DSE learns sentence embeddings from cross-attention models to speed up sentence-pair similarity computation.
problem Computing sentence-pair similarity is computationally expensive when candidate sentences are large.
method Distilled Sentence Embedding (DSE) based on knowledge distillation from cross-attentive models.
result DSE significantly outperforms ELMO variants and other sentence embedding methods, accelerating computation by several orders of magnitude.
Prototype sentences edited for better language models and quality.
problem Improving sentence generation quality and efficiency.
method Samples a prototype sentence, edits it, and uses a latent edit vector.
result Improves perplexity and generates higher quality sentences.
Neural network model improves sentence classification in medical abstracts.
problem Individual sentence classification misses contextual information.
method Combines ANN effectiveness with structured prediction for joint sentence classification.
result Achieves state-of-the-art results on medical abstract datasets.
Estimating the difficulty level of math word problems is an important task for many educational applications. Identification of relevant and irrelevant sentences in math word problems is an important step for calculating the difficulty levels of such problems. This paper addresses a novel application of text categoriza…
Paper proposes a novel model to improve n-ary cross-sentence relation extraction by addressing noisy data and non-consecutive sentences.
problem Noisy labeled data and non-consecutive sentences in n-ary cross-sentence relation extraction.
method Two-level agent reinforcement learning model and hybrid attention mechanism/PCNN approach.
result The model reduces the impact of noisy data and achieves better performance.
New metrics predict human sentence comprehension across languages.
problem Predicting human sentence comprehension using computational models.
method Developed sentence-level metrics using multilingual large language models.
result Achieved high accuracy in predicting human sentence reading speeds.
Bayesian EnKF improves sentence comprehension uncertainty modeling.
problem Uncertainty in human language comprehension, especially with ambiguous inputs.
method Bayesian framework using ensemble Kalman filter (EnKF) for uncertainty quantification.
result Enhanced model's ability to approximate human cognitive processing with linguistic ambiguities.
AUTR generates sentences using a dynamic memory and attention mechanism.
problem Generating coherent sentences without explicit training data.
method Recurrent neural network with dynamic attention and canvas memory.
result AUTR learns meaningful latent representations and achieves competitive performance.
SenGen generates sentences conditioned on topics, improving topic visualization.
problem Improve topic visualization and interpretability in documents.
method Variational auto-encoder with RNN decoder conditioned on topics.
result Preliminary experiments show promise but also challenges remain.
Paper tackles noisy relation classification by sentence-level reinforcement learning.
problem Noisy distant supervision in relation classification.
method Two-module approach: instance selector using reinforcement learning, relation classifier making sentence-level predictions.
result Jointly trained model optimizes instance selection and relation classification, effectively handling noisy data.
A graph model improves short text classification by integrating sentence relationships.
problem Sparse features in short text classification due to limited text length.
method PathWalk model combining graph networks and short sentences.
result PathWalk achieves state-of-the-art results on four datasets.
This study improves sentence embeddings from BERT models.
problem Capturing the underlying meaning of sentences using BERT models.
method Comprehensive review and testing of various sentence embedding extraction and refinement methods.
result Representation-shaping techniques significantly improve sentence embeddings from BERT-based and simple baseline models.
The paper improves ESG taxonomy and classifies sentences as sustainable or unsustainable.
problem Improving ESG taxonomy and classifying sentences based on ESG factors.
method For ESG taxonomy, used Sentence-BERT models. For sentence classification, combined RoBERTa with a multi-layer perceptron.
result Significant performance improvement and high accuracy in classifying sentences.
SFBoW provides sentence embeddings with predefined dimensions.
problem Sentence embeddings problem at document-level.
method Refinement of Fuzzy Bag-of-Words, predefined dimension.
result Competitive performances in Semantic Textual Similarity benchmarks.
Models improve syntactic clustering by adding more translation and part-of-speech decoders.
problem Improving syntactic saliency in hidden sentence representations.
method Training multi-task autoencoders on linguistic tasks and analyzing the learned hidden representations.
result The representation space becomes less entangled with more decoders, leading to better syntactic clustering.
This study identifies sentence relationships in legal transcripts.
problem Improving understanding of legal case proceedings through sentence relationships.
method Combining machine learning and rule-based approach to classify sentence relationships.
result First study to use discourse relationships for legal court case transcripts.
