Парафразаны анықтау

AbstractThis paper presents the ADN-Classifier, an Automatic classification system of Spanish Deverbal Nominalizations aimed at identifying its semantic denotation (i.e. event, result, underspecified, or lexicalized). The classifier can be used …...

AbstractIn previous work, we presented a preliminary study to identify paraphrases between technical and lay discourse types from medical corpora dedicated to the French language. In this paper, we test …...

In this paper we address the problem of modeling compositional meaning for phrases and sentences using distributional methods. We experiment with several possible com- binations of representation and composition, exhibiting …...

. Our work addresses the automatic detection of paraphrastic rephrasing in spoken corpus. The proposed approach is syntagmatic. It is based on paraphrastic rephrasing markers and the specificities of the …...

We provide a comparative study be- tween neural word representations and traditional vector spaces based on co- occurrence counts, in a number of com- positional tasks. We use three differ- …...

We explore using recursive autoencoders for SemEval 2015 Task 1: Paraphrase and Seman- tic Similarity in Twitter. Our paraphrase de- tection system makes use of phrase-structure parse tree embeddings that …...

: It has not been evaluated on empirical data and applied to any language tasks. We generate concrete models for this setting by developing algorithms to construct tensors and linear …...

Paraphrase detection has numerous important applications in natural language processing (such as clustering, summarizing, and detect- ing plagiarism). One approach to detecting paraphrases is to use predicate argument tu- ples. …...

AbstractScripts are standardized event sequences describing typical everyday activities, which play an important role in the computational modeling of cognitive abilities (in particular for natural language processing). We present a …...

AbstractExisting systems deliver high accuracy and F1-scores for detecting paraphrase and semantic similarity on traditional clean-text corpus. For instance, on the clean-text Microsoft Paraphrase benchmark database, the existing systems attain …...

AbstractThe task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions. Ambiguous, misspelled, and …...

AbstractWe present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective. Our system is aimed at engaging …...

AbstractText similarity measures are used in multiple tasks such as plagiarism detection, information ranking and recognition of paraphrases and textual entailment. While recent advances in deep learning highlighted the relevance …...

AbstractAbstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form. As such, it is ideal …...

We propose ESCRITO, a toolkit for scoring student writings using NLP techniques that addresses two main user groups: teachers and NLP researchers. Teachers can use a high-level API in the …...

AbstractThe rapid growth of documents across the web has necessitated finding means of discarding redundant documents and retaining novel ones. Capturing redundancy is challenging as it may involve investigating at …...

Duplicate Question Detection (DQD) is a Natural Language Processing task under active research, with applications to fields like Community Question Answering and Information Retrieval. While DQD falls under the umbrella …...

In this paper, we describe our effort to create a new corpus for the evaluation of detecting and linking so-called survey variables in social science publications (e.g., ”Do you believe …...

AbstractIn this paper, we present a method for adversarial decomposition of text representation. This method can be used to decompose a representation of an input sentence into several independent vectors, …...

AbstractPre-trained Transformers are now ubiquitous in natural language processing, but despite their high end-task performance, little is known empirically about whether they are calibrated. Specifically, do these models’ posterior probabilities …...

AbstractIn this paper, we introduce NLP resources for 11 major Indian languages from two major language families. These resources include: (a) large-scale sentence-level monolingual corpora, (b) pre-trained word embeddings, (c) …...

AbstractMany pairwise classification tasks, such as paraphrase detection and open-domain question answering, naturally have extreme label imbalance (e.g., 99.99% of examples are negatives). In contrast, many recent datasets heuristically choose …...

AbstractThe ability of humans to symbolically represent social events and situations is crucial for various interactions in everyday life. Several studies in cognitive psychology have established the role of mental …...

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 …...

AbstractWe introduce MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learning on a large set of …...

AbstractNoisy labels in training data present a challenging issue in classification tasks, misleading a model towards incorrect decisions during training. In this paper, we propose the use of a linear …...

AbstractA well-calibrated neural model produces confidence (probability outputs) closely approximated by the expected accuracy. While prior studies have shown that mixup training as a data augmentation technique can improve model …...

Paraphrase detection allows identifying the degree of likelihood between source and suspect sentences. It is a critical machine learning problem in computational linguistics. This is due to the expression variability …...

AbstractWhile fine-tuning pre-trained models for downstream classification is the conventional paradigm in NLP, often task-specific nuances may not get captured in the resultant models. Specifically, for tasks that take two …...

AbstractParaphrase detection is useful in many natural language understanding applications. Current works typically formulate this problem as a sentence pair binary classification task. However, this setup is not a good …...