Search Results for author:
Found 39 papers, 6 papers with code
EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learning
To tackle these issues, in this paper, we present EXPLORER which is an exploration-guided reasoning agent for textual reinforcement learning.
Are Human Conversations Special? A Large Language Model Perspective
This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human).
Biomechanics-Guided Facial Action Unit Detection Through Force Modeling
In this paper, we propose a biomechanics-guided AU detection approach, where facial muscle activation forces are modelled, and are employed to predict AU activation.
Knowledge-augmented Deep Learning and Its Applications: A Survey
Based on our taxonomy, we provide a systematic review of existing techniques, different from existing works that survey integration approaches agnostic to taxonomy of knowledge.
Investigating Explainability of Generative AI for Code through Scenario-based Design
Using scenario-based design and question-driven XAI design approaches, we explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion.
When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data
We can invent novel rules on the fly.
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming
These rules are learned in an online manner and applied with an ASP solver to predict an action for the agent.
Inductive logic programming
Natural Language Understanding
+2
Using Document Similarity Methods to create Parallel Datasets for Code Translation
However, due to the paucity of parallel data in this domain, supervised techniques have only been applied to a limited set of popular programming languages.
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL).
Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents
This improves the agent's overall understanding of the game 'scene' and objects' relationships to the world around them, and the variety of visual representations on offer allow the agent to generate a better generalization of a relationship.
Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization
Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled.
NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands
The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line.
Reading Comprehension as Natural Language Inference:A Semantic Analysis
We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning
The goal is to force an RL agent to use both text and visual features to predict natural language action commands for solving the final task of cooking a meal.
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines
Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making.
Ranked #1 on
Commonsense Reasoning for RL
on commonsense-rl
Reading Comprehension as Natural Language Inference: A Semantic Analysis
We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games
Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x-20x fewer training games compared to previous state-of-the-art methods despite requiring less number of training episodes.
Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks
In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it.
Type-augmented Relation Prediction in Knowledge Graphs
Knowledge graph completion (also known as relation prediction) is the task of inferring missing facts given existing ones.
Towards an Atlas of Cultural Commonsense for Machine Reasoning
Existing commonsense reasoning datasets for AI and NLP tasks fail to address an important aspect of human life: cultural differences.
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches
We introduce a number of RL agents that combine the sequential context with a dynamic graph representation of their beliefs of the world and commonsense knowledge from ConceptNet in different ways.
Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge
In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments.
Project CLAI: Instrumenting the Command Line as a New Environment for AI Agents
This whitepaper reports on Project CLAI (Command Line AI), which aims to bring the power of AI to the command line interface (CLI).
Path-Based Contextualization of Knowledge Graphs for Textual Entailment
In this paper, we introduce the problem of knowledge graph contextualization -- that is, given a specific NLP task, the problem of extracting meaningful and relevant sub-graphs from a given knowledge graph.
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.
Answering Science Exam Questions Using Query Reformulation with Background Knowledge
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
An Interface for Annotating Science Questions
Recent work introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set.
Tentacular Artificial Intelligence, and the Architecture Thereof, Introduced
We briefly introduce herein a new form of distributed, multi-agent artificial intelligence, which we refer to as "tentacular."
Improving Natural Language Inference Using External Knowledge in the Science Questions Domain
To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain.
Answering Science Exam Questions Using Query Rewriting with Background Knowledge
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset
We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset.
Toward Cognitive and Immersive Systems: Experiments in a Cognitive Microworld
These \textit{cognitive and immersive systems} (CAISs) fall squarely into the intersection of AI with HCI/HRI: such systems interact with and assist the human agents that enter them, in no small part because such systems are infused with AI able to understand and reason about these humans and their knowledge, beliefs, goals, communications, plans, etc.
Workflow Complexity for Collaborative Interactions: Where are the Metrics? -- A Challenge
In this paper, we introduce the problem of denoting and deriving the complexity of workflows (plans, schedules) in collaborative, planner-assisted settings where humans and agents are trying to jointly solve a task.
Visualizations for an Explainable Planning Agent
In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making.
UbuntuWorld 1.0 LTS - A Platform for Automated Problem Solving & Troubleshooting in the Ubuntu OS
In this paper, we present UbuntuWorld 1. 0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system.
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens.
Ranked #1 on
Dialogue Generation
on Ubuntu Dialogue (Activity)
The Metrics Matter! On the Incompatibility of Different Flavors of Replanning
An open question is whether these metrics are interchangeable; answering this requires a normalized comparison of the various replanning quality metrics.
Herding the Crowd: Automated Planning for Crowdsourced Planning
In this paper, we will argue that the automated oversight used in these systems can be viewed as a primitive automated planner, and that there are several opportunities for more sophisticated automated planning in effectively steering crowdsourced planning.
Strategic Planning for Network Data Analysis
In this paper, we motivate the novel "strategic planning" problem -- one of gathering data from the world and applying the underlying model of the domain in order to come up with decisions that will monitor the system in an automated manner.
