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Found 14 papers, 1 papers with code
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.
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.
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.
A Simple but Effective Method to Incorporate Multi-turn Context with BERT for Conversational Machine Comprehension
Conversational machine comprehension (CMC) requires understanding the context of multi-turn dialogue.
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
Commonsense Knowledge Base Completion and Generation
To improve the accuracy of CKB completion and expand the size of CKBs, we formulate a new commonsense knowledge base generation task (CKB generation) and propose a joint learning method that incorporates both CKB completion and CKB generation.
Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension
Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages.
Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels
In this study, we investigated the effectiveness of augmented data for encoder-decoder-based neural normalization models.
Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization
Social media texts, such as tweets from Twitter, contain many types of non-standard tokens, and the number of normalization approaches for handling such noisy text has been increasing.
Name Translation based on Fine-grained Named Entity Recognition in a Single Language
We propose named entity abstraction methods with fine-grained named entity labels for improving statistical machine translation (SMT).
