| Challenge: | a crowd-sourced approach to gather dialogue data is still a challenge due to the complexity of human dialogue structure and diversity of dialogue topics. |
| Approach: | They propose a platform for collecting task-oriented situated dialogue data by using gamification. |
| Outcome: | The proposed platform collects task-oriented situated dialogue data by using gamification. |
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SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues (2022.acl-long)
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| Challenge: | Until now, researchers have separated open-domain and task-oriented dialogues into two different types due to their different purposes. |
| Approach: | They propose a framework to automatically generate many dialogues without human involvement . the framework can be easily leveraged to generate unlimited dialogues in target scenarios . |
| Outcome: | The proposed framework can automatically generate many dialogues without human involvement . the human evaluation shows that the generated dialogues have a reasonable quality . |
A Unifying View On Task-oriented Dialogue Annotation (2022.lrec-1)
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| Challenge: | Recent research attention in task-oriented dialogue systems focuses on end-to-end neural models. |
| Approach: | They present a dataset that combines annotated corpora from four domains to provide a unified ontology and annotation schema for task-oriented dialogues. |
| Outcome: | The proposed dataset improves language, information content and performance in dialogues with two recent models. |
NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation (2021.acl-long)
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| Challenge: | NeuralWOZ generates dialogues from user’s goal instructions and system’s API call results. |
| Approach: | They propose a framework that uses model-based dialogue simulation to generate dialogues from user’s goal instructions and system’s API call results. |
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Task-Oriented Dialogue as Dataflow Synthesis (2020.tacl-1)
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Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, Hao Fang, Alan Guo, David Hall, Kristin Hayes, Kellie Hill, Diana Ho, Wendy Iwaszuk, Smriti Jha, Dan Klein, Jayant Krishnamurthy, Theo Lanman, Percy Liang, Christopher H. Lin, Ilya Lintsbakh, Andy McGovern, Aleksandr Nisnevich, Adam Pauls, Dmitrij Petters, Brent Read, Dan Roth, Subhro Roy, Jesse Rusak, Beth Short, Div Slomin, Ben Snyder, Stephon Striplin, Yu Su, Zachary Tellman, Sam Thomson, Andrei Vorobev, Izabela Witoszko, Jason Wolfe, Abby Wray, Yuchen Zhang, Alexander Zotov
| Challenge: | Existing approaches to task-oriented dialogue represent dialogue state as a dataflow graph . microsoft's SMCalFlow dataset features complex dialogues about events, weather, places, and people . |
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| Outcome: | The proposed framework improves representability and predictability in natural dialogues . it uses dataflow graphs and metacomputation to map user intents to a program . |
Data Collection for Dialogue System: A Startup Perspective (N18-3)
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| Challenge: | Developing dialogue systems such as Apple Siri and Google Now requires high quality training data but data collection with crowdsourcing is largely an open question. |
| Approach: | They propose to use crowdsourcing to collect data for a user intent classification task in a dialogue system. |
| Outcome: | The proposed method improves the quality of the collected data and the model performance on real user queries. |
AirDialogue: An Environment for Goal-Oriented Dialogue Research (D18-1)
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| Challenge: | Recent advances in dialogue generation have inspired a number of studies on dialogue systems . however, current datasets are limited in size and the environment for training agents is relatively unsophisticated. |
| Approach: | They propose to use a context-generator to generate travel and flight restrictions to train agents. |
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Game-Based Video-Context Dialogue (D18-1)
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| Challenge: | Current dialogue systems focus more on textual and speech context knowledge and are usually based on two speakers. |
| Approach: | They propose to use live soccer game videos and Twitch.tv chats to develop visual-grounded dialogue models. |
| Outcome: | The proposed model can generate relevant temporal and spatial event language from live video and chat history while also being relevant to chat history. |
Dialogue Scenario Collection of Persuasive Dialogue with Emotional Expressions via Crowdsourcing (L18-1)
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| Challenge: | Existing methods for data collection and annotation are costly and prevent launching new dialogue systems. |
| Approach: | They asked crowd workers to create persuasive dialogue systems using emotional expressions . they annotated emotional states and users' acceptance for system persuasion . |
| Outcome: | The proposed system has sufficient agreement even without training, the researchers found . the experiment showed that the collected data are comparable to real-world dialogue recording methods . |
Adding Chit-Chat to Enhance Task-Oriented Dialogues (2021.naacl-main)
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Kai Sun, Seungwhan Moon, Paul Crook, Stephen Roller, Becka Silvert, Bing Liu, Zhiguang Wang, Honglei Liu, Eunjoon Cho, Claire Cardie
| Challenge: | Existing dialogue systems focus on functional goals, open-domain chatbots on socially engaging conversations. |
| Approach: | They propose to add chit-chat to ENhance Task-ORiented dialogues by a human-assisted data collection approach to augment task-oriented dialogues with minimal annotation effort. |
| Outcome: | The proposed models can code-switch between task and chit-chat to be more engaging, interesting, knowledgeable, and humanlike while maintaining competitive task performance. |
Data Collection and End-to-End Learning for Conversational AI (D19-2)
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| Challenge: | tutorial aims to familiarise research community with recent advances in statistical dialogue systems . focus of tutorial is on learning end-to-end from data and their relation to more common modular systems. |
| Approach: | This tutorial aims to familiarise the research community with the latest advances in statistical dialogue systems . the focus of the tutorial is on recently introduced end-to-end learning for dialogue systems and their relation to more common modular systems. |
| Outcome: | This tutorial aims to familiarise the research community with the recent advances in statistical dialogue systems for open-domain and task-based dialogue paradigms. |