "Dian", |
| "middle": [], |
| "last": "Yu", |
| "suffix": "" |
| }, |
| { |
| "first": "Michelle", |
| "middle": [], |
| "last": "Cohn", |
| "suffix": "" |
| }, |
| { |
| "first": "Yi", |
| "middle": [ |
| "Mang" |
| ], |
| "last": "Yang", |
| "suffix": "" |
| }, |
| { |
| "first": "Chun-Yen", |
| "middle": [], |
| "last": "Chen", |
| "suffix": "" |
| }, |
| { |
| "first": "Weiming", |
| "middle": [], |
| "last": "Wen", |
| "suffix": "" |
| }, |
| { |
| "first": "Jiaping", |
| "middle": [], |
| "last": "Zhang", |
| "suffix": "" |
| }, |
| { |
| "first": "Mingyang", |
| "middle": [], |
| "last": "Zhou", |
| "suffix": "" |
| } |
| ], |
| "year": 2019, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Dian Yu, Michelle Cohn, Yi Mang Yang, Chun-Yen Chen, Weiming Wen, Jiaping Zhang, Mingyang Zhou, Kevin Jesse, Austin Chau, Antara Bhowmick, Shreenath Iyer, Giritheja Sreenivasulu, Sam David- son, Ashwin Bhandare, and Zhou Yu. 2019. Gun- rock: A social bot for complex and engaging long conversations.", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "text": "New .drawio file created in VS Code Figure 2: Setting Speech Function for the current node", |
| "uris": null, |
| "type_str": "figure", |
| "num": null |
| }, |
| "FIGREF1": { |
| "text": "Using the Speech Function Recommendations", |
| "uris": null, |
| "type_str": "figure", |
| "num": null |
| }, |
| "FIGREF2": { |
| "text": "Outlining Scenario In VS Code ExtensionFigure 5: Using the Show Markup functionality to generate DFF Markup Code Figure 6: Adding Custom Code to the DFF Markup File", |
| "uris": null, |
| "type_str": "figure", |
| "num": null |
| }, |
| "FIGREF3": { |
| "text": "Statistic data frame is shown in the Dashboard Transition graph with counters is built for all dialogues. Counters show numbers of users hitting these nodes or edges.", |
| "uris": null, |
| "type_str": "figure", |
| "num": null |
| }, |
| "FIGREF4": { |
| "text": "These figures are built in the dashboard. The top figure shows nodes that will be sent by all users at a certain step of the dialogue. The abscissa axis shows the order of a step in the dialog, the ordinate axis shows the node in which the user is located for this step. The bottom figure shows the frequency of users hitting each node, different colors are shown in different flows in which the nodesFigure 10: Statistic of transitions in dashboard. The top figure shows the frequency of users hitting each transition between nodes. The bottom figure shows duration of processing time for each transition between nodes. Different colors are shown in different flows in which the nodes.", |
| "uris": null, |
| "type_str": "figure", |
| "num": null |
| }, |
| "FIGREF5": { |
| "text": "Statistic of transition in dashboard for one dialog. This figure shows the user transitions from node to node.", |
| "uris": null, |
| "type_str": "figure", |
| "num": null |
| }, |
| "FIGREF6": { |
| "text": "This figure is built in the dashboard. The graph shows user transitions through the script graph using one dialog as an example. Counters show numbers of users hitting these nodes or edges.", |
| "uris": null, |
| "type_str": "figure", |
| "num": null |
| }, |
| "FIGREF7": { |
| "text": "Example of dialog flow in DFF DSL format Figure 14: DFF DSL elements for entity extraction from user utterance and slot filling in response Figure 15: DFF DSL elements for entity extraction using Entity Detection annotator Figure 16: Dialog Flow with \"fact_provider\" -a function for response generation using a wikiHow article Figure 17: Dialogflow for generic responses generationFigure 18: Coronavirus skill in pure Python Figure 19: Coronavirus skill DFF DSL format", |
| "uris": null, |
| "type_str": "figure", |
| "num": null |
| } |
| } |
| } |
| } |
