Search Results for author: Nikhil Krishnaswamy

Found 20 papers, 0 papers with code

Embodied Multimodal Agents to Bridge the Understanding Gap

no code implementations EACL (HCINLP) 2021 Nikhil Krishnaswamy, Nada Alalyani

In this paper we argue that embodied multimodal agents, i. e., avatars, can play an important role in moving natural language processing toward “deep understanding.” Fully-featured interactive agents, model encounters between two “people,” but a language-only agent has little environmental and situational awareness.

Natural Language Processing

Exploiting Embodied Simulation to Detect Novel Object Classes Through Interaction

no code implementations17 Apr 2022 Nikhil Krishnaswamy, Sadaf Ghaffari

In this paper we present a novel method for a naive agent to detect novel objects it encounters in an interaction.


Neurosymbolic AI for Situated Language Understanding

no code implementations5 Dec 2020 Nikhil Krishnaswamy, James Pustejovsky

In recent years, data-intensive AI, particularly the domain of natural language processing and understanding, has seen significant progress driven by the advent of large datasets and deep neural networks that have sidelined more classic AI approaches to the field.

Natural Language Processing

Situated Multimodal Control of a Mobile Robot: Navigation through a Virtual Environment

no code implementations13 Jul 2020 Katherine Krajovic, Nikhil Krishnaswamy, Nathaniel J. Dimick, R. Pito Salas, James Pustejovsky

We present a new interface for controlling a navigation robot in novel environments using coordinated gesture and language.

Robot Navigation

A Formal Analysis of Multimodal Referring Strategies Under Common Ground

no code implementations LREC 2020 Nikhil Krishnaswamy, James Pustejovsky

In this paper, we present an analysis of computationally generated mixed-modality definite referring expressions using combinations of gesture and linguistic descriptions.

Multimodal Continuation-style Architectures for Human-Robot Interaction

no code implementations18 Sep 2019 Nikhil Krishnaswamy, James Pustejovsky

We present an architecture for integrating real-time, multimodal input into a computational agent's contextual model.

One-Shot Learning

Generating a Novel Dataset of Multimodal Referring Expressions

no code implementations WS 2019 Nikhil Krishnaswamy, James Pustejovsky

Referring expressions and definite descriptions of objects in space exploit information both about object characteristics and locations.

Situational Grounding within Multimodal Simulations

no code implementations5 Feb 2019 James Pustejovsky, Nikhil Krishnaswamy

In this paper, we argue that simulation platforms enable a novel type of embodied spatial reasoning, one facilitated by a formal model of object and event semantics that renders the continuous quantitative search space of an open-world, real-time environment tractable.

Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise

no code implementations27 Nov 2018 Nikhil Krishnaswamy, Scott Friedman, James Pustejovsky

We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms.

Every Object Tells a Story

no code implementations COLING 2018 James Pustejovsky, Nikhil Krishnaswamy

Most work within the computational event modeling community has tended to focus on the interpretation and ordering of events that are associated with verbs and event nominals in linguistic expressions.

Building Multimodal Simulations for Natural Language

no code implementations EACL 2017 James Pustejovsky, Nikhil Krishnaswamy

Simulation and automatic visualization of events from natural language descriptions and supplementary modalities, such as gestures, allows humans to use their native capabilities as linguistic and visual interpreters to collaborate on tasks with an artificial agent or to put semantic intuitions to the test in an environment where user and agent share a common context. In previous work (Pustejovsky and Krishnaswamy, 2014; Pustejovsky, 2013a), we introduced a method for modeling natural language expressions within a 3D simulation environment built on top of the game development platform Unity (Goldstone, 2009).

Referring Expression Referring expression generation +2

The Development of Multimodal Lexical Resources

no code implementations WS 2016 James Pustejovsky, Tuan Do, Gitit Kehat, Nikhil Krishnaswamy

Human communication is a multimodal activity, involving not only speech and written expressions, but intonation, images, gestures, visual clues, and the interpretation of actions through perception.

Question Answering Visual Question Answering

Generating Simulations of Motion Events from Verbal Descriptions

no code implementations SEMEVAL 2014 James Pustejovsky, Nikhil Krishnaswamy

The generated simulations act as a conceptual "debugger" for the semantics of different motion verbs: that is, by testing for consistency and informativeness in the model, simulations expose the presuppositions associated with linguistic expressions and their compositions.

Informativeness Unity

ECAT: Event Capture Annotation Tool

no code implementations5 Oct 2016 Tuan Do, Nikhil Krishnaswamy, James Pustejovsky

This paper introduces the Event Capture Annotation Tool (ECAT), a user-friendly, open-source interface tool for annotating events and their participants in video, capable of extracting the 3D positions and orientations of objects in video captured by Microsoft's Kinect(R) hardware.

VoxML: A Visualization Modeling Language

no code implementations LREC 2016 James Pustejovsky, Nikhil Krishnaswamy

We present the specification for a modeling language, VoxML, which encodes semantic knowledge of real-world objects represented as three-dimensional models, and of events and attributes related to and enacted over these objects.

Multimodal Semantic Simulations of Linguistically Underspecified Motion Events

no code implementations3 Oct 2016 Nikhil Krishnaswamy, James Pustejovsky

In this paper, we describe a system for generating three-dimensional visual simulations of natural language motion expressions.

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