Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

The significance of silence. Long gaps attenuate the preference for ‘yes’ responses in conversation.

In conversation, negative responses to invitations, requests, offers and the like more often occur with a delay – conversation analysts talk of them as dispreferred. Here we examine the contrastive cognitive load ‘yes’ and ‘no’ responses make, either when given relatively fast (300 ms) or delayed (1000 ms). Participants heard minidialogues, with turns extracted from a spoken corpus, while having their EEG recorded. We find that a fast ‘no’ evokes an N400-effect relative to a fast ‘yes’, however this contrast is not present for delayed responses. This shows that an immediate response is expected to be positive – but this expectation disappears as the response time lengthens because now in ordinary conversation the probability of a ‘no’ has increased. Additionally, however, 'No' responses elicit a late frontal positivity both when they are fast and when they are delayed. Thus, regardless of the latency of response, a ‘no’ response is associated with a late positivity, since a negative response is always dispreferred and may require an account. Together these results show that negative responses to social actions exact a higher cognitive load, but especially when least expected, as an immediate response

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Continually improving grounded natural language understanding through human-robot dialog

As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge are required to understand and respond to a human's natural language commands and questions. If a person requests an assistant robot to take me to Alice's office, the robot must know that Alice is a person who owns some unique office, and that take me means it should navigate there. Similarly, if a person requests bring me the heavy, green mug, the robot must have accurate mental models of the physical concepts heavy, green, and mug. To avoid forcing humans to use key phrases or words robots already know, this thesis focuses on helping robots understanding new language constructs through interactions with humans and with the world around them. To understand a command in natural language, a robot must first convert that command to an internal representation that it can reason with. Semantic parsing is a method for performing this conversion, and the target representation is often semantic forms represented as predicate logic with lambda calculus. Traditional semantic parsing relies on hand-crafted resources from a human expert: an ontology of concepts, a lexicon connecting language to those concepts, and training examples of language with abstract meanings. One thrust of this thesis is to perform semantic parsing with sparse initial data. We use the conversations between a robot and human users to induce pairs of natural language utterances with the target semantic forms a robot discovers through its questions, reducing the annotation effort of creating training examples for parsing. We use this data to build more dialog-capable robots in new domains with much less expert human effort (Thomason et al., 2015; Padmakumar et al., 2017). Meanings of many language concepts are bound to the physical world. Understanding object properties and categories, such as heavy, green, and mug requires interacting with and perceiving the physical world. Embodied robots can use manipulation capabilities, such as pushing, picking up, and dropping objects to gather sensory data about them. This data can be used to understand non-visual concepts like heavy and empty (e.g. get the empty carton of milk from the fridge), and assist with concepts that have both visual and non-visual expression (e.g. tall things look big and also exert force sooner than short things when pressed down on). A second thrust of this thesis focuses on strategies for learning these concepts using multi-modal sensory information. We use human-in-the-loop learning to get labels between concept words and actual objects in the environment (Thomason et al., 2016, 2017). We also explore ways to tease out polysemy and synonymy in concept words (Thomason and Mooney, 2017) such as light, which can refer to a weight or a color, the latter sense being synonymous with pale. Additionally, pushing, picking up, and dropping objects to gather sensory information is prohibitively time-consuming, so we investigate strategies for using linguistic information and human input to expedite exploration when learning a new concept (Thomason et al., 2018). Finally, we build an integrated agent with both parsing and perception capabilities that learns from conversations with users to improve both components over time. We demonstrate that parser learning from conversations (Thomason et al., 2015) can be combined with multi-modal perception (Thomason et al., 2016) using predicate-object labels gathered through opportunistic active learning (Thomason et al., 2017) during those conversations to improve performance for understanding natural language commands from humans. Human users also qualitatively rate this integrated learning agent as more usable after it has improved from conversation-based learning.Computer Science

