2,928 research outputs found
Variational Reasoning for Question Answering with Knowledge Graph
Knowledge graph (KG) is known to be helpful for the task of question
answering (QA), since it provides well-structured relational information
between entities, and allows one to further infer indirect facts. However, it
is challenging to build QA systems which can learn to reason over knowledge
graphs based on question-answer pairs alone. First, when people ask questions,
their expressions are noisy (for example, typos in texts, or variations in
pronunciations), which is non-trivial for the QA system to match those
mentioned entities to the knowledge graph. Second, many questions require
multi-hop logic reasoning over the knowledge graph to retrieve the answers. To
address these challenges, we propose a novel and unified deep learning
architecture, and an end-to-end variational learning algorithm which can handle
noise in questions, and learn multi-hop reasoning simultaneously. Our method
achieves state-of-the-art performance on a recent benchmark dataset in the
literature. We also derive a series of new benchmark datasets, including
questions for multi-hop reasoning, questions paraphrased by neural translation
model, and questions in human voice. Our method yields very promising results
on all these challenging datasets
Variational Knowledge Graph Reasoning
Inferring missing links in knowledge graphs (KG) has attracted a lot of
attention from the research community. In this paper, we tackle a practical
query answering task involving predicting the relation of a given entity pair.
We frame this prediction problem as an inference problem in a probabilistic
graphical model and aim at resolving it from a variational inference
perspective. In order to model the relation between the query entity pair, we
assume that there exists an underlying latent variable (paths connecting two
nodes) in the KG, which carries the equivalent semantics of their relations.
However, due to the intractability of connections in large KGs, we propose to
use variation inference to maximize the evidence lower bound. More
specifically, our framework (\textsc{Diva}) is composed of three modules, i.e.
a posterior approximator, a prior (path finder), and a likelihood (path
reasoner). By using variational inference, we are able to incorporate them
closely into a unified architecture and jointly optimize them to perform KG
reasoning. With active interactions among these sub-modules, \textsc{Diva} is
better at handling noise and coping with more complex reasoning scenarios. In
order to evaluate our method, we conduct the experiment of the link prediction
task on multiple datasets and achieve state-of-the-art performances on both
datasets.Comment: Accepted to NAACL 201
UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering
In relation extraction for knowledge-based question answering, searching from
one entity to another entity via a single relation is called "one hop". In
related work, an exhaustive search from all one-hop relations, two-hop
relations, and so on to the max-hop relations in the knowledge graph is
necessary but expensive. Therefore, the number of hops is generally restricted
to two or three. In this paper, we propose UHop, an unrestricted-hop framework
which relaxes this restriction by use of a transition-based search framework to
replace the relation-chain-based search one. We conduct experiments on
conventional 1- and 2-hop questions as well as lengthy questions, including
datasets such as WebQSP, PathQuestion, and Grid World. Results show that the
proposed framework enables the ability to halt, works well with
state-of-the-art models, achieves competitive performance without exhaustive
searches, and opens the performance gap for long relation paths.Comment: To appear in NAACL-HLT 201
Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document Traversal
Reading Comprehension has received significant attention in recent years as
high quality Question Answering (QA) datasets have become available. Despite
state-of-the-art methods achieving strong overall accuracy, Multi-Hop (MH)
reasoning remains particularly challenging. To address MH-QA specifically, we
propose a Deep Reinforcement Learning based method capable of learning
sequential reasoning across large collections of documents so as to pass a
query-aware, fixed-size context subset to existing models for answer
extraction. Our method is comprised of two stages: a linker, which decomposes
the provided support documents into a graph of sentences, and an extractor,
which learns where to look based on the current question and already-visited
sentences. The result of the linker is a novel graph structure at the sentence
level that preserves logical flow while still allowing rapid movement between
documents. Importantly, we demonstrate that the sparsity of the resultant graph
is invariant to context size. This translates to fewer decisions required from
the Deep-RL trained extractor, allowing the system to scale effectively to
large collections of documents.
The importance of sequential decision making in the document traversal step
is demonstrated by comparison to standard IE methods, and we additionally
introduce a BM25-based IR baseline that retrieves documents relevant to the
query only. We examine the integration of our method with existing models on
the recently proposed QAngaroo benchmark and achieve consistent increases in
accuracy across the board, as well as a 2-3x reduction in training time
KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings
The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question
answering (QA) has been recently released. ARC only contains natural science
questions authored for human exams, which are hard to answer and require
advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art
QA systems fail to significantly outperform random baseline, reflecting the
difficult nature of this task. In this paper, we propose a novel framework for
answering science exam questions, which mimics human solving process in an
open-book exam. To address the reasoning challenge, we construct contextual
knowledge graphs respectively for the question itself and supporting sentences.
