3,333 research outputs found
Automatic generation of large-scale paraphrases
Research on paraphrase has mostly focussed on lexical or syntactic variation within individual sentences. Our concern is with larger-scale paraphrases, from multiple sentences or paragraphs to entire documents. In this paper
we address the problem of generating paraphrases of large chunks of texts. We ground our discussion through a
worked example of extending an existing NLG system to accept as input a source text, and to generate a range of fluent semantically-equivalent alternatives, varying not only at the lexical and syntactic levels, but also in document structure and layout
Generating Syntactic Paraphrases
International audienceWe study the automatic generation of syntactic paraphrases using four different models for generation: data-to-text generation, textto-text generation, text reduction and text expansion, We derive training data for each of these tasks from the WebNLG dataset and we show (i) that conditioning generation on syntactic constraints effectively permits the generation of syntactically distinct paraphrases for the same input and (ii) that exploiting different types of input (data, text or data+text) further increases the number of distinct paraphrases that can be generated for a given input
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Contextual bitext-derived paraphrases in automatic MT evaluation
In this paper we present a novel method for deriving paraphrases during automatic MT evaluation using only the source and reference texts, which are necessary for
the evaluation, and word and phrase alignment software. Using target language paraphrases produced through word and
phrase alignment a number of alternative reference sentences are constructed automatically for each candidate translation. The method produces lexical and lowlevel
syntactic paraphrases that are relevant to the domain in hand, does not use external knowledge resources, and can be
combined with a variety of automatic MT evaluation system
Using Natural Language as Knowledge Representation in an Intelligent Tutoring System
Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they donât learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge
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