2,136 research outputs found
SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers
This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7
on Semantic Relation Extraction and Classification in Scientific Papers. First
we extract the shortest dependency path (sdp) between two entities, then we
introduce a convolutional neural network (CNN) which takes the shortest
dependency path embeddings as input and performs relation classification with
differing objectives for each subtask of the shared task. This approach
achieved overall F1 scores of 76.7 and 83.2 for relation classification on
clean and noisy data, respectively. Furthermore, for combined relation
extraction and classification on clean data, it obtained F1 scores of 37.4 and
33.6 for each phase. Our system ranks 3rd in all three sub-tasks of the shared
task
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
We present a neural approach called IRNet for complex and cross-domain
Text-to-SQL. IRNet aims to address two challenges: 1) the mismatch between
intents expressed in natural language (NL) and the implementation details in
SQL; 2) the challenge in predicting columns caused by the large number of
out-of-domain words. Instead of end-to-end synthesizing a SQL query, IRNet
decomposes the synthesis process into three phases. In the first phase, IRNet
performs a schema linking over a question and a database schema. Then, IRNet
adopts a grammar-based neural model to synthesize a SemQL query which is an
intermediate representation that we design to bridge NL and SQL. Finally, IRNet
deterministically infers a SQL query from the synthesized SemQL query with
domain knowledge. On the challenging Text-to-SQL benchmark Spider, IRNet
achieves 46.7% accuracy, obtaining 19.5% absolute improvement over previous
state-of-the-art approaches. At the time of writing, IRNet achieves the first
position on the Spider leaderboard.Comment: To appear in ACL 201
Simple Question Answering with Subgraph Ranking and Joint-Scoring
Knowledge graph based simple question answering (KBSQA) is a major area of
research within question answering. Although only dealing with simple
questions, i.e., questions that can be answered through a single knowledge base
(KB) fact, this task is neither simple nor close to being solved. Targeting on
the two main steps, subgraph selection and fact selection, the research
community has developed sophisticated approaches. However, the importance of
subgraph ranking and leveraging the subject--relation dependency of a KB fact
have not been sufficiently explored. Motivated by this, we present a unified
framework to describe and analyze existing approaches. Using this framework as
a starting point, we focus on two aspects: improving subgraph selection through
a novel ranking method and leveraging the subject--relation dependency by
proposing a joint scoring CNN model with a novel loss function that enforces
the well-order of scores. Our methods achieve a new state of the art (85.44% in
accuracy) on the SimpleQuestions dataset.Comment: Accepted by The 2019 Annual Conference of the North American Chapter
of the Association for Computational Linguistics (NAACL-HLT 2019). 11 pages,
1 figur
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers
Solving mathematical word problems (MWPs) automatically is challenging,
primarily due to the semantic gap between human-readable words and
machine-understandable logics. Despite the long history dated back to the1960s,
MWPs have regained intensive attention in the past few years with the
advancement of Artificial Intelligence (AI). Solving MWPs successfully is
considered as a milestone towards general AI. Many systems have claimed
promising results in self-crafted and small-scale datasets. However, when
applied on large and diverse datasets, none of the proposed methods in the
literature achieves high precision, revealing that current MWP solvers still
have much room for improvement. This motivated us to present a comprehensive
survey to deliver a clear and complete picture of automatic math problem
solvers. In this survey, we emphasize on algebraic word problems, summarize
their extracted features and proposed techniques to bridge the semantic gap and
compare their performance in the publicly accessible datasets. We also cover
automatic solvers for other types of math problems such as geometric problems
that require the understanding of diagrams. Finally, we identify several
emerging research directions for the readers with interests in MWPs.Comment: 18 pages, 5 figure
Towards Open Intent Discovery for Conversational Text
Detecting and identifying user intent from text, both written and spoken,
plays an important role in modelling and understand dialogs. Existing research
for intent discovery model it as a classification task with a predefined set of
known categories. To generailze beyond these preexisting classes, we define a
new task of \textit{open intent discovery}. We investigate how intent can be
generalized to those not seen during training. To this end, we propose a
two-stage approach to this task - predicting whether an utterance contains an
intent, and then tagging the intent in the input utterance. Our model consists
of a bidirectional LSTM with a CRF on top to capture contextual semantics,
subject to some constraints. Self-attention is used to learn long distance
dependencies. Further, we adapt an adversarial training approach to improve
robustness and perforamce across domains. We also present a dataset of 25k
real-life utterances that have been labelled via crowd sourcing. Our
experiments across different domains and real-world datasets show the
