Articles

AbstractWe argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model …...

AbstractStyle transfer is the task of automatically transforming a piece of text in one particular style into another. A major barrier to progress in this field has been a lack of training and evaluation datasets, as well as benchmarks and automatic metrics. In this work, we create the largest corpus …...

AbstractAnalyzing language in context, both from a theoretical and from a computational perspective, is receiving increased interest. Complementing the research in linguistics on discourse and information structure, in computational linguistics identifying discourse concepts was also shown to improve the performance of certain applications, for example, Short Answer Assessment systems (Ziai …...

AbstractWe present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder’s (OH’s) reasoning and a challenger’s argument, with the goal of predicting if the argument successfully changes the OH’s view. The model has two components: (1) vulnerable region detection, an attention model that …...

AbstractThis work examines the rhetorical techniques that speakers employ during political campaigns. We introduce a new corpus of speeches from campaign events in the months leading up to the 2016 U.S. presidential election and develop new models for predicting moments of audience applause. In contrast to existing datasets, we tackle …...

AbstractWe explore multitask models for neural translation of speech, augmenting them in order to reflect two intuitive notions. First, we introduce a model where the second task decoder receives information from the decoder of the first task, since higher-level intermediate representations should provide useful information. Second, we apply regularization that …...

AbstractRecently, neural machine translation (NMT) has emerged as a powerful alternative to conventional statistical approaches. However, its performance drops considerably in the presence of morphologically rich languages (MRLs). Neural engines usually fail to tackle the large vocabulary and high out-of-vocabulary (OOV) word rate of MRLs. Therefore, it is not suitable …...

AbstractIn conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses. For automatically parsing spoken utterances, we introduce a model that integrates transcribed text and acoustic-prosodic features using a convolutional neural network over energy and pitch trajectories coupled with an attention-based recurrent neural network that accepts text …...

AbstractMorphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define the state of the art for morphological segmentation in high-resource settings and for (mostly) European languages, we first show that they …...

AbstractSemi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge in bootstrapping is semantic drift: if a false positive instance is added during an iteration, then all following iterations are contaminated. We introduce BREX, …...

AbstractWhat makes some types of languages more probable than others? For instance, we know that almost all spoken languages contain the vowel phoneme /i/; why should that be? The field of linguistic typology seeks to answer these questions and, thereby, divine the mechanisms that underlie human language. In our work, …...

AbstractThe task of Fine-grained Entity Type Classification (FETC) consists of assigning types from a hierarchy to entity mentions in text. Existing methods rely on distant supervision and are thus susceptible to noisy labels that can be out-of-context or overly-specific for the training sentence. Previous methods that attempt to address these …...

AbstractWe demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, and propose a framework for integrating and jointly …...

AbstractWe study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and …...

This study aims to firstly implement four well-known metaheuristic optimization algorithms which, among other things, are utilized on adaptive filter design, for dual-channel speech enhancement, with voice communication devices. The implementations are conducted in a simulation fashion using MATLAB code under its newly release version of R2018a. Lately, the study …...

Smart solutions and solid solutions are two different approaches for computational linguistics. To receive a satisfactory result at a minimum cost, smart solutions use special properties of the on-hand application. Solid solutions, in contrast, more generally analyze the application in order that these solutions can be used for a whole …...

This platform includes two main concepts. One, provide multifaceted and deep evaluation for users of Natural Language Processing (NLP) System (such as Syntaxnet or CoreNLP), to help them choose the best one for their needs. Two, independently evaluate single NLP stage of a NLP system (such as pos tagging or …...

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 media posts are usually very short, there’s a lack of features for effective classification. Thus, word embedding models can be …...

. We report a series of experiments with different semantic models on top of various statistical models for extractive text summarization. Though statistical models may better cap- ture word co-occurrences and distribution around the text, they fail to detect the context and the sense of sentences /words as a whole. …...

An adversarial attack is an exploitative process in which minute alterations are made to natural inputs, causing the inputs to be misclassified by neural models. In the field of speech recognition, this has become an issue of increasing significance. Although adversarial attacks were originally introduced in computer vision, they have …...