13,033 research outputs found
Probabilistic Reasoning via Deep Learning: Neural Association Models
In this paper, we propose a new deep learning approach, called neural
association model (NAM), for probabilistic reasoning in artificial
intelligence. We propose to use neural networks to model association between
any two events in a domain. Neural networks take one event as input and compute
a conditional probability of the other event to model how likely these two
events are to be associated. The actual meaning of the conditional
probabilities varies between applications and depends on how the models are
trained. In this work, as two case studies, we have investigated two NAM
structures, namely deep neural networks (DNN) and relation-modulated neural
nets (RMNN), on several probabilistic reasoning tasks in AI, including
recognizing textual entailment, triple classification in multi-relational
knowledge bases and commonsense reasoning. Experimental results on several
popular datasets derived from WordNet, FreeBase and ConceptNet have all
demonstrated that both DNNs and RMNNs perform equally well and they can
significantly outperform the conventional methods available for these reasoning
tasks. Moreover, compared with DNNs, RMNNs are superior in knowledge transfer,
where a pre-trained model can be quickly extended to an unseen relation after
observing only a few training samples. To further prove the effectiveness of
the proposed models, in this work, we have applied NAMs to solving challenging
Winograd Schema (WS) problems. Experiments conducted on a set of WS problems
prove that the proposed models have the potential for commonsense reasoning.Comment: Probabilistic reasoning, Winograd Schema Challenge, Deep learning,
Neural Networks, Distributed Representatio
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
Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
The state-of-the-art pre-trained language representation models, such as
Bidirectional Encoder Representations from Transformers (BERT), rarely
incorporate commonsense knowledge or other knowledge explicitly. We propose a
pre-training approach for incorporating commonsense knowledge into language
representation models. We construct a commonsense-related multi-choice question
answering dataset for pre-training a neural language representation model. The
dataset is created automatically by our proposed "align, mask, and select"
(AMS) method. We also investigate different pre-training tasks. Experimental
results demonstrate that pre-training models using the proposed approach
followed by fine-tuning achieve significant improvements over previous
state-of-the-art models on two commonsense-related benchmarks, including
CommonsenseQA and Winograd Schema Challenge. We also observe that fine-tuned
models after the proposed pre-training approach maintain comparable performance
on other NLP tasks, such as sentence classification and natural language
inference tasks, compared to the original BERT models. These results verify
that the proposed approach, while significantly improving commonsense-related
NLP tasks, does not degrade the general language representation capabilities
WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge
In this paper, we present the first comprehensive categorization of essential
commonsense knowledge for answering the Winograd Schema Challenge (WSC). For
each of the questions, we invite annotators to first provide reasons for making
correct decisions and then categorize them into six major knowledge categories.
By doing so, we better understand the limitation of existing methods (i.e.,
what kind of knowledge cannot be effectively represented or inferred with
existing methods) and shed some light on the commonsense knowledge that we need
to acquire in the future for better commonsense reasoning. Moreover, to
investigate whether current WSC models can understand the commonsense or they
simply solve the WSC questions based on the statistical bias of the dataset, we
leverage the collected reasons to develop a new task called WinoWhy, which
requires models to distinguish plausible reasons from very similar but wrong
reasons for all WSC questions. Experimental results prove that even though
pre-trained language representation models have achieved promising progress on
the original WSC dataset, they are still struggling at WinoWhy. Further
experiments show that even though supervised models can achieve better
performance, the performance of these models can be sensitive to the dataset
distribution. WinoWhy and all codes are available at:
https://github.com/HKUST-KnowComp/WinoWhy.Comment: Accepted by ACL 202
Semantically Enhanced Models for Commonsense Knowledge Acquisition
Commonsense knowledge is paramount to enable intelligent systems. Typically,
it is characterized as being implicit and ambiguous, hindering thereby the
automation of its acquisition. To address these challenges, this paper presents
semantically enhanced models to enable reasoning through resolving part of
commonsense ambiguity. The proposed models enhance in a knowledge graph
embedding (KGE) framework for knowledge base completion. Experimental results
show the effectiveness of the new semantic models in commonsense reasoning
Hybrid Knowledge Routed Modules for Large-scale Object Detection
The dominant object detection approaches treat the recognition of each region
separately and overlook crucial semantic correlations between objects in one
scene. This paradigm leads to substantial performance drop when facing heavy
long-tail problems, where very few samples are available for rare classes and
plenty of confusing categories exists. We exploit diverse human commonsense
knowledge for reasoning over large-scale object categories and reaching
semantic coherency within one image. Particularly, we present Hybrid Knowledge
Routed Modules (HKRM) that incorporates the reasoning routed by two kinds of
knowledge forms: an explicit knowledge module for structured constraints that