effectiveness of our approach, with less than 100 annotated examples needed per
unique domain to recognize diverse intents. The approach outperforms
state-of-the-art baselines by 5-15% F1 score points
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
One-shot Learning for Question-Answering in Gaokao History Challenge
Answering questions from university admission exams (Gaokao in Chinese) is a
challenging AI task since it requires effective representation to capture
complicated semantic relations between questions and answers. In this work, we
propose a hybrid neural model for deep question-answering task from history
examinations. Our model employs a cooperative gated neural network to retrieve
answers with the assistance of extra labels given by a neural turing machine
labeler. Empirical study shows that the labeler works well with only a small
training dataset and the gated mechanism is good at fetching the semantic
representation of lengthy answers. Experiments on question answering
demonstrate the proposed model obtains substantial performance gains over
various neural model baselines in terms of multiple evaluation metrics.Comment: Proceedings of the 27th International Conference on Computational
Linguistics (COLING 2018
Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF
Sentence representation at the semantic level is a challenging task for
Natural Language Processing and Artificial Intelligence. Despite the advances
in word embeddings (i.e. word vector representations), capturing sentence
meaning is an open question due to complexities of semantic interactions among
words. In this paper, we present an embedding method, which is aimed at
learning unsupervised sentence representations from unlabeled text. We propose
an unsupervised method that models a sentence as a weighted series of word
embeddings. The weights of the word embeddings are fitted by using Shannon's
word entropies provided by the Term Frequency--Inverse Document Frequency
(TF--IDF) transform. The hyperparameters of the model can be selected according
to the properties of data (e.g. sentence length and textual gender).
Hyperparameter selection involves word embedding methods and dimensionalities,
as well as weighting schemata. Our method offers advantages over existing
methods: identifiable modules, short-term training, online inference of
(unseen) sentence representations, as well as independence from domain,
external knowledge and language resources. Results showed that our model
outperformed the state of the art in well-known Semantic Textual Similarity
(STS) benchmarks. Moreover, our model reached state-of-the-art performance when
compared to supervised and knowledge-based STS systems
The Natural Language Decathlon: Multitask Learning as Question Answering
Deep learning has improved performance on many natural language processing
(NLP) tasks individually. However, general NLP models cannot emerge within a
paradigm that focuses on the particularities of a single metric, dataset, and
task. We introduce the Natural Language Decathlon (decaNLP), a challenge that
spans ten tasks: question answering, machine translation, summarization,
natural language inference, sentiment analysis, semantic role labeling,
zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and
commonsense pronoun resolution. We cast all tasks as question answering over a
context. Furthermore, we present a new Multitask Question Answering Network
(MQAN) jointly learns all tasks in decaNLP without any task-specific modules or
parameters in the multitask setting. MQAN shows improvements in transfer
learning for machine translation and named entity recognition, domain
adaptation for sentiment analysis and natural language inference, and zero-shot
capabilities for text classification. We demonstrate that the MQAN's
multi-pointer-generator decoder is key to this success and performance further
improves with an anti-curriculum training strategy. Though designed for
decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic
parsing task in the single-task setting. We also release code for procuring and
processing data, training and evaluating models, and reproducing all
experiments for decaNLP
Deep Short Text Classification with Knowledge Powered Attention
Short text classification is one of important tasks in Natural Language
Processing (NLP). Unlike paragraphs or documents, short texts are more
ambiguous since they have not enough contextual information, which poses a
great challenge for classification. In this paper, we retrieve knowledge from
external knowledge source to enhance the semantic representation of short
texts. We take conceptual information as a kind of knowledge and incorporate it
into deep neural networks. For the purpose of measuring the importance of
knowledge, we introduce attention mechanisms and propose deep Short Text
Classification with Knowledge powered Attention (STCKA). We utilize Concept
towards Short Text (C- ST) attention and Concept towards Concept Set (C-CS)
attention to acquire the weight of concepts from two aspects. And we classify a
short text with the help of conceptual information. Unlike traditional
approaches, our model acts like a human being who has intrinsic ability to make
decisions based on observation (i.e., training data for machines) and pays more
attention to important knowledge. We also conduct extensive experiments on four
public datasets for different tasks. The experimental results and case studies
show that our model outperforms the state-of-the-art methods, justifying the
effectiveness of knowledge powered attention
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