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Found 12 papers, 0 papers with code
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.
Natural Language Inference with Definition Embedding Considering Context On the Fly
Natural language inference (NLI) is one of the most important tasks in NLP.
A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences
This paper describes a hierarchical neural network we propose for sentence classification to extract product information from product documents.
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).
Constructing a Corpus of Japanese Predicate Phrases for Synonym/Antonym Relations
We construct a large corpus of Japanese predicate phrases for synonym-antonym relations.
