6 research outputs found
Machine Common Sense Concept Paper
This paper summarizes some of the technical background, research ideas, and
possible development strategies for achieving machine common sense. Machine
common sense has long been a critical-but-missing component of Artificial
Intelligence (AI). Recent advances in machine learning have resulted in new AI
capabilities, but in all of these applications, machine reasoning is narrow and
highly specialized. Developers must carefully train or program systems for
every situation. General commonsense reasoning remains elusive. The absence of
common sense prevents intelligent systems from understanding their world,
behaving reasonably in unforeseen situations, communicating naturally with
people, and learning from new experiences. Its absence is perhaps the most
significant barrier between the narrowly focused AI applications we have today
and the more general, human-like AI systems we would like to build in the
future. Machine common sense remains a broad, potentially unbounded problem in
AI. There are a wide range of strategies that could be employed to make
progress on this difficult challenge. This paper discusses two diverse
strategies for focusing development on two different machine commonsense
services: (1) a service that learns from experience, like a child, to construct
computational models that mimic the core domains of child cognition for objects
(intuitive physics), agents (intentional actors), and places (spatial
navigation); and (2) service that learns from reading the Web, like a research
librarian, to construct a commonsense knowledge repository capable of answering
natural language and image-based questions about commonsense phenomena
Temporal Common Sense Acquisition with Minimal Supervision
Temporal common sense (e.g., duration and frequency of events) is crucial for
understanding natural language. However, its acquisition is challenging, partly
because such information is often not expressed explicitly in text, and human
annotation on such concepts is costly. This work proposes a novel sequence
modeling approach that exploits explicit and implicit mentions of temporal
common sense, extracted from a large corpus, to build TACOLM, a temporal common
sense language model. Our method is shown to give quality predictions of
various dimensions of temporal common sense (on UDST and a newly collected
dataset from RealNews). It also produces representations of events for relevant
tasks such as duration comparison, parent-child relations, event coreference
and temporal QA (on TimeBank, HiEVE and MCTACO) that are better than using the
standard BERT. Thus, it will be an important component of temporal NLP.Comment: Accepted by ACL 202
"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
Understanding time is crucial for understanding events expressed in natural
language. Because people rarely say the obvious, it is often necessary to have
commonsense knowledge about various temporal aspects of events, such as
duration, frequency, and temporal order. However, this important problem has so
far received limited attention. This paper systematically studies this temporal
commonsense problem. Specifically, we define five classes of temporal
commonsense, and use crowdsourcing to develop a new dataset, MCTACO, that
serves as a test set for this task. We find that the best current methods used
on MCTACO are still far behind human performance, by about 20%, and discuss
several directions for improvement. We hope that the new dataset and our study
here can foster more future research on this topic.Comment: EMNLP 2019 (short paper). arXiv admin note: text overlap with
arXiv:1908.0492
Do NLP Models Know Numbers? Probing Numeracy in Embeddings
The ability to understand and work with numbers (numeracy) is critical for
many complex reasoning tasks. Currently, most NLP models treat numbers in text
in the same way as other tokens---they embed them as distributed vectors. Is
this enough to capture numeracy? We begin by investigating the numerical
reasoning capabilities of a state-of-the-art question answering model on the
DROP dataset. We find this model excels on questions that require numerical
reasoning, i.e., it already captures numeracy. To understand how this
capability emerges, we probe token embedding methods (e.g., BERT, GloVe) on
synthetic list maximum, number decoding, and addition tasks. A surprising
degree of numeracy is naturally present in standard embeddings. For example,
GloVe and word2vec accurately encode magnitude for numbers up to 1,000.
Furthermore, character-level embeddings are even more precise---ELMo captures
numeracy the best for all pre-trained methods---but BERT, which uses sub-word
units, is less exact.Comment: EMNLP 201
How Large Are Lions? Inducing Distributions over Quantitative Attributes
Most current NLP systems have little knowledge about quantitative attributes
of objects and events. We propose an unsupervised method for collecting
quantitative information from large amounts of web data, and use it to create a
new, very large resource consisting of distributions over physical quantities
associated with objects, adjectives, and verbs which we call Distributions over
Quantitative (DoQ). This contrasts with recent work in this area which has
focused on making only relative comparisons such as "Is a lion bigger than a
wolf?". Our evaluation shows that DoQ compares favorably with state of the art
results on existing datasets for relative comparisons of nouns and adjectives,
and on a new dataset we introduce
Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches
In the NLP community, recent years have seen a surge of research activities
that address machines' ability to perform deep language understanding which
goes beyond what is explicitly stated in text, rather relying on reasoning and
knowledge of the world. Many benchmark tasks and datasets have been created to
support the development and evaluation of such natural language inference
ability. As these benchmarks become instrumental and a driving force for the
NLP research community, this paper aims to provide an overview of recent
benchmarks, relevant knowledge resources, and state-of-the-art learning and
inference approaches in order to support a better understanding of this growing
field