47 research outputs found
Effective LSTMs for Target-Dependent Sentiment Classification
Target-dependent sentiment classification remains a challenge: modeling the
semantic relatedness of a target with its context words in a sentence.
Different context words have different influences on determining the sentiment
polarity of a sentence towards the target. Therefore, it is desirable to
integrate the connections between target word and context words when building a
learning system. In this paper, we develop two target dependent long short-term
memory (LSTM) models, where target information is automatically taken into
account. We evaluate our methods on a benchmark dataset from Twitter. Empirical
results show that modeling sentence representation with standard LSTM does not
perform well. Incorporating target information into LSTM can significantly
boost the classification accuracy. The target-dependent LSTM models achieve
state-of-the-art performances without using syntactic parser or external
sentiment lexicons.Comment: 7 pages, 3 figures published in COLING 201
Emotion Analysis Platform on Chinese Microblog
Weibo, as the largest social media service in China, has billions of messages
generated every day. The huge number of messages contain rich sentimental
information. In order to analyze the emotional changes in accordance with time
and space, this paper presents an Emotion Analysis Platform (EAP), which
explores the emotional distribution of each province, so that can monitor the
global pulse of each province in China. The massive data of Weibo and the
real-time requirements make the building of EAP challenging. In order to solve
the above problems, emoticons, emotion lexicon and emotion-shifting rules are
adopted in EAP to analyze the emotion of each tweet. In order to verify the
effectiveness of the platform, case study on the Sichuan earthquake is done,
and the analysis result of the platform accords with the fact. In order to
analyze from quantity, we manually annotate a test set and conduct experiment
on it. The experimental results show that the macro-Precision of EAP reaches
80% and the EAP works effectively.Comment: 11 pages, 6 figure
Question Answering and Question Generation as Dual Tasks
We study the problem of joint question answering (QA) and question generation
(QG) in this paper.
Our intuition is that QA and QG have intrinsic connections and these two
tasks could improve each other.
On one side, the QA model judges whether the generated question of a QG model
is relevant to the answer.
On the other side, the QG model provides the probability of generating a
question given the answer, which is a useful evidence that in turn facilitates
QA.
In this paper we regard QA and QG as dual tasks.
We propose a training framework that trains the models of QA and QG
simultaneously, and explicitly leverages their probabilistic correlation to
guide the training process of both models.
We implement a QG model based on sequence-to-sequence learning, and a QA
model based on recurrent neural network.
As all the components of the QA and QG models are differentiable, all the
parameters involved in these two models could be conventionally learned with
back propagation.
We conduct experiments on three datasets. Empirical results show that our
training framework improves both QA and QG tasks.
The improved QA model performs comparably with strong baseline approaches on
all three datasets
Improving Question Answering by Commonsense-Based Pre-Training
Although neural network approaches achieve remarkable success on a variety of
NLP tasks, many of them struggle to answer questions that require commonsense
knowledge. We believe the main reason is the lack of commonsense
\mbox{connections} between concepts. To remedy this, we provide a simple and
effective method that leverages external commonsense knowledge base such as
ConceptNet. We pre-train direct and indirect relational functions between
concepts, and show that these pre-trained functions could be easily added to
existing neural network models. Results show that incorporating
commonsense-based function improves the baseline on three question answering
tasks that require commonsense reasoning. Further analysis shows that our
system \mbox{discovers} and leverages useful evidence from an external
commonsense knowledge base, which is missing in existing neural network models
and help derive the correct answer.Comment: 7 page
Knowledge-Aware Conversational Semantic Parsing Over Web Tables
Conversational semantic parsing over tables requires knowledge acquiring and
reasoning abilities, which have not been well explored by current
state-of-the-art approaches. Motivated by this fact, we propose a
knowledge-aware semantic parser to improve parsing performance by integrating
various types of knowledge. In this paper, we consider three types of
knowledge, including grammar knowledge, expert knowledge, and external resource
knowledge. First, grammar knowledge empowers the model to effectively replicate
previously generated logical form, which effectively handles the co-reference
and ellipsis phenomena in conversation Second, based on expert knowledge, we
propose a decomposable model, which is more controllable compared with
traditional end-to-end models that put all the burdens of learning on
trial-and-error in an end-to-end way. Third, external resource knowledge, i.e.,
provided by a pre-trained language model or an entity typing model, is used to
improve the representation of question and table for a better semantic
understanding. We conduct experiments on the SequentialQA dataset. Results show
that our knowledge-aware model outperforms the state-of-the-art approaches.