Our model learns to reason with neural embeddings of both knowledge graphs.
Experiments on the ARC Challenge Set show that our model outperforms the
previous state-of-the-art QA systems
Multi-step Reasoning via Recurrent Dual Attention for Visual Dialog
This paper presents a new model for visual dialog, Recurrent Dual Attention
Network (ReDAN), using multi-step reasoning to answer a series of questions
about an image. In each question-answering turn of a dialog, ReDAN infers the
answer progressively through multiple reasoning steps. In each step of the
reasoning process, the semantic representation of the question is updated based
on the image and the previous dialog history, and the recurrently-refined
representation is used for further reasoning in the subsequent step. On the
VisDial v1.0 dataset, the proposed ReDAN model achieves a new state-of-the-art
of 64.47% NDCG score. Visualization on the reasoning process further
demonstrates that ReDAN can locate context-relevant visual and textual clues
via iterative refinement, which can lead to the correct answer step-by-step.Comment: Accepted to ACL 201
Knowledge Authoring and Question Answering with KALM
Knowledge representation and reasoning (KRR) is one of the key areas in
artificial intelligence (AI) field. It is intended to represent the world
knowledge in formal languages (e.g., Prolog, SPARQL) and then enhance the
expert systems to perform querying and inference tasks. Currently, constructing
large scale knowledge bases (KBs) with high quality is prohibited by the fact
that the construction process requires many qualified knowledge engineers who
not only understand the domain-specific knowledge but also have sufficient
skills in knowledge representation. Unfortunately, qualified knowledge
engineers are in short supply. Therefore, it would be very useful to build a
tool that allows the user to construct and query the KB simply via text.
Although there is a number of systems developed for knowledge extraction and
question answering, they mainly fail in that these system don't achieve high
enough accuracy whereas KRR is highly sensitive to erroneous data. In this
thesis proposal, I will present Knowledge Authoring Logic Machine (KALM), a
rule-based system which allows the user to author knowledge and query the KB in
text. The experimental results show that KALM achieved superior accuracy in
knowledge authoring and question answering as compared to the state-of-the-art
systems.Comment: In Proceedings ICLP 2019, arXiv:1909.0764
Semi-Automatic Terminology Ontology Learning Based on Topic Modeling
Ontologies provide features like a common vocabulary, reusability,
machine-readable content, and also allows for semantic search, facilitate agent
interaction and ordering & structuring of knowledge for the Semantic Web (Web
3.0) application. However, the challenge in ontology engineering is automatic
learning, i.e., the there is still a lack of fully automatic approach from a
text corpus or dataset of various topics to form ontology using machine
learning techniques. In this paper, two topic modeling algorithms are explored,
namely LSI & SVD and Mr.LDA for learning topic ontology. The objective is to
determine the statistical relationship between document and terms to build a
topic ontology and ontology graph with minimum human intervention. Experimental
analysis on building a topic ontology and semantic retrieving corresponding
topic ontology for the user's query demonstrating the effectiveness of the
proposed approach
Factor Graph Attention
Dialog is an effective way to exchange information, but subtle details and
nuances are extremely important. While significant progress has paved a path to
address visual dialog with algorithms, details and nuances remain a challenge.
Attention mechanisms have demonstrated compelling results to extract details in
visual question answering and also provide a convincing framework for visual
dialog due to their interpretability and effectiveness. However, the many data
utilities that accompany visual dialog challenge existing attention techniques.
We address this issue and develop a general attention mechanism for visual
dialog which operates on any number of data utilities. To this end, we design a
factor graph based attention mechanism which combines any number of utility
representations. We illustrate the applicability of the proposed approach on
the challenging and recently introduced VisDial datasets, outperforming recent
state-of-the-art methods by 1.1% for VisDial0.9 and by 2% for VisDial1.0 on
MRR. Our ensemble model improved the MRR score on VisDial1.0 by more than 6%.Comment: Accepted to CVPR 2019; revised version includes bottom-up feature
Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering
We propose a new class of probabilistic neural-symbolic models, that have
symbolic functional programs as a latent, stochastic variable. Instantiated in
the context of visual question answering, our probabilistic formulation offers
two key conceptual advantages over prior neural-symbolic models for VQA.
Firstly, the programs generated by our model are more understandable while
requiring lesser number of teaching examples. Secondly, we show that one can
pose counterfactual scenarios to the model, to probe its beliefs on the
programs that could lead to a specified answer given an image. Our results on
the CLEVR and SHAPES datasets verify our hypotheses, showing that the model
gets better program (and answer) prediction accuracy even in the low data
regime, and allows one to probe the coherence and consistency of reasoning
performed.Comment: ICML 2019 Camera Ready + Appendi
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