10,114 research outputs found
Indirect supervision for relation extraction using question-answer pairs
Automatic relation extraction (RE) for types of interest is of great importance for interpreting massive text corpora in an efficient manner. For example, we want to identify the relationship "president_of" between entities "Donald Trump" and "United States" in a sentence expressing such a relation. Traditional RE models have heavily relied on human-annotated corpus for training, which can be costly in generating labeled data and become obstacles when dealing with more relation types. Thus, more RE extraction systems have shifted to be built upon training data automatically acquired by linking to knowledge bases (distant supervision). However, due to the incompleteness of knowledge bases and the context-agnostic labeling, the training data collected via distant supervision (DS) can be very noisy. In recent years, as increasing attention has been brought to tackling question-answering (QA) tasks, user feedback or datasets of such tasks become more accessible. In this paper, we propose a novel framework, ReQuest, to leverage question-answer pairs as an indirect source of supervision for relation extraction, and study how to use such supervision to reduce noise induced from DS. Our model jointly embeds relation mentions, types, QA entity mention pairs and text features in two low-dimensional spaces (RE and QA), where objects with same relation types or semantically similar question-answer pairs have similar representations. Shared features connect these two spaces, carrying clearer semantic knowledge from both sources. ReQuest, then use these learned embeddings to estimate the types of test relation mentions. We formulate a global objective function and adopt a novel margin-based QA loss to reduce noise in DS by exploiting semantic evidence from the QA dataset. Our experimental results achieve an average of 11% improvement in F1 score on two public RE datasets combined with TREC QA dataset. Codes and datasets can be downloaded at https://github.com/ellenmellon/ReQuest
Relation Discovery with Out-of-Relation Knowledge Base as Supervision
Unsupervised relation discovery aims to discover new relations from a given
text corpus without annotated data. However, it does not consider existing
human annotated knowledge bases even when they are relevant to the relations to
be discovered. In this paper, we study the problem of how to use
out-of-relation knowledge bases to supervise the discovery of unseen relations,
where out-of-relation means that relations to discover from the text corpus and
those in knowledge bases are not overlapped. We construct a set of constraints
between entity pairs based on the knowledge base embedding and then incorporate
constraints into the relation discovery by a variational auto-encoder based
algorithm. Experiments show that our new approach can improve the
state-of-the-art relation discovery performance by a large margin.Comment: Aceepted by NAACL-HLT 201
Question Answering with Subgraph Embeddings
This paper presents a system which learns to answer questions on a broad
range of topics from a knowledge base using few hand-crafted features. Our
model learns low-dimensional embeddings of words and knowledge base
constituents; these representations are used to score natural language
questions against candidate answers. Training our system using pairs of
questions and structured representations of their answers, and pairs of
question paraphrases, yields competitive results on a competitive benchmark of
the literature
Machine Learning with World Knowledge: The Position and Survey
Machine learning has become pervasive in multiple domains, impacting a wide
variety of applications, such as knowledge discovery and data mining, natural
language processing, information retrieval, computer vision, social and health
informatics, ubiquitous computing, etc. Two essential problems of machine
learning are how to generate features and how to acquire labels for machines to
learn. Particularly, labeling large amount of data for each domain-specific
problem can be very time consuming and costly. It has become a key obstacle in
making learning protocols realistic in applications. In this paper, we will
discuss how to use the existing general-purpose world knowledge to enhance
machine learning processes, by enriching the features or reducing the labeling
work. We start from the comparison of world knowledge with domain-specific
knowledge, and then introduce three key problems in using world knowledge in
learning processes, i.e., explicit and implicit feature representation,
inference for knowledge linking and disambiguation, and learning with direct or
indirect supervision. Finally we discuss the future directions of this research
topic
QuASE: Question-Answer Driven Sentence Encoding
Question-answering (QA) data often encodes essential information in many
facets. This paper studies a natural question: Can we get supervision from QA
data for other tasks (typically, non-QA ones)? For example, {\em can we use
QAMR (Michael et al., 2017) to improve named entity recognition?} We suggest
that simply further pre-training BERT is often not the best option, and propose
the {\em question-answer driven sentence encoding (QuASE)} framework. QuASE
learns representations from QA data, using BERT or other state-of-the-art
contextual language models. In particular, we observe the need to distinguish
between two types of sentence encodings, depending on whether the target task
is a single- or multi-sentence input; in both cases, the resulting encoding is
shown to be an easy-to-use plugin for many downstream tasks. This work may
point out an alternative way to supervise NLP tasks
Open Information Extraction from Question-Answer Pairs
Open Information Extraction (OpenIE) extracts meaningful structured tuples
from free-form text. Most previous work on OpenIE considers extracting data
from one sentence at a time. We describe NeurON, a system for extracting tuples
from question-answer pairs. Since real questions and answers often contain
precisely the information that users care about, such information is
particularly desirable to extend a knowledge base with.
