898 research outputs found
Reasoning about Actions and State Changes by Injecting Commonsense Knowledge
Comprehending procedural text, e.g., a paragraph describing photosynthesis,
requires modeling actions and the state changes they produce, so that questions
about entities at different timepoints can be answered. Although several recent
systems have shown impressive progress in this task, their predictions can be
globally inconsistent or highly improbable. In this paper, we show how the
predicted effects of actions in the context of a paragraph can be improved in
two ways: (1) by incorporating global, commonsense constraints (e.g., a
non-existent entity cannot be destroyed), and (2) by biasing reading with
preferences from large-scale corpora (e.g., trees rarely move). Unlike earlier
methods, we treat the problem as a neural structured prediction task, allowing
hard and soft constraints to steer the model away from unlikely predictions. We
show that the new model significantly outperforms earlier systems on a
benchmark dataset for procedural text comprehension (+8% relative gain), and
that it also avoids some of the nonsensical predictions that earlier systems
make.Comment: Accepted at EMNLP 2018. Niket Tandon and Bhavana Dalvi Mishra
contributed equally to this wor
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
Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension
We propose a neural machine-reading model that constructs dynamic knowledge
graphs from procedural text. It builds these graphs recurrently for each step
of the described procedure, and uses them to track the evolving states of
participant entities. We harness and extend a recently proposed machine reading
comprehension (MRC) model to query for entity states, since these states are
generally communicated in spans of text and MRC models perform well in
extracting entity-centric spans. The explicit, structured, and evolving
knowledge graph representations that our model constructs can be used in
downstream question answering tasks to improve machine comprehension of text,
as we demonstrate empirically. On two comprehension tasks from the recently
proposed PROPARA dataset (Dalvi et al., 2018), our model achieves
state-of-the-art results. We further show that our model is competitive on the
RECIPES dataset (Kiddon et al., 2015), suggesting it may be generally
applicable. We present some evidence that the model's knowledge graphs help it
to impose commonsense constraints on its predictions.Comment: ICLR 2019 submissio
Knowledge-Aware Procedural Text Understanding with Multi-Stage Training
Procedural text describes dynamic state changes during a step-by-step natural
process (e.g., photosynthesis). In this work, we focus on the task of
procedural text understanding, which aims to comprehend such documents and
track entities' states and locations during a process. Although recent
approaches have achieved substantial progress, their results are far behind
human performance. Two challenges, the difficulty of commonsense reasoning and
data insufficiency, still remain unsolved, which require the incorporation of
external knowledge bases. Previous works on external knowledge injection
usually rely on noisy web mining tools and heuristic rules with limited
applicable scenarios. In this paper, we propose a novel KnOwledge-Aware
proceduraL text understAnding (KOALA) model, which effectively leverages
multiple forms of external knowledge in this task. Specifically, we retrieve
informative knowledge triples from ConceptNet and perform knowledge-aware
reasoning while tracking the entities. Besides, we employ a multi-stage
training schema which fine-tunes the BERT model over unlabeled data collected
from Wikipedia before further fine-tuning it on the final model. Experimental
results on two procedural text datasets, ProPara and Recipes, verify the
effectiveness of the proposed methods, in which our model achieves
state-of-the-art performance in comparison to various baselines.Comment: Published as full paper in Proceedings of the Web Conference 2021
(WWW'21
Commonsense Properties from Query Logs and Question Answering Forums
Commonsense knowledge about object properties, human behavior and general
concepts is crucial for robust AI applications. However, automatic acquisition
of this knowledge is challenging because of sparseness and bias in online
sources. This paper presents Quasimodo, a methodology and tool suite for
distilling commonsense properties from non-standard web sources. We devise
novel ways of tapping into search-engine query logs and QA forums, and
combining the resulting candidate assertions with statistical cues from
encyclopedias, books and image tags in a corroboration step. Unlike prior work
on commonsense knowledge bases, Quasimodo focuses on salient properties that
are typically associated with certain objects or concepts. Extensive
evaluations, including extrinsic use-case studies, show that Quasimodo provides
better coverage than state-of-the-art baselines with comparable quality.Comment: Updated appendix reporting on Quasimodo v4.3 (2/2021
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
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
Non-extractive commonsense QA remains a challenging AI task, as it requires
systems to reason about, synthesize, and gather disparate pieces of
information, in order to generate responses to queries. Recent approaches on
such tasks show increased performance, only when models are either pre-trained
with additional information or when domain-specific heuristics are used,
without any special consideration regarding the knowledge resource type. In
this paper, we perform a survey of recent commonsense QA methods and we provide
a systematic analysis of popular knowledge resources and knowledge-integration
methods, across benchmarks from multiple commonsense datasets. Our results and
analysis show that attention-based injection seems to be a preferable choice
for knowledge integration and that the degree of domain overlap, between
knowledge bases and datasets, plays a crucial role in determining model
success.Comment: EMNLP-COIN 201
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
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. We introduce an
approach that extends prior work on crowdsourcing commonsense knowledge by
incorporating differences in knowledge that are attributable to cultural or
national groups. We demonstrate the technique by collecting commonsense
knowledge that surrounds six fairly universal rituals -- birth, coming-of-age,
marriage, funerals, new year, and birthdays -- across two national groups: the
United States and India. Our study expands the different types of relationships
identified by existing work in the field of commonsense reasoning for
commonplace events, and uses these new types to gather information that
distinguish the identity of the groups providing the knowledge. It also moves
us a step closer towards building a machine that doesn't assume a rigid
framework of universal (and likely Western-biased) commonsense knowledge, but
rather has the ability to reason in a contextually and culturally sensitive
way. Our hope is that cultural knowledge of this sort will lead to more
human-like performance in NLP tasks such as question answering (QA) and text
understanding and generation.Comment: 9 pages, 9 figure
Understanding in Artificial Intelligence
Current Artificial Intelligence (AI) methods, most based on deep learning,
have facilitated progress in several fields, including computer vision and
natural language understanding. The progress of these AI methods is measured
using benchmarks designed to solve challenging tasks, such as visual question
answering. A question remains of how much understanding is leveraged by these
methods and how appropriate are the current benchmarks to measure understanding
capabilities. To answer these questions, we have analysed existing benchmarks
and their understanding capabilities, defined by a set of understanding
capabilities, and current research streams. We show how progress has been made
in benchmark development to measure understanding capabilities of AI methods
and we review as well how current methods develop understanding capabilities.Comment: 28 pages, 282 reference
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