9 research outputs found
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.
Imposing transparency and explainability requirements on such agents is
especially important in order to establish trust and common ground with the
end-to-end automated planning system. Visualizing the agent's internal
decision-making processes is a crucial step towards achieving this. This may
include externalizing the "brain" of the agent -- starting from its sensory
inputs, to progressively higher order decisions made by it in order to drive
its planning components. We also show how the planner can bootstrap on the
latest techniques in explainable planning to cast plan visualization as a plan
explanation problem, and thus provide concise model-based visualization of its
plans. We demonstrate these functionalities in the context of the automated
planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator
(appeared in AAAI 2017 Fall Symposium on Human-Agent Groups
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
Textual entailment is a fundamental task in natural language processing. Most
approaches for solving the problem use only the textual content present in
training data. 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. However, the proposed models do not fully exploit the information in the
usually large and noisy KGs, and it is not clear how it can be effectively
encoded to be useful for entailment. We present an approach that complements
text-based entailment models with information from KGs by (1) using
Personalized PageR- ank to generate contextual subgraphs with reduced noise and
(2) encoding these subgraphs using graph convolutional networks to capture KG
structure. Our technique extends the capability of text models exploiting
structural and semantic information found in KGs. We evaluate our approach on
multiple textual entailment datasets and show that the use of external
knowledge helps improve prediction accuracy. This is particularly evident in
the challenging BreakingNLI dataset, where we see an absolute improvement of
5-20% over multiple text-based entailment models
PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
The field of Question Answering (QA) has made remarkable progress in recent
years, thanks to the advent of large pre-trained language models, newer
realistic benchmark datasets with leaderboards, and novel algorithms for key
components such as retrievers and readers. In this paper, we introduce PRIMEQA:
a one-stop and open-source QA repository with an aim to democratize QA
re-search and facilitate easy replication of state-of-the-art (SOTA) QA
methods. PRIMEQA supports core QA functionalities like retrieval and reading
comprehension as well as auxiliary capabilities such as question generation.It
has been designed as an end-to-end toolkit for various use cases: building
front-end applications, replicating SOTA methods on pub-lic benchmarks, and
expanding pre-existing methods. PRIMEQA is available at :
https://github.com/primeqa
A Unified Implicit Dialog Framework for Conversational Commerce
We propose a unified Implicit Dialog framework for goal-oriented, information seeking tasks of Conversational Commerce applications. It aims to enable the dialog interactions with domain data without replying on the explicitly encoded rules but utilizing the underlying data representation to build the components required for the interactions, which we refer as Implicit Dialog in this work. The proposed framework consists of a pipeline of End-to-End trainable modules. It generates a centralized knowledge representation to semantically ground multiple sub-modules. The framework is also integrated with an associated set of tools to gather end users' input for continuous improvement of the system. This framework is designed to facilitate fast development of conversational systems by identifying the components and the data that can be adapted and reused across many end-user applications. We demonstrate our approach by creating conversational agents for several independent domains
Doc2Dial: A Framework for Dialogue Composition Grounded in Documents
We introduce Doc2Dial, an end-to-end framework for generating conversational data grounded in given documents. It takes the documents as input and generates the pipelined tasks for obtaining the annotations specifically for producing the simulated dialog flows. Then, the dialog flows are used to guide the collection of the utterances via the integrated crowdsourcing tool. The outcomes include the human-human dialogue data grounded in the given documents, as well as various types of automatically or human labeled annotations that help ensure the quality of the dialog data with the flexibility to (re)composite dialogues. We expect such data can facilitate building automated dialogue agents for goal-oriented tasks. We demonstrate Doc2Dial system with the various domain documents for customer care