Paraphrase Identification

This paper proposes a general learning frame- work for a class of problems that require learn- ing over latent intermediate representations. Many natural language processing (NLP) de- cision problems are …...

We describe tree edit models for representing sequences of tree transformations involving complex reordering phenomena and demon- strate that they offer a simple, intuitive, and effective method for modeling pairs …...

We propose to re-examine the hypothesis that automated metrics developed for MT evalu- ation can prove useful for paraphrase iden- tification in light of the significant work on the development …...

A citing sentence is one that appears in a sci- entific article and cites previous work. Cit- ing sentences have been studied and used in many applications. For example, they …...

Matrix and tensor factorization have been ap- plied to a number of semantic relatedness tasks, including paraphrase identification. The key idea is that similarity in the latent space implies semantic …...

In this paper, we describe a method for as- sessing student answers, modeled as a para- phrase identification problem, based on sub- stitution by Basic English variants. Basic En- glish …...

AbstractWe analyze in this paper a number of data sets proposed over the last decade or so for the task of paraphrase identification. The goal of the analysis is to …...

We present a new deep learning architecture Bi-CNN-MI for paraphrase identification (PI). Based on the insight that PI requires compar- ing two sentences on multiple levels of granu- larity, we …...

This work, concerning paraphrase identifica- tion task, on one hand contributes to expand- ing deep learning embeddings to include con- tinuous and discontinuous linguistic phrases. On the other hand, it …...

We present a work to evaluate the hy- pothesis that automatic evaluation metrics developed for Machine Translation (MT) systems have significant impact on pre- dicting semantic similarity scores in Se- …...

This paper describes MITRE’s participation in the Paraphrase and Semantic Similar- ity in Twitter task (SemEval-2015 Task 1). This effort placed first in Semantic Similar- ity and second in Paraphrase …...

In this shared task, we present evaluations on two related tasks Paraphrase Identification (PI) and Semantic Textual Similarity (SS) sys- tems for the Twitter data. Given a pair of sentences, …...

AbstractHow to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work …...

AbstractWe describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs. Since existing attentive models exert attention on the sequential structure, …...

AbstractA major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets …...

AbstractWhen we build a neural network model predicting the relationship between two sentences, the most general and intuitive approach is to use a Siamese architecture, where the sentence vectors obtained …...

We present the Extended Paraphrase Typology (EPT) and the Extended Typology Paraphrase Corpus (ETPC). The EPT typology addresses several practical limitations of existing paraphrase typologies: it is the first typology …...

AbstractIn this paper, we present a new approach for the evaluation, error analysis, and interpretation of supervised and unsupervised Paraphrase Identification (PI) systems. Our evaluation framework makes use of a …...

AbstractIn this paper, we present a fast and strong neural approach for general purpose text matching applications. We explore what is sufficient to build a fast and well-performed text matching …...

AbstractA semantic equivalence assessment is defined as a task that assesses semantic equivalence in a sentence pair by binary judgment (i.e., paraphrase identification) or grading (i.e., semantic textual similarity measurement). …...

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 …...

AbstractRecent work has shown that pre-trained language models such as BERT improve robustness to spurious correlations in the dataset. Intrigued by these results, we find that the key to their …...

AbstractRevealing the robustness issues of natural language processing models and improving their robustness is important to their performance under difficult situations. In this paper, we study the robustness of paraphrase …...

Argumentation is an important tool within hu- man interaction, not only in law and politics but also for discussing issues, expressing and exchanging opinions and coming to decisions in our …...

AbstractExplaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting …...

AbstractTransformers-based pretrained language models achieve outstanding results in many well-known NLU benchmarks. However, while pretraining methods are very convenient, they are expensive in terms of time and resources. This calls …...

AbstractIf two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets …...

AbstractIn this paper, we investigate the Domain Generalization (DG) problem for supervised Paraphrase Identification (PI). We observe that the performance of existing PI models deteriorates dramatically when tested in an …...

AbstractTraditional (fickle) adversarial examples involve finding a small perturbation that does not change an input’s true label but confuses the classifier into outputting a different prediction. Conversely, obstinate adversarial examples …...

AbstractIn this paper, we present an evaluation of sentence representation models on the paraphrase detection task. The evaluation is designed to simulate a real-world problem of plagiarism and is based …...

AbstractParaphrase identification has been formulated as a binary classification task to decide whether two sentences hold a paraphrase relationship. Existing paraphrase datasets only annotate a binary label for each sentence …...

AbstractIn this paper we introduce PerPaDa, a Persian paraphrase dataset that is collected from users’ input in a plagiarism detection system. As an implicit crowdsourcing experience, we have gathered a …...

AbstractParaphrase identification involves identifying whether a pair of sentences express the same or similar meanings. While cross-encoders have achieved high performances across several benchmarks, bi-encoders such as SBERT have been …...

AbstractPrevious works have demonstrated the effectiveness of utilising pre-trained sentence encoders based on their sentence representations for meaning comparison tasks. Though such representations are shown to capture hidden syntax structures, …...