are summarized with linguistic knowledge (e.g. shared attributes,
relationships) about concepts; and an implicit knowledge module that depicts
some implicit constraints (e.g. common spatial layouts). By functioning over a
region-to-region graph, both modules can be individualized and adapted to
coordinate with visual patterns in each image, guided by specific knowledge
forms. HKRM are light-weight, general-purpose and extensible by easily
incorporating multiple knowledge to endow any detection networks the ability of
global semantic reasoning. Experiments on large-scale object detection
benchmarks show HKRM obtains around 34.5% improvement on VisualGenome (1000
categories) and 30.4% on ADE in terms of mAP. Codes and trained model can be
found in https://github.com/chanyn/HKRM.Comment: 9 pages, 5 figure
Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning
Fine-tuning of pre-trained transformer models has become the standard
approach for solving common NLP tasks. Most of the existing approaches rely on
a randomly initialized classifier on top of such networks. We argue that this
fine-tuning procedure is sub-optimal as the pre-trained model has no prior on
the specific classifier labels, while it might have already learned an
intrinsic textual representation of the task. In this paper, we introduce a new
scoring method that casts a plausibility ranking task in a full-text format and
leverages the masked language modeling head tuned during the pre-training
phase. We study commonsense reasoning tasks where the model must rank a set of
hypotheses given a premise, focusing on the COPA, Swag, HellaSwag and
CommonsenseQA datasets. By exploiting our scoring method without fine-tuning,
we are able to produce strong baselines (e.g. 80% test accuracy on COPA) that
are comparable to supervised approaches. Moreover, when fine-tuning directly on
the proposed scoring function, we show that our method provides a much more
stable training phase across random restarts (e.g standard
deviation reduction on COPA test accuracy) and requires less annotated data
than the standard classifier approach to reach equivalent performances.Comment: Accepted at ACL 202
RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms
Pre-trained language models (PTLMs) have achieved impressive performance on
commonsense inference benchmarks, but their ability to employ commonsense to
make robust inferences, which is crucial for effective communications with
humans, is debated. In the pursuit of advancing fluid human-AI communication,
we propose a new challenge, RICA: Robust Inference capability based on
Commonsense Axioms, that evaluates robust commonsense inference despite textual
perturbations. To generate data for this challenge, we develop a systematic and
scalable procedure using commonsense knowledge bases and probe PTLMs across two
different evaluation settings. Extensive experiments on our generated probe
sets with more than 10k statements show that PTLMs perform no better than
random guessing on the zero-shot setting, are heavily impacted by statistical
biases, and are not robust to perturbation attacks. We also find that
fine-tuning on similar statements offer limited gains, as PTLMs still fail to
generalize to unseen inferences. Our new large-scale benchmark exposes a
significant gap between PTLMs and human-level language understanding and offers
a new challenge for PTLMs to demonstrate commonsense.Comment: 18 pages, 8 figure
SocialIQA: Commonsense Reasoning about Social Interactions
We introduce Social IQa, the first largescale benchmark for commonsense
reasoning about social situations. Social IQa contains 38,000 multiple choice
questions for probing emotional and social intelligence in a variety of
everyday situations (e.g., Q: "Jordan wanted to tell Tracy a secret, so Jordan
leaned towards Tracy. Why did Jordan do this?" A: "Make sure no one else could
hear"). Through crowdsourcing, we collect commonsense questions along with
correct and incorrect answers about social interactions, using a new framework
that mitigates stylistic artifacts in incorrect answers by asking workers to
provide the right answer to a different but related question. Empirical results
show that our benchmark is challenging for existing question-answering models
based on pretrained language models, compared to human performance (>20% gap).
Notably, we further establish Social IQa as a resource for transfer learning of
commonsense knowledge, achieving state-of-the-art performance on multiple
commonsense reasoning tasks (Winograd Schemas, COPA).Comment: the first two authors contributed equally; accepted to EMNLP 2019;
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Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning
In this work, we aim at equipping pre-trained language models with structured
knowledge. We present two self-supervised tasks learning over raw text with the
guidance from knowledge graphs. Building upon entity-level masked language
models, our first contribution is an entity masking scheme that exploits
relational knowledge underlying the text. This is fulfilled by using a linked
knowledge graph to select informative entities and then masking their mentions.
In addition we use knowledge graphs to obtain distractors for the masked
entities, and propose a novel distractor-suppressed ranking objective which is
optimized jointly with masked language model. In contrast to existing
paradigms, our approach uses knowledge graphs implicitly, only during
pre-training, to inject language models with structured knowledge via learning
from raw text. It is more efficient than retrieval-based methods that perform
entity linking and integration during finetuning and inference, and generalizes
more effectively than the methods that directly learn from concatenated graph
triples. Experiments show that our proposed model achieves improved performance
on five benchmark datasets, including question answering and knowledge base
completion tasks
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