Сөйлемдердің ұқсастығы

This paper proposes a framework for repre- senting the meaning of phrases and sentences in vector space. Central to our approach is vector composition which we operationalize in terms of …...

This paper investigates novel methods for in- corporating syntactic information in proba- bilistic latent variable models of lexical choice and contextual similarity. The resulting mod- els capture the effects of …...

Sentence Similarity [SS] computes a similar- ity score between two sentences. The SS task differs from document level semantics tasks in that it features the sparsity of words in a …...

We combine logical and distributional rep- resentations of natural language meaning by transforming distributional similarity judg- ments into weighted inference rules using Markov Logic Networks (MLNs). We show that this …...

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- …...

Modeling sentence similarity is compli- cated by the ambiguity and variability of linguistic expression. To cope with these challenges, we propose a model for com- paring sentences that uses a …...

AbstractMethods for text simplification using the framework of statistical machine translation have been extensively studied in recent years. However, building the monolingual parallel corpus necessary for training the model requires …...

We present CHARAGRAM embeddings, a sim- ple approach for learning character-based compositional models to embed textual se- quences. A word or sentence is represented using a character n-gram count vector, …...

We study the problem of jointly aligning sen- tence constituents and predicting their sim- ilarities. While extensive sentence similar- ity data exists, manually generating reference alignments and labeling the similarities …...

AbstractEvaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges. In this paper, we explore the use of embeddings obtained from …...

AbstractWe introduce an approach based on using the dependency grammar representations of sentences to compute sentence similarity for extractive multi-document summarization. We adapt and investigate the effects of two untyped …...

AbstractMost conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about …...

We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. …...

AbstractPrevious work has indicated that pretrained Masked Language Models (MLMs) are not effective as universal lexical and sentence encoders off-the-shelf, i.e., without further task-specific fine-tuning on NLI, sentence similarity, or …...

AbstractInteractive-predictive translation is a collaborative iterative process and where human translators produce translations with the help of machine translation (MT) systems interactively. Various sampling techniques in active learning (AL) exist …...

AbstractThe task of Event Detection (ED) in Information Extraction aims to recognize and classify trigger words of events in text. The recent progress has featured advanced transformer-based language models (e.g., …...

AbstractModels based on large-pretrained language models, such as S(entence)BERT, provide effective and efficient sentence embeddings that show high correlation to human similarity ratings, but lack interpretability. On the other hand, …...

AbstractExisting methods to measure sentence similarity are faced with two challenges: (1) labeled datasets are usually limited in size, making them insufficient to train supervised neural models; and (2) there …...

AbstractA variety of NLP applications use word2vec skip-gram, GloVe, and fastText word embeddings. These models learn two sets of embedding vectors, but most practitioners use only one of them, or …...