2,938 research outputs found
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
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
We describe PARANMT-50M, a dataset of more than 50 million English-English
sentential paraphrase pairs. We generated the pairs automatically by using
neural machine translation to translate the non-English side of a large
parallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M
can be a valuable resource for paraphrase generation and can provide a rich
source of semantic knowledge to improve downstream natural language
understanding tasks. To show its utility, we use ParaNMT-50M to train
paraphrastic sentence embeddings that outperform all supervised systems on
every SemEval semantic textual similarity competition, in addition to showing
how it can be used for paraphrase generation
Automatic extraction of paraphrastic phrases from medium size corpora
This paper presents a versatile system intended to acquire paraphrastic
phrases from a representative corpus. In order to decrease the time spent on
the elaboration of resources for NLP system (for example Information
Extraction, IE hereafter), we suggest to use a machine learning system that
helps defining new templates and associated resources. This knowledge is
automatically derived from the text collection, in interaction with a large
semantic network
ParaPhraser: Russian paraphrase corpus and shared task
The paper describes the results of the First Russian Paraphrase Detection Shared Task held in St.-Petersburg, Russia, in October 2016. Research in the area of paraphrase extraction, detection and generation has been successfully developing for a long time while there has been only a recent surge of interest towards the problem in the Russian community of computational linguistics. We try to overcome this gap by introducing the project ParaPhraser.ru dedicated to the collection of Russian paraphrase corpus and organizing a Paraphrase Detection Shared Task, which uses the corpus as the training data. The participants of the task applied a wide variety of techniques to the problem of paraphrase detection, from rule-based approaches to deep learning, and results of the task reflect the following tendencies: the best scores are obtained by the strategy of using traditional classifiers combined with fine-grained linguistic features, however, complex neural networks, shallow methods and purely technical methods also demonstrate competitive results.Peer reviewe
Facilitating translation using source language paraphrase lattices
For resource-limited language pairs, coverage of the test set by the parallel corpus is an important factor that affects translation quality in two respects: 1) out of vocabulary words; 2) the same information in an input
sentence can be expressed in different ways, while current phrase-based SMT systems cannot automatically select an alternative way to transfer the same information. Therefore,
given limited data, in order to facilitate translation
from the input side, this paper proposes a novel method to reduce the translation difficulty using source-side lattice-based paraphrases. We utilise the original phrases from the input sentence and the corresponding paraphrases to build a lattice with estimated weights for each edge to improve translation quality. Compared to the baseline system, our method achieves relative improvements of 7.07%, 6.78% and 3.63% in terms of BLEU score on small, medium and largescale
English-to-Chinese translation tasks respectively. The results show that the proposed method is effective not only for resourcelimited language pairs, but also for resource sufficient pairs to some extent
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters
To bridge the gap between the capabilities of the state-of-the-art in factoid
question answering (QA) and what users ask, we need large datasets of real user
questions that capture the various question phenomena users are interested in,
and the diverse ways in which these questions are formulated. We introduce
ComQA, a large dataset of real user questions that exhibit different
challenging aspects such as compositionality, temporal reasoning, and
comparisons. ComQA questions come from the WikiAnswers community QA platform,
which typically contains questions that are not satisfactorily answerable by
existing search engine technology. Through a large crowdsourcing effort, we
clean the question dataset, group questions into paraphrase clusters, and
annotate clusters with their answers. ComQA contains 11,214 questions grouped
into 4,834 paraphrase clusters. We detail the process of constructing ComQA,
including the measures taken to ensure its high quality while making effective
use of crowdsourcing. We also present an extensive analysis of the dataset and
the results achieved by state-of-the-art systems on ComQA, demonstrating that
our dataset can be a driver of future research on QA.Comment: 11 pages, NAACL 201
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
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