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SenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis
TLDR
SenticNet 3 models nuanced semantics and sentics (that is, the conceptual and affective information associated with multi-word natural language expressions), representing information with a symbolic opacity of an intermediate nature between that of neural networks and typical symbolic systems.
Gated-Attention Architectures for Task-Oriented Language Grounding
TLDR
An end-to-end trainable neural architecture for task-oriented language grounding in 3D environments which assumes no prior linguistic or perceptual knowledge and requires only raw pixels from the environment and the natural language instruction as input.
Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts
We present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective. Our system is aimed at engaging
Simple and Effective Semi-Supervised Question Answering
TLDR
This work envisions a system where the end user specifies a set of base documents and only a few labelled examples, and exploits the document structure to create cloze-style questions from these base documents; pre-trains a powerful neural network on the cloze style questions; and further fine-tunes the model on the labeled examples.
A graph-based approach to commonsense concept extraction and semantic similarity detection
TLDR
This work proposes an approach for effective multi-word commonsense expression extraction from unrestricted English text, in addition to a semantic similarity detection technique allowing additional matches to be found for specific concepts not already present in knowledge bases.
GECKA: Game Engine for Commonsense Knowledge Acquisition
TLDR
GECKA merges, as never before, the potential of serious games and games with a purpose, for the acquisition of re-usable and multi-purpose knowledge and enables the development of games that can, apart from providing entertainment value, also teach gamers something meaningful about the world they live in.
A Dataset for Tracking Entities in Open Domain Procedural Text
TLDR
This work presents the first dataset for tracking state changes in procedural text from arbitrary domains by using an unrestricted (open) vocabulary, and creates OPENPI, a high-quality, large-scale dataset comprising 29,928 state changes over 4,050 sentences from 810 procedural real-world paragraphs from WikiHow.com.
EIGEN: Event Influence GENeration using Pre-trained Language Models
TLDR
This paper presents EIGEN - a method to leverage pre-trained language models to generate event influences conditioned on a context, nature of their influence, and the distance in a reasoning chain, and derives a new dataset for research and evaluation of methods for event influence generation.
Commonsense-based topic modeling
TLDR
A commonsense knowledge based algorithm for document topic modeling is presented that does not involve training of any kind and does not depend on word co-occurrence or particular word distributions, making the algorithm effective on texts of any length and composition.
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