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Found 28 papers, 19 papers with code
GarmentAligner: Text-to-Garment Generation via Retrieval-augmented Multi-level Corrections
General text-to-image models bring revolutionary innovation to the fields of arts, design, and media.
CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
Virtual try-on methods based on diffusion models achieve realistic try-on effects but often replicate the backbone network as a ReferenceNet or use additional image encoders to process condition inputs, leading to high training and inference costs.
Kernel Semi-Implicit Variational Inference
This way, the upper-level objective becomes the kernel Stein discrepancy (KSD), which is readily computable for stochastic gradient descent due to the hierarchical structure of semi-implicit variational distributions.
Reflected Flow Matching
We propose reflected flow matching (RFM) to train the velocity model in reflected CNFs by matching the conditional velocity fields in a simulation-free manner, similar to the vanilla FM.
DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment
Cross-modal garment synthesis and manipulation will significantly benefit the way fashion designers generate garments and modify their designs via flexible linguistic interfaces. Current approaches follow the general text-to-image paradigm and mine cross-modal relations via simple cross-attention modules, neglecting the structural correspondence between visual and textual representations in the fashion design domain.
MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies
Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P -- that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE).
HistAlign: Improving Context Dependency in Language Generation by Aligning with History
Cache-LMs, which augment LMs with a memory of recent history, can increase context dependency and have shown remarkable performance in diverse language generation tasks.
Evaluating the Factual Consistency of Large Language Models Through News Summarization
To generate summaries that are factually inconsistent, we generate summaries from a suite of summarization models that we have manually annotated as factually inconsistent.
Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees
We demonstrate that SP-Search effectively represents the generative process behind human summaries using modules that are typically faithful to their intended behavior.
Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization
Though extractive summarization is less prone to the common unfaithfulness issues of abstractive summaries, does that mean extractive is equal to faithful?
SETSum: Summarization and Visualization of Student Evaluations of Teaching
Ten university professors from diverse departments serve as evaluators of the system and all agree that SETSum helps them interpret SET results more efficiently; and 6 out of 10 instructors prefer our system over the standard static PDF report (while the remaining 4 would like to have both).
Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging?
MPoSM can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction.
How Robust is Neural Machine Translation to Language Imbalance in Multilingual Tokenizer Training?
Since a skewed data distribution is considered to be harmful, a sampling strategy is usually used to balance languages in the corpus.
How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language
We hope that our work serves not only to inform the NLP community about Cherokee, but also to provide inspiration for future work on endangered languages in general.
Finding a Balanced Degree of Automation for Summary Evaluation
In this work, we propose flexible semiautomatic to automatic summary evaluation metrics, following the Pyramid human evaluation method.
Continuous Language Generative Flow
Recent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformer based sequence-to-sequence models, as well as variational autoencoder (VAE) and generative adversarial network (GAN) based models.
EmailSum: Abstractive Email Thread Summarization
Furthermore, we find that widely used automatic evaluation metrics (ROUGE, BERTScore) are weakly correlated with human judgments on this email thread summarization task.
Ranked #1 on
Email Thread Summarization
on EmailSum (short)
ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback
The quantitative evaluation demonstrates that our backbone translation models achieve state-of-the-art translation performance and our quality estimation well correlates with both BLEU and human judgment.
ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization
To help save this endangered language, we introduce ChrEn, a Cherokee-English parallel dataset, to facilitate machine translation research between Cherokee and English.
Cultural Vocal Bursts Intensity Prediction
Language Modelling
+5
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?
We provide code for the experiments in this paper at https://github. com/peterbhase/LAS-NL-Explanations
Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering
Second, since the traditional evaluation metrics (e. g., BLEU) often fall short in evaluating the quality of generated questions, we propose a QA-based evaluation method which measures the QG model's ability to mimic human annotators in generating QA training data.
Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative Learning
In hospital, physicians rely on massive clinical data to make diagnosis decisions, among which laboratory tests are one of the most important resources.
Enhanced Neural Machine Translation by Learning from Draft
Neural machine translation (NMT) has recently achieved impressive results.
Memory-augmented Neural Machine Translation
Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs.
Memory-augmented Chinese-Uyghur Neural Machine Translation
Neural machine translation (NMT) has achieved notable performance recently.
Flexible and Creative Chinese Poetry Generation Using Neural Memory
It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism.
Memory Visualization for Gated Recurrent Neural Networks in Speech Recognition
Recurrent neural networks (RNNs) have shown clear superiority in sequence modeling, particularly the ones with gated units, such as long short-term memory (LSTM) and gated recurrent unit (GRU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Collaborative Learning for Language and Speaker Recognition
This paper presents a unified model to perform language and speaker recognition simultaneously and altogether.
