Search Results for author:
Found 11 papers, 5 papers with code
Related Work and Citation Text Generation: A Survey
To convince readers of the novelty of their research paper, authors must perform a literature review and compose a coherent story that connects and relates prior works to the current work.
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation
In this paper, we present a high-quality benchmark named multi-source Wizard of Wikipedia (Ms. WoW) for evaluating multi-source dialogue knowledge selection and response generation.
Contextualizing Generated Citation Texts
Abstractive citation text generation is usually framed as an infilling task, where a sequence-to-sequence model is trained to generate a citation given a reference paper and the context window around the target; the generated citation should be a brief discussion of the reference paper as it relates to the citing context.
Explaining Relationships Among Research Papers
Due to the rapid pace of research publications, keeping up to date with all the latest related papers is very time-consuming, even with daily feed tools.
Cited Text Spans for Citation Text Generation
Because manual CTS annotation is extremely time- and labor-intensive, we experiment with distant labeling of candidate CTS sentences, achieving sufficiently strong performance to substitute for expensive human annotations in model training, and we propose a human-in-the-loop, keyword-based CTS retrieval approach that makes generating citation texts grounded in the full text of cited papers both promising and practical.
CORWA: A Citation-Oriented Related Work Annotation Dataset
As a first step toward a linguistically-motivated related work generation framework, we present a Citation Oriented Related Work Annotation (CORWA) dataset that labels different types of citation text fragments from different information sources.
Automatic Related Work Generation: A Meta Study
In this survey, we conduct a meta-study to compare the existing literature on related work generation from the perspectives of problem formulation, dataset collection, methodological approach, performance evaluation, and future prospects to provide the reader insight into the progress of the state-of-the-art studies, as well as and how future studies can be conducted.
CASPR: A Commonsense Reasoning-based Conversational Socialbot
We present the philosophy behind CASPR's design as well as details of its implementation.
A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification
Even for domain experts, it is a non-trivial task to verify a scientific claim by providing supporting or refuting evidence rationales.
Context-aware Stand-alone Neural Spelling Correction
Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings.
Scientific Discourse Tagging for Evidence Extraction
We apply richly contextualized deep representation learning pre-trained on biomedical domain corpus to the analysis of scientific discourse structures and the extraction of "evidence fragments" (i. e., the text in the results section describing data presented in a specified subfigure) from a set of biomedical experimental research articles.