This paper explores sentence vector properties for automatic summarization.
problem Understanding the internal structure and properties of sentence vectors.
method Compositional sentence vector representations using artificial neural networks.
result Cosine similarity correlates with sentence importance and can identify gaps in summaries.
This work speeds up unsupervised sentence learning using paragraph coherence.
problem Training fast unsupervised sentence encoders.
method Discourse-based objective function for neural network training.
result Models trained with this method are faster and perform well.
Detects out-of-distribution sentences in Neural Machine Translation.
problem Identifying sentences from a different language than the training data.
method Developed a new uncertainty measure for long sequences of words in Transformers.
result Shows ability to identify Dutch sentences as German input.
XL-Editor improves sentence post-editing using XLNet's variable-length insertion probability.
problem Post-editing sentences to refine generated text.
method XL-Editor trains XLNet to estimate variable-length insertion probabilities and apply post-editing operations.
result XL-Editor outperforms XLNet on text insertion and deletion tasks, and achieves significant style transfer improvements.
Generates text with specified attributes, improving content compatibility.
problem Modifying textual attributes of sentences while maintaining content compatibility.
method Introduces reconstruction and adversarial losses to generate attribute-compatible, realistic sentences.
result Demonstrates superior content compatibility and attribute control compared to prior methods.
Graph model detects fake news by analyzing sentence interactions.
problem Detecting fake news through online media.
method Graph Neural Network-based model for sentence interactions.
result Our model achieves state-of-the-art accuracy on fake news datasets.
Deep learning models improved sentence similarity in medical records.
problem Improving sentence similarity in electronic medical records.
method Developed models using traditional machine learning and deep learning approaches, pre-trained sentence embeddings on biomedical corpora.
result Ensembled model achieved a Person correlation coefficient of 0.8528.
Paper proposes continual learning for sentence encoders.
problem Optimize sentence encoders for new corpora while maintaining old corpus accuracy.
method Initialize encoders with corpus-independent features, update using Boolean operations of conceptor matrices.
result Proposed sentence encoder can continually learn features from new corpora.
Improved sentence modeling using Suffix Bidirectional LSTM.
problem Sequential bias in BiLSTMs limits long-range dependencies.
method Encodes each suffix and prefix of a sequence in both forward and reverse directions.
result SuBiLSTM improves performance in various NLP tasks.
CGMH uses Metropolis-Hastings sampling to generate sentences with complex constraints.
problem Generating sentences with specific constraints while maintaining fluency and naturalness.
method CGMH employs Metropolis-Hastings sampling for constrained sentence generation.
result CGMH outperforms previous methods in various constrained sentence generation tasks.
New method improves sentence classification using context information.
problem Classifying sentences with limited context information.
method Context-LSTM-CNN method that considers large contexts and long-range dependencies.
result Consistently improves over previous methods on two datasets.
Unified multi-view sentence representation improves downstream tasks.
problem Improving sentence representation learning from diverse views.
method Unified multi-view sentence representation learning framework using RNN and linear model, maximizing agreement with adjacent context.
result Improved representations and transferability on downstream tasks.
Paper uses AC-GAN to generate high-quality relational sentences for relation extraction.
problem Limited training data for relation extraction models.
method Auxiliary Classifier Generative Adversarial Networks (AC-GANs).
result Significantly improved performance of relation extraction.
SIVAE integrates sentences and their syntactic trees for improved text generation.
problem Improving the grammar of generated text.
method SIVAE uses two separate latent spaces for sentences and syntactic trees, optimizing a joint distribution with two encoders and two decoders.
result SIVAE generates sentences with better grammar compared to existing models.
ROTS improves sentence similarity by incorporating structural information.
problem Measuring sentence similarity with theoretical insights and structural awareness.
method Recursive Optimal Transport (ROT) framework to incorporate structural information.
result ROTS outperforms weakly supervised approaches in sentence similarity tasks.
SemSentSum uses embeddings to link facts across documents efficiently.
problem Linking facts across documents is challenging due to language variability.
method Develops SemSentSum, a fully data-driven model using universal and domain-specific sentence embeddings to build a semantic relation graph.
result SemSentSum achieves competitive results on multi-document summarization tasks.