NeurON addresses several challenges. First, an answer text is often hard to
understand without knowing the question, and second, relevant information can
span multiple sentences. To address these, NeurON formulates extraction as a
multi-source sequence-to-sequence learning task, wherein it combines
distributed representations of a question and an answer to generate knowledge
facts. We describe experiments on two real-world datasets that demonstrate that
NeurON can find a significant number of new and interesting facts to extend a
knowledge base compared to state-of-the-art OpenIE methods.Comment: NAACL 201
Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus
In Web search, entity-seeking queries often trigger a special Question
Answering (QA) system. It may use a parser to interpret the question to a
structured query, execute that on a knowledge graph (KG), and return direct
entity responses. QA systems based on precise parsing tend to be brittle: minor
syntax variations may dramatically change the response. Moreover, KG coverage
is patchy. At the other extreme, a large corpus may provide broader coverage,
but in an unstructured, unreliable form. We present AQQUCN, a QA system that
gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of
query syntax, between well-formed questions to short `telegraphic' keyword
sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals
from KGs and large corpora to directly rank KG entities, rather than commit to
one semantic interpretation of the query. AQQUCN models the ideal
interpretation as an unobservable or latent variable. Interpretations and
candidate entity responses are scored as pairs, by combining signals from
multiple convolutional networks that operate collectively on the query, KG and
corpus. On four public query workloads, amounting to over 8,000 queries with
diverse query syntax, we see 5--16% absolute improvement in mean average
precision (MAP), compared to the entity ranking performance of recent systems.
Our system is also competitive at entity set retrieval, almost doubling F1
scores for challenging short queries.Comment: Accepted to Information Retrieval Journa
Neural Architecture for Question Answering Using a Knowledge Graph and Web Corpus
In Web search, entity-seeking queries often trigger a special Question
Answering (QA) system. It may use a parser to interpret the question to a
structured query, execute that on a knowledge graph (KG), and return direct
entity responses. QA systems based on precise parsing tend to be brittle: minor
syntax variations may dramatically change the response. Moreover, KG coverage
is patchy. At the other extreme, a large corpus may provide broader coverage,
but in an unstructured, unreliable form. We present AQQUCN, a QA system that
gracefully combines KG and corpus evidence. AQQUCN accepts a broad spectrum of
query syntax, between well-formed questions to short `telegraphic' keyword
sequences. In the face of inherent query ambiguities, AQQUCN aggregates signals
from KGs and large corpora to directly rank KG entities, rather than commit to
one semantic interpretation of the query. AQQUCN models the ideal
interpretation as an unobservable or latent variable. Interpretations and
candidate entity responses are scored as pairs, by combining signals from
multiple convolutional networks that operate collectively on the query, KG and
corpus. On four public query workloads, amounting to over 8,000 queries with
diverse query syntax, we see 5--16% absolute improvement in mean average
precision (MAP), compared to the entity ranking performance of recent systems.
Our system is also competitive at entity set retrieval, almost doubling F1
scores for challenging short queries.Comment: Accepted to Information Retrieval Journa
Knowledge Efficient Deep Learning for Natural Language Processing
Deep learning has become the workhorse for a wide range of natural language
processing applications. But much of the success of deep learning relies on
annotated examples. Annotation is time-consuming and expensive to produce at
scale. Here we are interested in methods for reducing the required quantity of
annotated data -- by making the learning methods more knowledge efficient so as
to make them more applicable in low annotation (low resource) settings. There
are various classical approaches to making the models more knowledge efficient
such as multi-task learning, transfer learning, weakly supervised and
unsupervised learning etc. This thesis focuses on adapting such classical
methods to modern deep learning models and algorithms.
This thesis describes four works aimed at making machine learning models more
knowledge efficient. First, we propose a knowledge rich deep learning model
(KRDL) as a unifying learning framework for incorporating prior knowledge into
deep models. In particular, we apply KRDL built on Markov logic networks to
denoise weak supervision. Second, we apply a KRDL model to assist the machine
reading models to find the correct evidence sentences that can support their
decision. Third, we investigate the knowledge transfer techniques in
multilingual setting, where we proposed a method that can improve pre-trained
multilingual BERT based on the bilingual dictionary. Fourth, we present an
episodic memory network for language modelling, in which we encode the large
external knowledge for the pre-trained GPT.Comment: Ph.D thesi
Visual Relationship Detection using Scene Graphs: A Survey
Understanding a scene by decoding the visual relationships depicted in an
image has been a long studied problem. While the recent advances in deep
learning and the usage of deep neural networks have achieved near human
accuracy on many tasks, there still exists a pretty big gap between human and
machine level performance when it comes to various visual relationship
detection tasks. Developing on earlier tasks like object recognition,
segmentation and captioning which focused on a relatively coarser image
understanding, newer tasks have been introduced recently to deal with a finer
level of image understanding. A Scene Graph is one such technique to better
represent a scene and the various relationships present in it. With its wide
number of applications in various tasks like Visual Question Answering,
Semantic Image Retrieval, Image Generation, among many others, it has proved to
be a useful tool for deeper and better visual relationship understanding. In
this paper, we present a detailed survey on the various techniques for scene
graph generation, their efficacy to represent visual relationships and how it
has been used to solve various downstream tasks. We also attempt to analyze the
various future directions in which the field might advance in the future. Being
one of the first papers to give a detailed survey on this topic, we also hope
to give a succinct introduction to scene graphs, and guide practitioners while
developing approaches for their applications
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