Articles

AbstractSplit-and-rephrase is a challenging task that promotes the transformation of a given complex input sentence into multiple shorter sentences retaining equivalent meaning. This rewriting approach conceptualizes that shorter sentences benefit human readers and improve NLP downstream tasks attending as a preprocessing step. This work presents a complete pipeline capable of …...

AbstractThis paper presents a global summarization method for live sport commentaries for which we have a human-written summary available. This method is based on a neural generative summarizer. The amount of data available for training is limited compared to corpora commonly used by neural summarizers. We propose to help the …...

AbstractDefinition modelling is the task of automatically generating a dictionary-style definition given a target word. In this paper, we consider cross-lingual definition generation. Specifically, we generate English definitions for Wolastoqey (Malecite-Passamaquoddy) words. Wolastoqey is an endangered, low-resource polysynthetic language. We hypothesize that sub-word representations based on byte pair encoding (Sennrich …...

AbstractEmotion Classification is the task of automatically associating a text with a human emotion. State-of-the-art models are usually learned using annotated corpora or rely on hand-crafted affective lexicons. We present an emotion classification model that does not require a large annotated corpus to be competitive. We experiment with pretrained language …...

AbstractBest-worst Scaling (BWS) is a methodology for annotation based on comparing and ranking instances, rather than classifying or scoring individual instances. Studies have shown the efficacy of this methodology applied to NLP tasks in terms of a higher quality of the datasets produced by following it. In this system demonstration …...

AbstractRecently, the majority of sentiment analysis researchers focus on target-based sentiment analysis because it delivers in-depth analysis with more accurate results as compared to traditional sentiment analysis. In this paper, we propose an interactive learning approach to tackle a target-based sentiment analysis task for the Arabic language. The proposed IA-LSTM …...

AbstractSentiment analysis aims to detect the overall sentiment, i.e., the polarity of a sentence, paragraph, or text span, without considering the entities mentioned and their aspects. Aspect-based sentiment analysis aims to extract the aspects of the given target entities and their respective sentiments. Prior works formulate this as a sequence …...

AbstractEuroVoc is a multilingual thesaurus that was built for organizing the legislative documentary of the European Union institutions. It contains thousands of categories at different levels of specificity and its descriptors are targeted by legal texts in almost thirty languages. In this work we propose a unified framework for EuroVoc …...

AbstractWe address the compositionality challenge presented by the SCAN benchmark. Using data augmentation and a modification of the standard seq2seq architecture with attention, we achieve SOTA results on all the relevant tasks from the benchmark, showing the models can generalize to words used in unseen contexts. We propose an extension …...

AbstractCoreference resolution is an NLP task to find out whether the set of referring expressions belong to the same concept in discourse. A multi-pass sieve is a deterministic coreference model that implements several layers of sieves, where each sieve takes a pair of correlated mentions from a collection of non-coherent …...

AbstractIntroducing factors, that is to say, word features such as linguistic information referring to the source tokens, is known to improve the results of neural machine translation systems in certain settings, typically in recurrent architectures. This study proposes enhancing the current state-of-the-art neural machine translation architecture, the Transformer, so that …...

AbstractIn this paper, we propose a definition and taxonomy of various types of non-standard textual content – generally referred to as “noise” – in Natural Language Processing (NLP). While data pre-processing is undoubtedly important in NLP, especially when dealing with user-generated content, a broader understanding of different sources of noise …...

AbstractThe advancement of the web and information technology has contributed to the rapid growth of digital libraries and automatic machine translation tools which easily translate texts from one language into another. These have increased the content accessible in different languages, which results in easily performing translated plagiarism, which are referred …...

AbstractUsing pre-trained transformer models such as BERT has proven to be effective in many NLP tasks. This paper presents our work to fine-tune BERT models for Arabic Word Sense Disambiguation (WSD). We treated the WSD task as a sentence-pair binary classification task. First, we constructed a dataset of labeled Arabic …...

AbstractIn this paper, we present a novel approachfor domain adaptation in Neural MachineTranslation which aims to improve thetranslation quality over a new domain. Adapting new domains is a highly challeng-ing task for Neural Machine Translation onlimited data, it becomes even more diffi-cult for technical domains such as Chem-istry and Artificial …...

AbstractIndia is one of the richest language hubs on the earth and is very diverse and multilingual. But apart from a few Indian languages, most of them are still considered to be resource poor. Since most of the NLP techniques either require linguistic knowledge that can only be developed by …...

AbstractThis work presents a generic semi-automatic strategy to populate the domain ontology of an ontology-driven task-oriented dialogue system, with the aim of performing successful intent detection in the dialogue process, reusing already existing multilingual resources. This semi-automatic approach allows ontology engineers to exploit available resources so as to associate the …...

AbstractWritten communication is of utmost importance to the progress of scientific research. The speed of such development, however, may be affected by the scarcity of reviewers to referee the quality of research articles. In this context, automatic approaches that are able to query linguistic segments in written contributions by detecting …...

AbstractWe adapt BLiMP (Benchmark of Linguistic Minimal Pairs) language model evaluation framework to the context of poetry, introducing the first of a series of tasks titled Benchmark of Poetic Minimal Pairs (BPoMP). The tasks presented herein use one genre of English-language poetry, the limerick (five-lines, rhyme scheme AABBA). Following the …...

AbstractComplex natural language applications such as speech translation or pivot translation traditionally rely on cascaded models. However,cascaded models are known to be prone to error propagation and model discrepancy problems. Furthermore, there is no possibility of using end-to-end training data in conventional cascaded systems, meaning that the training data most …...