How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus (original) (raw)
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Situated dialogue is prominent in the robot navigation task, where a human gives route instructions (i.e., a sequence of navigation commands) to an agent. We propose an approach for situated dialogue agents whereby they use strategies such as asking questions to repair or recover from unclear instructions, namely those that an agent misunderstands or considers ambiguous. Most immediately in this work we study examples from existing human-human dialogue corpora and relate them to our proposed approach.
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2010
This paper presents an implemented approach of processing situated dialogue between a human and a robot. The focus is on task-oriented dialogue, set in the larger context of human-robot collaborative activity. The approach models understanding and production of dialogue to include intension (what is being talked about), intention (the goal of why something is being said), and attention (what is being focused on). These dimensions are directly construed in terms of assumptions and assertions on situated multi-agent belief models. The approach is continual in that it allows for interpretations to be dynamically retracted, revised, or deferred. This makes it possible to deal with the inherent asymmetry in how robots and humans tend to understand dialogue, and the world in which it is set. The approach has been fully implemented, and integrated into a cognitive robot. The paper discusses the implementation, and illustrates it in a collaborative learning setting.
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Content authoring for conversations is a limiting factor in creating verbal interactions with intelligent virtual agents. Building on techniques utilizing semi-situated learning in an incremental crowdworking pipeline, this paper introduces an embodied agent that self-authors its own dialog for social chat. In particular, the autonomous use of crowdworkers is supplemented with a generalization method that borrows and assesses the validity of dialog across conversational states. We argue that the approach offers a community-focused tailoring of dialog responses that is not available in approaches that rely solely on statistical methods across big data. We demonstrate the advantages that this can bring to interactions through data collected from 486 conversations between a situated social agent and 22 users during a 3 week long evaluation period.
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We teach goal-driven agents to interactively act and speak in situated environments by training on generated curriculums. Our agents operate in LIGHT (Urbanek et al., 2019)—a large-scale crowd-sourced fantasy text adventure game wherein an agent perceives and interacts with the world through textual natural language. Goals in this environment take the form of character-based quests, consisting of personas and motivations. We augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents to achieve such goals. In particular, we measure curriculum difficulty in terms of the rarity of the quest in the original training distribution—an easier environment is one that is more likely to have been found in the unaugmented dataset. An ablation study shows that this method of learning from the tail of a distribution results in significantly higher generalization abilities as measured by...
Balancing Efficiency and Coverage in Human-Robot Dialogue Collection
ArXiv, 2018
We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automated robot dialogue system to support collaborative exploration tasks. In the first phase, a wizard freely typed robot utterances to human participants. For the second phase, this data was used to design a GUI that includes buttons for the most common communications, and templates for communications with varying parameters. Comparison of the data gathered in these phases show that the GUI enabled a faster pace of dialogue while still maintaining high coverage of suitable responses, enabling more efficient targeted data collection, and improvements in natural language understanding using GUI-collected data. As a promising first step towards interactive learning, this work shows that our approach enables the collection of useful training data for navigation-based HRI tasks.
Learning through Dialogue Interactions by Asking Questions
2017
A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction. In this work, we explore this direction by designing a simulator and a set of synthetic tasks in the movie domain that allow such interactions between a learner and a teacher. We investigate how a learner can benefit from asking questions in both offline and online reinforcement learning settings, and demonstrate that the learner improves when asking questions. Finally, real experiments with Mechanical Turk validate the approach. Our work represents a first step in developing such end-to-end learned interactive dialogue agents.
2018
The Niki and Julie corpus contains more than 600 dialogues between human participants and a human-controlled robot or virtual agent, engaged in a series of collaborative item-ranking tasks designed to measure influence. Some of the dialogues contain deliberate conversational errors by the robot, designed to simulate the kinds of conversational breakdown that are typical of present-day automated agents. Data collected include audio and video recordings, the results of the ranking tasks, and questionnaire responses; some of the recordings have been transcribed and annotated for verbal and nonverbal feedback. The corpus has been used to study influence and grounding in dialogue. All the dialogues are in American English.
Learning environmental knowledge from task-based human-robot dialog
2013 IEEE International Conference on Robotics and Automation, 2013
This paper presents an approach for learning environmental knowledge from task-based human-robot dialog. Previous approaches to dialog use domain knowledge to constrain the types of language people are likely to use. In contrast, by introducing a joint probabilistic model over speech, the resulting semantic parse and the mapping from each element of the parse to a physical entity in the building (e.g., grounding), our approach is flexible to the ways that untrained people interact with robots, is robust to speech to text errors and is able to learn referring expressions for physical locations in a map (e.g., to create a semantic map). Our approach has been evaluated by having untrained people interact with a service robot. Starting with an empty semantic map, our approach is able ask 50% fewer questions than a baseline approach, thereby enabling more effective and intuitive human robot dialog.