Learning to Interpret and Apply Multimodal Descriptions

Han T. Learning to Interpret and Apply Multimodal Descriptions. Bielefeld: Universität Bielefeld; 2018.Enabling computers to understand natural human communication is a goal researchers have been long aspired to in artificial intelligence. Since the concept demonstration of “Put-That- There” in 1980s, significant achievements have been made in developing multimodal interfaces that can process human communication such as speech, eye gaze, facial emotion, co-verbal hand gestures and pen input. State-of-the-art multimodal interfaces are able to process pointing gestures, symbolic gestures with conventional meanings, as well as gesture commands with pre-defined meanings (e.g., circling for “select”). However, in natural communication, co- verbal gestures/pen input rarely convey meanings via conventions or pre-defined rules, but embody meanings relatable to the accompanying speech. For example, in route given tasks, people often describe landmarks verbally (e.g., two buildings), while demonstrating the relative position with two hands facing each other in the space. Interestingly, when the same gesture is accompanied by the utterance a ball, it may indicate the size of the ball. Hence, the interpretation of such co-verbal hand gestures largely depends on the accompanied verbal content. Similarly, when describing objects, while verbal utterances are most convenient for describing colour and category (e.g., a brown elephant), hand-drawn sketches are often deployed to convey iconic information such as the exact shape of the elephant’s trunk, which is typically difficult to encode in language. This dissertation concerns the task of learning to interpret multimodal descriptions com- posed of verbal utterances and hand gestures/sketches, and apply corresponding interpretations to tasks such as image retrieval. Specifically, we aim to address following research questions: 1) For co-verbal gestures that embody meanings relatable to accompanied verbal content, how can we use natural language information to interpret the semantics of such co-verbal gestures, e.g., does a gesture indicate relative position or size? 2) As an integral system of commu- nication, speech and gestures not only bear close semantic relations, but also close temporal relations. To what degree and on which dimensions can hand gestures benefit the task of inter- preting multimodal descriptions? 3) While it’s obvious that iconic information in hand-drawn sketches enriches verbal content in object descriptions, how to model the joint contributions of such multimodal descriptions and to what degree can verbal descriptions compensate reduced iconic details in hand-drawn sketches? To address the above questions, we first introduce three multimodal description corpora: a spatial description corpus composed of natural language and placing gestures (also referred as abstract deictics), a multimodal object description corpus composed of natural language and hand-drawn sketches, and an existing corpus - the Bielefeld Speech and Gesture Alignment Corpus (SAGA). 3 4 We frame the problem of learning gesture semantics as a multi-label classification task us- ing natural language information and hand gesture features. We conducted an experiment with the SAGA corpus. The results show that natural language is informative for the interpretation of hand gestures. Further more, we describe a system that models the interpretation and application of spatial descriptions and explored three variants of representation methods of the verbal content. When representing the verbal content in the descriptions with a set of automatically learned symbols, the system’s performance is on par with representations with manually defined symbols (e.g., pre-defined object properties). We show that abstract deictic gestures not only lead to better understanding of spatial descriptions, but also result in earlier correct decisions of the system, which can be used to trigger immediate reactions in dialogue systems. Finally, we investigate the interplay of semantics between symbolic (natural language) and iconic (sketches) modes in multimodal object descriptions, where natural language and sketches jointly contribute to the communications. We model the meaning of natural language and sketches two existing models and combine the meanings from both modalities with a late fusion approach. The results show that even adding reduced sketches (30% of full sketches) can help in the retrieval task. Moreover, in current setup, natural language descriptions can compensate around 30% of reduced sketches

From Verbs to Tasks: An Integrated Account of Learning Tasks from Situated Interactive Instruction.

Intelligent collaborative agents are becoming common in the human society. From virtual assistants such as Siri and Google Now to assistive robots, they contribute to human activities in a variety of ways. As they become more pervasive, the challenge of customizing them to a variety of environments and tasks becomes critical. It is infeasible for engineers to program them for each individual use. Our research aims at building interactive robots and agents that adapt to new environments autonomously by interacting with human users using natural modalities. This dissertation studies the problem of learning novel tasks from human-agent dialog. We propose a novel approach for interactive task learning, situated interactive instruction (SII), and investigate approaches to three computational challenges that arise in designing SII agents: situated comprehension, mixed-initiative interaction, and interactive task learning. We propose a novel mixed-modality grounded representation for task verbs which encompasses their lexical, semantic, and task-oriented aspects. This representation is useful in situated comprehension and can be learned through human-agent interactions. We introduce the Indexical Model of comprehension that can exploit extra-linguistic contexts for resolving semantic ambiguities in situated comprehension of task commands. The Indexical model is integrated with a mixed-initiative interaction model that facilitates a flexible task-oriented human-agent dialog. This dialog serves as the basis of interactive task learning. We propose an interactive variation of explanation-based learning that can acquire the proposed representation. We demonstrate that our learning paradigm is efficient, can transfer knowledge between structurally similar tasks, integrates agent-driven exploration with instructional learning, and can acquire several tasks. The methods proposed in this thesis are integrated in Rosie - a generally instructable agent developed in the Soar cognitive architecture and embodied on a table-top robot.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111573/1/shiwali_1.pd