Search Results for author:
Found 8 papers, 4 papers with code
Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning
Unsupervised image captioning is a challenging task that aims at generating captions without the supervision of image-sentence pairs, but only with images and sentences drawn from different sources and object labels detected from the images.
Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning
Discriminativeness is a desirable feature of image captions: captions should describe the characteristic details of input images.
On the True Distribution Approximation of Minimum Bayes-Risk Decoding
Minimum Bayes-risk (MBR) decoding has recently gained renewed attention in text generation.
Pruning Basic Elements for Better Automatic Evaluation of Summaries
We propose a simple but highly effective automatic evaluation measure of summarization, pruned Basic Elements (pBE).
On the Depth between Beam Search and Exhaustive Search for Text Generation
To this end, we introduce Lookahead Beam Search (LBS), a multi-step lookahead search that optimizes the objective considering a fixed number of future steps.
Model-Based Minimum Bayes Risk Decoding
MBR decoding selects a hypothesis from a pool of hypotheses that has the least expected risk under a probability model according to a given utility function.
Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding
We propose two variants of MBR, Diverse MBR (DMBR) and $k$-medoids MBR (KMBR), methods to generate a set of sentences with high quality and diversity.
A Single Linear Layer Yields Task-Adapted Low-Rank Matrices
To confirm this hypothesis, we devise a method named Conditionally Parameterized LoRA (CondLoRA) that updates initial weight matrices with low-rank matrices derived from a single linear layer.
