Paraphrasing

This paper discusses a system for producing English descriptions (or "paraphrases") of tile content of formal relational calculus (RC) formulae expressing a database (DB) query. It explains the underlying design …...

While paraphrasing is critical both for interpretation and generation of natu- ral language, current systems use man- ual or semi-automatic methods to col- lect paraphrases. We present an un- supervised …...

Aligning sentences belonging to compa- rable monolingual corpora has been sug- gested as a first step towards training text rewriting algorithms, for tasks such as summarization or paraphrasing. We present …...

This paper proposes a novel method that ex- ploits multiple resources to improve statisti- cal machine translation (SMT) based para- phrasing. In detail, a phrasal paraphrase ta- ble and a …...

We present ParaMetric, an automatic eval- uation metric for data-driven approaches to paraphrasing. ParaMetric provides an ob- jective measure of quality using a collec- tion of multiple translations whose para- …...

AbstractWe present a method for acquiring reliable predicate-argument structures from raw corpora for automatic compilation of case frames. Such lexicon compilation requires highly reliable predicate-argument structures to practically contribute to …...

Near-synonyms are useful knowledge re- sources for many natural language applica- tions such as query expansion for information retrieval (IR) and paraphrasing for text gen- eration. However, near-synonyms are not …...

Current Translation Memory (TM) sys- tems work at the surface level and lack se- mantic knowledge while matching. This paper presents an approach to incorpo- rating semantic knowledge in the …...

We consider the problem of automatically paraphrasing a text in order to find an equivalent text that contains a given acrostic. A text contains an acrostic, if the first letters …...

This paper presents the system submit- ted by University of Wolverhampton for SemEval-2014 task 1. We proposed a ma- chine learning approach which is based on features extracted using Typed …...

This paper investigates to what extent the use of paraphrasing in translation mem- ory (TM) matching and retrieval is use- ful for human translators. Current trans- lation memories lack semantic …...

We present an intuitive and effective method for inducing style scores on words and phrases. We exploit signal in a phrase’s rate of occurrence across stylistically contrasting cor- pora, making …...

Relational phrases (e.g., “got married to”) and their hypernyms (e.g., “is a relative of”) are central for many tasks including question answering, open information ex- traction, paraphrasing, and entailment de- …...

AbstractAs data-driven approaches started to make their way into the Natural Language Generation (NLG) domain, the need for automation of corpus building and extension became apparent. Corpus creation and extension …...

AbstractThis work exploits translation data as a source of semantically relevant learning signal for models of word representation. In particular, we exploit equivalence through translation as a form of distributional …...

In recent years, the representation of words as vectors in a vector space, also known as word embeddings, has achieved a high degree of attention in the research community and …...

AbstractWe propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing. Our proposal relies on very simple interactions: a human editor modifies a sentence by …...

AbstractWord embedding models are now widely used in most NLP applications. Despite their effectiveness, there is no clear evidence about the choice of the most appropriate model. It often depends …...

AbstractInducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents. Previous paraphrasing approaches have mainly focused on the issue …...

AbstractParaphrasing is an important task demonstrating the ability to abstract semantic content from its surface form. Recent literature on automatic paraphrasing is dominated by methods leveraging machine translation as an …...

AbstractParaphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented. Traditionally, paraphrasing has been focused on finding different words that …...

AbstractIn this paper, we present a methodology for decomposing and comparing multiple meaning relations (paraphrasing, textual entailment, contradiction, and specificity). The methodology includes SHARel - a new typology that consists …...

AbstractParaphrasing natural language sentences is a multifaceted process: it might involve replacing individual words or short phrases, local rearrangement of content, or high-level restructuring like topicalization or passivization. Past approaches …...

AbstractUnconscious biases continue to be prevalent in modern text and media, calling for algorithms that can assist writers with bias correction. For example, a female character in a story is …...

We introduce a new approach for the task of Controllable Text Simplification, where sys- tems rewrite a complex English sentence so that it can be understood by readers at differ- …...

AbstractSemantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method - Synchronous …...

AbstractPublicly available, large pretrained Language Models (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks …...

AbstractUnsupervised consistency training is a way of semi-supervised learning that encourages consistency in model predictions between the original and augmented data. For Named Entity Recognition (NER), existing approaches augment the …...

AbstractA large body of recent work highlights the fallacies of zero-shot cross-lingual transfer (ZS-XLT) with large multilingual language models. Namely, their performance varies substantially for different target languages and is …...

AbstractWe propose GRS: an unsupervised approach to sentence simplification that combines text generation and text revision. We start with an iterative framework in which an input sentence is revised using …...

AbstractAugmentation of task-oriented dialogues has followed standard methods used for plain-text such as back-translation, word-level manipulation, and paraphrasing despite its richly annotated structure. In this work, we introduce an augmentation …...

AbstractThe quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation. In Natural …...

AbstractParaphrasing, i.e., restating the same meaning in different ways, is an important data augmentation approach for natural language processing (NLP). Zhang et al. (2019b) propose to extract sentence-level paraphrases from …...

AbstractAlthough the fluency of automatically generated abstractive summaries has improved significantly with advanced methods, the inconsistency that remains in summarization is recognized as an issue to be addressed. In this …...

AbstractParaphrasing is often performed with less concern for controlled style conversion. Especially for questions and commands, style-variant paraphrasing can be crucial in tone and manner, which also matters with industrial …...

AbstractMany text generation applications require the generated text to be factually consistent with input information. Automatic evaluation of factual consistency is challenging. Previous work has developed various metrics that often …...

AbstractThis paper explores negative lexical constraining in English to Czech neural machine translation. Negative lexical constraining is used to prohibit certain words or expressions in the translation produced by the …...

AbstractWhile paraphrasing is a promising approach for data augmentation in classification tasks, its effect on named entity recognition (NER) is not investigated systematically due to the difficulty of span-level label …...