Search Results for author:
Found 12 papers, 1 papers with code
Robust Text-driven Image Editing Method that Adaptively Explores Directions in Latent Spaces of StyleGAN and CLIP
Our model represents the edit direction as a normal vector in the CLIP space obtained by training a SVM to classify positive and negative images.
SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images
Visual question answering on document images that contain textual, visual, and layout information, called document VQA, has received much attention recently.
Self-Adaptive Named Entity Recognition by Retrieving Unstructured Knowledge
Although named entity recognition (NER) helps us to extract domain-specific entities from text (e. g., artists in the music domain), it is costly to create a large amount of training data or a structured knowledge base to perform accurate NER in the target domain.
Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions
Our proposed model, LIDE (Learning from Image and DEscription), has a text decoder to generate the descriptions and a text encoder to obtain the text representations of machine- or user-generated descriptions.
Towards Interpretable and Reliable Reading Comprehension: A Pipeline Model with Unanswerability Prediction
In this study, we define an interpretable reading comprehension (IRC) model as a pipeline model with the capability of predicting unanswerable queries.
Task-adaptive Pre-training of Language Models with Word Embedding Regularization
TAPTER runs additional pre-training by making the static word embeddings of a PTLM close to the word embeddings obtained in the target domain with fastText.
Abstractive Summarization with Combination of Pre-trained Sequence-to-Sequence and Saliency Models
Experimental results showed that most of the combination models outperformed a simple fine-tuned seq-to-seq model on both the CNN/DM and XSum datasets even if the seq-to-seq model is pre-trained on large-scale corpora.
Length-controllable Abstractive Summarization by Guiding with Summary Prototype
Unlike the previous models, our length-controllable abstractive summarization model incorporates a word-level extractive module in the encoder-decoder model instead of length embeddings.
Unsupervised Domain Adaptation of Language Models for Reading Comprehension
The second one is the proposed model that uses a multi-task learning approach of LM and RC.
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction
It enables QFE to consider the dependency among the evidence sentences and cover important information in the question sentence.
Ranked #61 on
Question Answering
on HotpotQA
Multi-style Generative Reading Comprehension
Second, whereas previous studies built a specific model for each answer style because of the difficulty of acquiring one general model, our approach learns multi-style answers within a model to improve the NLG capability for all styles involved.
Ranked #1 on
Question Answering
on MS MARCO
Natural Language Inference with Definition Embedding Considering Context On the Fly
Natural language inference (NLI) is one of the most important tasks in NLP.
