This work tackles extractive compression by formulating it as tree transduction.
problem Extractive compression as a challenging natural language processing problem.
method Formulated as a parse tree transduction problem, using a deep neural model with Long Short-Term Memory extended to consider parent-child relationships.
result Achieves state-of-the-art performance on sentence compression benchmarks.
Compression method reduces word embedding size for NLP models.
problem Memory constraints in deploying deep learning models for NLP tasks.
method Low rank matrix factorization during training to compress word embeddings.
result 90% compression with minimal accuracy loss for sentence classification 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.
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.
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.
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…
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.
Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sente…
We propose a new generative model of sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional models that generate from scratch either left-to-right or by first sampling a latent sentence vector, our prototype-then-edit model improves perp…
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.
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.
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.
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.
New ODE-Block handles stateful layers with continuous-in-depth functions using basis functions.
problem Handling stateful layers in ODE-Nets.
method Formulate ODE-Block using continuous-in-depth functions with basis function expansions.
result Enables state-of-the-art performance and reduces memory footprint.
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.
In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a …
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.
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.
We train multi-task autoencoders on linguistic tasks and analyze the learned hidden sentence representations. The representations change significantly when translation and part-of-speech decoders are added. The more decoders a model employs, the better it clusters sentences according to their syntactic similarity, as t…
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.
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.
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.
Existing relation classification methods that rely on distant supervision assume that a bag of sentences mentioning an entity pair are all describing a relation for the entity pair. Such methods, performing classification at the bag level, cannot identify the mapping between a relation and a sentence, and largely suffe…
Case Law has a significant impact on the proceedings of legal cases. Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties. This paper describes a methodology of applying discourse relations between sentences when processi…
We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word level generative model for natural language. It uses a recurrent neural network with a dynamic attention and canvas memory mechanism to iteratively construct sentences. By viewing the state of the memory at intermediate stages and where the m…
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.
Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our methodology for mining such data from previously obtained comparable corpora. The task is highly practical since non-parallel m…
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.
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.
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.
We present a new topic model that generates documents by sampling a topic for one whole sentence at a time, and generating the words in the sentence using an RNN decoder that is conditioned on the topic of the sentence. We argue that this novel formalism will help us not only visualize and model the topical discourse s…
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.
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.
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.
Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications. We propose a bidirectional recurrent neural network based approach to extract parallel sentences from collections of multilingual texts. Our experiments with noisy parallel corpora…
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.
Randomly initialized sentence encoders perform well on tasks, suggesting learning is key.
problem The role of sentence encoder architectures in language tasks.
method Random initialization and fixed architecture approach to evaluate sentence encoders.
result Priors do not leverage additional information, learning is necessary.
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.
Method compares sentences by cosine similarity of vector projections.
problem Measuring semantic similarity of sentences.
method Cosine similarity of vector projections of sentence groups.
result Advantages over existing methods in preserving word order and syntactic connections.
Recurrent models for sequences have been recently successful at many tasks, especially for language modeling and machine translation. Nevertheless, it remains challenging to extract good representations from these models. For instance, even though language has a clear hierarchical structure going from characters throug…
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…
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.
We present PubMed 200k RCT, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following …
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.
Multi-view learning can provide self-supervision when different views are available of the same data. The distributional hypothesis provides another form of useful self-supervision from adjacent sentences which are plentiful in large unlabelled corpora. Motivated by the asymmetry in the two hemispheres of the human bra…
Novel CNN array for sign language recognition using wearable IMUs.
problem Efficiently recognizing sign language from wearable IMU signals.
method Two-dimensional Convolutional Neural Network array architecture for Indian sign language recognition.
result Peak classification accuracies of 94.20% for general sentences and 95.00% for interrogative sentences achieved.
Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP share the same learned (and static) set of filters for all input sentences. In this paper, we consider an approach of using a smal…