2,788 research outputs found
QAGCN: A Graph Convolutional Network-based Multi-Relation Question Answering System
Answering multi-relation questions over knowledge graphs is a challenging task as it requires multi-step reasoning over a huge number of possible paths. Reasoning-based methods with complex reasoning mechanisms, such as reinforcement learning-based sequential decision making, have been regarded as the default pathway for this task. However, these mechanisms are difficult to implement and train, which hampers their reproducibility and transferability to new domains. In this paper, we propose QAGCN - a simple but effective and novel model that leverages attentional graph convolutional networks that can perform multi-step reasoning during the encoding of knowledge graphs. As a consequence, complex reasoning mechanisms are avoided. In addition, to improve efficiency, we retrieve answers using highly-efficient embedding computations and, for better interpretability, we extract interpretable paths for returned answers. On widely adopted benchmark datasets, the proposed model has been demonstrated competitive against state-of-the-art methods that rely on complex reasoning mechanisms. We also conducted extensive experiments to scrutinize the efficiency and contribution of each component of our model
Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering
Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer
entity in a knowledge base which is several hops from the topic entity
mentioned in the question. Existing Retrieval-based approaches first generate
instructions from the question and then use them to guide the multi-hop
reasoning on the knowledge graph. As the instructions are fixed during the
whole reasoning procedure and the knowledge graph is not considered in
instruction generation, the model cannot revise its mistake once it predicts an
intermediate entity incorrectly. To handle this, we propose KBIGER(Knowledge
Base Iterative Instruction GEnerating and Reasoning), a novel and efficient
approach to generate the instructions dynamically with the help of reasoning
graph. Instead of generating all the instructions before reasoning, we take the
(k-1)-th reasoning graph into consideration to build the k-th instruction. In
this way, the model could check the prediction from the graph and generate new
instructions to revise the incorrect prediction of intermediate entities. We do
experiments on two multi-hop KBQA benchmarks and outperform the existing
approaches, becoming the new-state-of-the-art. Further experiments show our
method does detect the incorrect prediction of intermediate entities and has
the ability to revise such errors.Comment: Accepted by NLPCC 2022(oral
KQA Pro: A Large-Scale Dataset with Interpretable Programs and Accurate SPARQLs for Complex Question Answering over Knowledge Base
Complex question answering over knowledge base (Complex KBQA) is challenging
because it requires various compositional reasoning capabilities, such as
multi-hop inference, attribute comparison, set operation, and etc. Existing
benchmarks have some shortcomings that limit the development of Complex KBQA:
1) they only provide QA pairs without explicit reasoning processes; 2)
questions are either generated by templates, leading to poor diversity, or on a
small scale. To this end, we introduce KQA Pro, a large-scale dataset for
Complex KBQA. We define a compositional and highly-interpretable formal format,
named Program, to represent the reasoning process of complex questions. We
propose compositional strategies to generate questions, corresponding SPARQLs,
and Programs with a small number of templates, and then paraphrase the
generated questions to natural language questions (NLQ) by crowdsourcing,
giving rise to around 120K diverse instances. SPARQL and Program depict two
complementary solutions to answer complex questions, which can benefit a large
spectrum of QA methods. Besides the QA task, KQA Pro can also serves for the
semantic parsing task. As far as we know, it is currently the largest corpus of
NLQ-to-SPARQL and NLQ-to-Program. We conduct extensive experiments to evaluate
whether machines can learn to answer our complex questions in different cases,
that is, with only QA supervision or with intermediate SPARQL/Program
supervision. We find that state-of-the-art KBQA methods learnt from only QA
pairs perform very poor on our dataset, implying our questions are more
challenging than previous datasets. However, pretrained models learnt from our
NLQ-to-SPARQL and NLQ-to-Program annotations surprisingly achieve about 90\%
answering accuracy, which is even close to the human expert performance..
Complex Knowledge Base Question Answering: A Survey
Knowledge base question answering (KBQA) aims to answer a question over a
knowledge base (KB). Early studies mainly focused on answering simple questions
over KBs and achieved great success. However, their performance on complex
questions is still far from satisfactory. Therefore, in recent years,
researchers propose a large number of novel methods, which looked into the
challenges of answering complex questions. In this survey, we review recent
advances on KBQA with the focus on solving complex questions, which usually
contain multiple subjects, express compound relations, or involve numerical
operations. In detail, we begin with introducing the complex KBQA task and
relevant background. Then, we describe benchmark datasets for complex KBQA task
and introduce the construction process of these datasets. Next, we present two
mainstream categories of methods for complex KBQA, namely semantic
parsing-based (SP-based) methods and information retrieval-based (IR-based)
methods. Specifically, we illustrate their procedures with flow designs and
discuss their major differences and similarities. After that, we summarize the
challenges that these two categories of methods encounter when answering
complex questions, and explicate advanced solutions and techniques used in
existing work. Finally, we conclude and discuss several promising directions
related to complex KBQA for future research.Comment: 20 pages, 4 tables, 7 figures. arXiv admin note: text overlap with
arXiv:2105.1164
A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare
Reinforcement learning (RL) has emerged as a powerful approach for tackling
complex medical decision-making problems such as treatment planning,
personalized medicine, and optimizing the scheduling of surgeries and
appointments. It has gained significant attention in the field of Natural
Language Processing (NLP) due to its ability to learn optimal strategies for
tasks such as dialogue systems, machine translation, and question-answering.
This paper presents a review of the RL techniques in NLP, highlighting key
advancements, challenges, and applications in healthcare. The review begins by
visualizing a roadmap of machine learning and its applications in healthcare.
And then it explores the integration of RL with NLP tasks. We examined dialogue
systems where RL enables the learning of conversational strategies, RL-based
machine translation models, question-answering systems, text summarization, and
information extraction. Additionally, ethical considerations and biases in
RL-NLP systems are addressed
Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base
We consider the problem of conversational question answering over a
large-scale knowledge base. To handle huge entity vocabulary of a large-scale
knowledge base, recent neural semantic parsing based approaches usually
decompose the task into several subtasks and then solve them sequentially,
which leads to following issues: 1) errors in earlier subtasks will be
propagated and negatively affect downstream ones; and 2) each subtask cannot
naturally share supervision signals with others. To tackle these issues, we
propose an innovative multi-task learning framework where a pointer-equipped
semantic parsing model is designed to resolve coreference in conversations, and
naturally empower joint learning with a novel type-aware entity detection
model. The proposed framework thus enables shared supervisions and alleviates
the effect of error propagation. Experiments on a large-scale conversational
question answering dataset containing 1.6M question answering pairs over 12.8M
entities show that the proposed framework improves overall F1 score from 67% to
79% compared with previous state-of-the-art work
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