Chat-crowd: A Dialog-based Platform for Visual Layout Composition (N19-4)

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Challenge: We present Chat-crowd, an interactive environment for visual layout composition via conversational interactions . system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection .
Approach: They introduce an interactive environment for visual layout composition via conversational interactions that supports multiple agents with two conversational roles.
Outcome: The proposed system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection and has quality controls on the performance of both types of agents.

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Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions (2021.findings-emnlp)

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Challenge: Popular dialog datasets such as MultiWOZ are created by providing crowd workers with instructions that describe the task to be accomplished.
Approach: They propose a data creation strategy that uses a pre-trained language model to simulate the interaction between crowd workers by creating a user bot and an agent bot.
Outcome: The proposed data creation strategy improves on two publicly available datasets using a pre-trained language model and a smaller percentage of actual crowd-generated conversations and their corresponding instructions.
DialCrowd 2.0: A Quality-Focused Dialog System Crowdsourcing Toolkit (2022.lrec-1)

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Challenge: DialCrowd 2.0 helps requesters obtain higher quality data from human intelligence tasks.
Approach: They propose to use DialCrowd 2.0 to help requesters obtain higher quality data . they aim to improve the way requesters present tasks and facilitate effective communication with workers.
Outcome: The proposed toolkit enables requesters to obtain higher quality data by presenting tasks more clearly and facilitating effective communication with workers.
Situation-Based Multiparticipant Chat Summarization: a Concept, an Exploration-Annotation Tool and an Example Collection (2021.acl-srw)

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Challenge: Currently, text chat does not offer navigation or full-featured search, although the high volumes of messages demand it.
Approach: They propose a data annotation tool for situation-based summarization that can be used to extract messages from chat logs.
Outcome: The proposed tool is the first to be developed for situation-based summarization.
The slurk Interaction Server Framework: Better Data for Better Dialog Models (2022.lrec-1)

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Challenge: slurk is a lightweight dialog data collection and testing tool for crowdsourcing platforms.
Approach: They present a lightweight dialog server that allows to set up dialog data collections and run experiments.
Outcome: The slurk software allows to set up dialog data collections and run experiments with no limitations on the number of participants.
Construction and Analysis of a Multimodal Chat-talk Corpus for Dialog Systems Considering Interpersonal Closeness (2020.lrec-1)

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Challenge: a large-scale multimodal dialog corpus is needed to accelerate research on dialog systems that can handle social signals and verbal information.
Approach: They construct a multimodal dialog corpus focusing on the relationship between speakers and 19 pairs of participants.
Outcome: The proposed system is based on a multimodal dialog corpus of 19,303 utterances (10 hours) from 19 pairs of participants.
Are the Tools up to the Task? an Evaluation of Commercial Dialog Tools in Developing Conversational Enterprise-grade Dialog Systems (N19-2)

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Challenge: Existing toolsets are incomplete in meeting the goal of building effective dialog systems, authors say .
Approach: They compare dialog tools available from a number of companies to determine their strengths and weaknesses . they provide quantitative and qualitative results in three main areas: natural language understanding, dialog, and text generation .
Outcome: The toolsets are incomplete, but they are compared to other tools to determine their strengths and weaknesses.
Dialog Intent Structure: A Hierarchical Schema of Linked Dialog Acts (L18-1)

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Challenge: a schema for dialog representation captures the pragmatic intents of the conversation independently from any semantic representation.
Approach: They propose a hierarchical and extensible schema for dialog representation . schema captures pragmatic intents of conversation independently from any semantic representation based on semantic content .
Outcome: The proposed schema captures the pragmatic intents of the conversation independently from any semantic representation.
Sketching a Linguistically-Driven Reasoning Dialog Model for Social Talk (2022.acl-srw)

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Challenge: a new study shows that dialog systems that can hold social talk and make sense of conversational content are not efficient for context-sensitive natural language understanding and reasoning.
Approach: They propose a linguistically-informed architecture to handle social talk in English . they propose linguistic models that fit the context-sensitive components into a Bayesian game-theoretic model .
Outcome: The proposed architecture is based on corpus-based methods but does not track what is happening in a conversation.
SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations (2021.emnlp-main)

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Challenge: Existing task-oriented dialog datasets do not situate the dialog in the user’s multimodal context.
Approach: They propose to use a dataset to study multimodal task-oriented dialogs in the shopping domain to situate them in the user’s multimodal context.
Outcome: The proposed dataset includes 11K task-oriented user->assistant dialogs (117K utterances) in the shopping domain, grounded in immersive and photo-realistic scenes.
Collaborative Dialogue in Minecraft (P19-1)

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Challenge: Using computer games to simulate grounded situations, we want to develop interactive agents that can communicate with humans to solve tasks in grounded scenarios.
Approach: They propose a Minecraft-based collaborative building task in which one player is shown a building structure and needs to instruct the other player to build it.
Outcome: The proposed agent can communicate with humans to solve a building task in a Minecraft-based environment without the need for physical robots.

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