Search Results for author:
Found 32 papers, 21 papers with code
Inducing and Using Alignments for Transition-based AMR Parsing
These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints.
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings
We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings.
Ranked #7 on
Semantic Textual Similarity
on STS16
Controlling the Focus of Pretrained Language Generation Models
This work aims to develop a control mechanism by which a user can select spans of context as "highlights" for the model to focus on, and generate relevant output.
Co-training Improves Prompt-based Learning for Large Language Models
We demonstrate that co-training (Blum & Mitchell, 1998) can improve the performance of prompt-based learning by using unlabeled data.
Sequence-to-Sequence Learning with Latent Neural Grammars
While flexible and performant, these models often require large datasets for training and can fail spectacularly on benchmarks designed to test for compositional generalization.
Developmental Stage Classification of EmbryosUsing Two-Stream Neural Network with Linear-Chain Conditional Random Field
In this work, we propose a two-stream model for developmental stage classification.
Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models
While vector-based language representations from pretrained language models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workings.
Representational correlates of hierarchical phrase structure in deep language models
Importing from computational and cognitive neuroscience the notion of representational invariance, we perform a series of probes designed to test the sensitivity of Transformer representations to several kinds of structure in sentences.
Parameter-Efficient Transfer Learning with Diff Pruning
This approach views finetuning as learning a task-specific diff vector that is applied on top of the pretrained parameter vector, which remains fixed and is shared across different tasks.
Sequence-Level Mixed Sample Data Augmentation
Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language.
Emergence of Separable Manifolds in Deep Language Representations
In addition, we find that the emergence of linear separability in these manifolds is driven by a combined reduction of manifolds' radius, dimensionality and inter-manifold correlations.
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
One of the most surprising and exciting discoveries in supervised learning was the benefit of overparameterization (i. e. training a very large model) to improving the optimization landscape of a problem, with minimal effect on statistical performance (i. e. generalization).
Compound Probabilistic Context-Free Grammars for Grammar Induction
We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context-free grammar.
Ranked #5 on
Constituency Grammar Induction
on PTB
Amortized Bethe Free Energy Minimization for Learning MRFs
We propose to learn deep undirected graphical models (i. e., MRFs) with a non-ELBO objective for which we can calculate exact gradients.
Unsupervised Recurrent Neural Network Grammars
On language modeling, unsupervised RNNGs perform as well their supervised counterparts on benchmarks in English and Chinese.
Ranked #6 on
Constituency Grammar Induction
on PTB
(Max F1 (WSJ) metric)
A Tutorial on Deep Latent Variable Models of Natural Language
There has been much recent, exciting work on combining the complementary strengths of latent variable models and deep learning.
Avoiding Latent Variable Collapse With Generative Skip Models
VAEs can capture complex distributions, but they can also suffer from an issue known as "latent variable collapse," especially if the likelihood model is powerful.
Latent Alignment and Variational Attention
This work considers variational attention networks, alternatives to soft and hard attention for learning latent variable alignment models, with tighter approximation bounds based on amortized variational inference.
Ranked #23 on
Machine Translation
on IWSLT2014 German-English
On the Flip Side: Identifying Counterexamples in Visual Question Answering
Visual question answering (VQA) models respond to open-ended natural language questions about images.
OpenNMT: Neural Machine Translation Toolkit
OpenNMT is an open-source toolkit for neural machine translation (NMT).
Semi-Amortized Variational Autoencoders
Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network.
Ranked #2 on
Text Generation
on Yahoo Questions
OpenNMT: Open-source Toolkit for Neural Machine Translation
We introduce an open-source toolkit for neural machine translation (NMT) to support research into model architectures, feature representations, and source modalities, while maintaining competitive performance, modularity and reasonable training requirements.
Adapting Sequence Models for Sentence Correction
In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches.
Adversarially Regularized Autoencoders
This adversarially regularized autoencoder (ARAE) allows us to generate natural textual outputs as well as perform manipulations in the latent space to induce change in the output space.
Structured Attention Networks
Attention networks have proven to be an effective approach for embedding categorical inference within a deep neural network.
OpenNMT: Open-Source Toolkit for Neural Machine Translation
We describe an open-source toolkit for neural machine translation (NMT).
Sequence-Level Knowledge Distillation
We demonstrate that standard knowledge distillation applied to word-level prediction can be effective for NMT, and also introduce two novel sequence-level versions of knowledge distillation that further improve performance, and somewhat surprisingly, seem to eliminate the need for beam search (even when applied on the original teacher model).
Ranked #1 on
Machine Translation
on IWSLT2015 Thai-English
Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction
We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016.
Character-Aware Neural Language Models
We describe a simple neural language model that relies only on character-level inputs.
Convolutional Neural Networks for Sentence Classification
We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.
Ranked #33 on
Natural Language Inference
on SNLI
Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification
We provide a simple but novel supervised weighting scheme for adjusting term frequency in tf-idf for sentiment analysis and text classification.
Temporal Analysis of Language through Neural Language Models
We provide a method for automatically detecting change in language across time through a chronologically trained neural language model.