Incremental experimental results also prove the usefulness of various
knowledge. Further analysis shows that our approach has the ability to derive
the meaning representation of a context-dependent utterance by leveraging
previously generated outcomes
A Planning based Framework for Essay Generation
Generating an article automatically with computer program is a challenging
task in artificial intelligence and natural language processing. In this paper,
we target at essay generation, which takes as input a topic word in mind and
generates an organized article under the theme of the topic. We follow the idea
of text planning \cite{Reiter1997} and develop an essay generation framework.
The framework consists of three components, including topic understanding,
sentence extraction and sentence reordering. For each component, we studied
several statistical algorithms and empirically compared between them in terms
of qualitative or quantitative analysis. Although we run experiments on Chinese
corpus, the method is language independent and can be easily adapted to other
language. We lay out the remaining challenges and suggest avenues for future
research
Radical-Enhanced Chinese Character Embedding
We present a method to leverage radical for learning Chinese character
embedding. Radical is a semantic and phonetic component of Chinese character.
It plays an important role as characters with the same radical usually have
similar semantic meaning and grammatical usage. However, existing Chinese
processing algorithms typically regard word or character as the basic unit but
ignore the crucial radical information. In this paper, we fill this gap by
leveraging radical for learning continuous representation of Chinese character.
We develop a dedicated neural architecture to effectively learn character
embedding and apply it on Chinese character similarity judgement and Chinese
word segmentation. Experiment results show that our radical-enhanced method
outperforms existing embedding learning algorithms on both tasks.Comment: 8 pages, 4 figure
Deep Reason: A Strong Baseline for Real-World Visual Reasoning
This paper presents a strong baseline for real-world visual reasoning (GQA),
which achieves 60.93% in GQA 2019 challenge and won the sixth place. GQA is a
large dataset with 22M questions involving spatial understanding and multi-step
inference. To help further research in this area, we identified three crucial
parts that improve the performance, namely: multi-source features, fine-grained
encoder, and score-weighted ensemble. We provide a series of analysis on their
impact on performance.Comment: CVPR 2019 Visual Question Answering and Dialog Worksho
Knowledge Based Machine Reading Comprehension
Machine reading comprehension (MRC) requires reasoning about both the
knowledge involved in a document and knowledge about the world. However,
existing datasets are typically dominated by questions that can be well solved
by context matching, which fail to test this capability. To encourage the
progress on knowledge-based reasoning in MRC, we present knowledge-based MRC in
this paper, and build a new dataset consisting of 40,047 question-answer pairs.
The annotation of this dataset is designed so that successfully answering the
questions requires understanding and the knowledge involved in a document. We
implement a framework consisting of both a question answering model and a
question generation model, both of which take the knowledge extracted from the
document as well as relevant facts from an external knowledge base such as
Freebase/ProBase/Reverb/NELL. Results show that incorporating side information
from external KB improves the accuracy of the baseline question answer system.
We compare it with a standard MRC model BiDAF, and also provide the difficulty
of the dataset and lay out remaining challenges
Content-Based Table Retrieval for Web Queries
Understanding the connections between unstructured text and semi-structured
table is an important yet neglected problem in natural language processing. In
this work, we focus on content-based table retrieval. Given a query, the task
is to find the most relevant table from a collection of tables. Further
progress towards improving this area requires powerful models of semantic
matching and richer training and evaluation resources. To remedy this, we
present a ranking based approach, and implement both carefully designed
features and neural network architectures to measure the relevance between a
query and the content of a table. Furthermore, we release an open-domain
dataset that includes 21,113 web queries for 273,816 tables. We conduct
comprehensive experiments on both real world and synthetic datasets. Results
verify the effectiveness of our approach and present the challenges for this
task