Proposes a new RNN for language generation capturing long-range dependencies.
problem Capturing long-range word dependencies and sentence order in text corpora.
method Recurrent Hierarchical Topic-Guided RNN with dynamic deep topic model.
result Outperforms larger-context RNN-based language models and learns interpretable topics.
Paper introduces context-sensitive filters for better text processing.
problem Static filters limit NLP performance; need dynamic context.
method Meta network learns context-aware filters for sentences.
result Context-sensitive filters improve NLP tasks.
Improves document summarization by combining word embeddings and n-grams.
problem Exact word matching fails to measure semantic similarity between sentences.
method Uses deep embedding features and tf-idf features to improve sentence similarity measure; builds an improved sentence similarity graph; employs a submodular objective function; develops a Transformer-based compression model.
result Outperforms tf-idf based approach and achieves state-of-the-art performance on DUC04 dataset.
The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically se…
Unsupervised scheme ranks sentences in text documents based on semantic importance.
problem Ranking sentences in text documents without labeled data.
method Extracts essential words and phrases, constructs semantic phrase and sentence graphs, applies PageRank, combines scores, and optimizes for topic diversity.
result SSR outperforms individual judges and compares favorably with combined rankings on benchmarks.
PAC-Bayes analysis explains sentence vector learning from unlabeled data.
problem Understanding and improving sentence vector learning from unlabeled data.
method PAC-Bayes bound analysis for transfer learning.
result Simple heuristics and new algorithms derived from PAC-Bayes analysis.
Unified model improves coherence tasks, especially local contexts.
problem Existing neural coherence models struggle with local context tasks.
method Unified neural framework integrating grammar, relations, and patterns.
result Unified model outperforms existing models significantly.
Generative model controls text attributes for realistic sentences.
problem Challenges in generating natural language sentences with desired attributes.
method Combines variational auto-encoders and holistic attribute discriminators for semantic structure imposition.
result Effective generation of realistic sentences with desired attributes.
CERT improves language understanding by contrastively learning sentence-level semantics.
problem Lack of sentence-level semantics in existing pretraining tasks.
method Contrastive self-supervised learning at the sentence level using back-translation augmentations.
result CERT outperforms BERT on 7 out of 11 GLUE benchmark tasks, achieving the same performance as BERT on 2 tasks.
Paper proposes a new method for sentence embeddings using weighted word vectors.
problem Improving sentence embeddings for natural language processing tasks.
method A simple sentence embedding method using weighted average of word vectors followed by soft projection.
result Demonstrates effectiveness on clinical semantic textual similarity task.
System tackles indeterminacies in automated audio captioning.
problem Word selection and sentence length indeterminacies in automated audio captioning.
method Solves caption generation and sub-indeterminacy problems through multi-task learning to estimate keywords and sentence length.
result Model achieved 20.7 SPIDEr score, significantly outperforming baseline.
Extracts parallel sentences for machine translation.
problem Data sparsity in multilingual natural language processing.
method Bidirectional recurrent neural network approach.
result Significant improvements in machine translation performance.
Seq-CVAE learns a latent space for each word position to capture sentence intention.
problem Capturing diversity in image captioning models.
method Seq-CVAE learns a sequential latent space for each word position, mimicking future sentence summaries.
result Significantly improves diversity metrics on MSCOCO dataset compared to baselines.
We propose a new statistical model for computational linguistics. Rather than trying to estimate directly the probability distribution of a random sentence of the language, we define a Markov chain on finite sets of sentences with many finite recurrent communicating classes and define our language model as the invarian…
Systems rank PubMed abstracts and sentences for RDoC criteria, achieving high mAP and MAA.
problem Lack of RDoC labeled datasets and complex labelling process hinder full use of RDoC framework.
method Attention-based neural topic models, supervised and unsupervised sentence ranking models, BM25, BoW, TF-IDF.
result Best systems achieved 1st rank with 0.86 mAP and 0.58 MAA.
Framework learns sentence order from paragraphs using attention and transformer networks.
problem Learning to order sentences from a paragraph.
method Bidirectional sentence encoder and self-attention transformer network for ranking.
result Framework outperforms state-of-the-art methods on sentence ordering and discrimination tasks.