Long Short Term Memory

Document level sentiment classification remains a challenge: encoding the intrin- sic relations between sentences in the se- mantic meaning of a document. To ad- dress this, we introduce a neural …...

This paper demonstrates the effectiveness of a Long Short-Term Memory language model in our initial efforts to generate un- constrained rap lyrics. The goal of this model is to generate …...

We propose a first-ever attempt to employ a Long Short-Term memory based framework to predict humor in dialogues. We analyze data from a popular TV-sitcom, whose canned laughters give an …...

AbstractThe success of long short-term memory (LSTM) neural networks in language processing is typically attributed to their ability to capture long-distance statistical regularities. Linguistic regularities are often sensitive to syntactic …...

Automated Text Scoring (ATS) provides a cost-effective and consistent alternative to human marking. However, in order to achieve good performance, the pre- dictive features of the system need to be …...

Recurrent neural networks, particularly long short-term memory (LSTM), have recently shown to be very effective in a wide range of sequence modeling problems, core to which is effective learning of …...

AbstractNeural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are …...

We compare different word embeddings from a standard window based skipgram model, a skipgram model trained using dependency context features and a novel skipgram variant that utilizes additional information from …...

AbstractWhile automatic response generation for building chatbot systems has drawn a lot of attention recently, there is limited understanding on when we need to consider the linguistic context of an …...

AbstractTarget-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 …...

We introduce a recurrent neural network lan- guage model (RNN-LM) with long short- term memory (LSTM) units that utilizes both character-level and word-level inputs. Our model has a gate that …...

AbstractMicroblogging services allow users to create hashtags to categorize their posts. In recent years, the task of recommending hashtags for microblogs has been given increasing attention. However, most of existing …...

AbstractIn community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers. In this paper, we show that …...

Encoder-decoder networks are popular for modeling sequences probabilistically in many applications. These models use the power of the Long Short-Term Memory (LSTM) archi- tecture to capture the full dependence among …...

Recurrent Neural Network (RNN) and one of its specific architectures, Long Short-Term Memory (LSTM), have been widely used for sequence labeling. Explicitly modeling out- put label dependencies on top of …...

In this paper we address the question of how to render sequence-level networks better at handling structured input. We propose a ma- chine reading simulator which processes text incrementally from …...

Semantic dependency parsing aims at ex- tracting arcs and semantic role labels for all words in a sentence. In this paper, we propose a semantic dependency parser which is based …...

AbstractThis paper presents a novel approach to recurrent neural network (RNN) regularization. Differently from the widely adopted dropout method, which is applied to forward connections of feedforward architectures or RNNs, …...

Recurrent Neural Networks (RNNs) have ob- tained excellent result in many natural lan- guage processing (NLP) tasks. However, un- derstanding and interpreting the source of this success remains a challenge. …...

In this paper, we explore the possibility of leveraging Residual Networks (ResNet), a powerful structure in constructing extremely deep neural network for image understanding, to improve recurrent neural networks (RNN) …...

AbstractIn this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis. We develop various …...

AbstractInferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Many neural network models have been proposed to tackle this problem. However, the comparison …...

AbstractDespite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary …...

AbstractIn this paper, we describe our system implementation for sentiment analysis in Twitter. This system combines two models based on deep neural networks, namely a convolutional neural network (CNN) and …...

AbstractWe present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract …...

AbstractWe propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate. …...

AbstractWe propose a new encoder-decoder approach to learn distributed sentence representations that are applicable to multiple purposes. The model is learned by using a convolutional neural network as an encoder …...

Abbreviation is a common phenomenon across languages, especially in Chinese. In most cases, if an expression can be abbreviated, its abbreviation is used more often than its fully expanded forms, …...

AbstractIn this paper, we first utilize the word embedding that focuses on sub-word units to the Mongolian Phrase Break (PB) prediction task by using Long-Short-Term-Memory (LSTM) model. Mongolian is an …...

Sentiment classification techniques have been widely used for analyzing user opinions. In conventional supervised learning methods, hand-crafted features are needed, which requires a thorough understanding of the domain. Since social …...

AbstractLong short-term memory (LSTM) language model (LM) has been widely investigated for automatic speech recognition (ASR) and natural language processing (NLP). Although excellent performance is obtained for large vocabulary tasks, …...

AbstractThis paper describes the participation of ELiRF-UPV team at tasks 1 and 3 of Semeval-2018. We present a deep learning based system that assembles Convolutional Neural Networks and Long Short-Term …...

AbstractWhile Long Short-Term Memory networks (LSTMs) and other forms of recurrent neural network have been successfully applied to language modeling on a character level, the hidden state dynamics of these …...

This study presents a long short-term memory (LSTM) neural network approach to Japanese word segmentation (JWS). Previous studies on Chinese word segmentation have succeeded in using recurrent neural networks such …...

AbstractIn this paper, we describe the first attempt to perform transfer learning from sentiment to emotions. Our system employs Long Short-Term Memory (LSTM) networks, including bidirectional LSTM (biLSTM) and LSTM …...

Autistic children are less able to tell a fluent story than typical children, so measuring verbal fluency becomes an important indicator when diagnosing autistic children. Fluency assessment, however, needs time-consuming …...

AbstractPhrases play a key role in Machine Translation (MT). In this paper, we apply a Long Short-Term Memory (LSTM) model over conventional Phrase-Based Statistical MT (PBSMT). The core idea is …...

AbstractDeep neural network models such as long short-term memory (LSTM) and tree-LSTM have been proven to be effective for sentiment analysis. However, sequential LSTM is a bias model wherein the …...

AbstractThis paper describes our system submissions as part of our participation (team name: JU_ETCE_17_21) in the SemEval 2019 shared task 6: “OffensEval: Identifying and Catego- rizing Offensive Language in Social …...

AbstractWhen searching for information, a human reader first glances over a document, spots relevant sections, and then focuses on a few sentences for resolving her intention. However, the high variance …...

AbstractDeep neural network models have played a critical role in sentiment analysis with promising results in the recent decade. One of the essential challenges, however, is how external sentiment knowledge …...

AbstractLong Short-Term Memory recurrent neural network (LSTM) is widely used and known to capture informative long-term syntactic dependencies. However, how such information are reflected in its internal vectors for natural …...

AbstractMany organizations use domain- or organization-specific words and phrases. This paper explores the use of vetted terminology as an input to neural machine translation (NMT) for improved results: ensuring that …...

AbstractA Mathematical Word Problem (MWP) differs from a general textual representation due to the fact that it is comprised of numerical quantities and units, in addition to text. Therefore, MWP …...

AbstractEarly rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available. Most of the existing methods have largely worked on event-level detection …...

AbstractDetecting arguments in online interactions is useful to understand how conflicts arise and get resolved. Users often use figurative language, such as sarcasm, either as persuasive devices or to attack …...