Search Results for author:
Found 9 papers, 5 papers with code
Investigating the Effect of Background Knowledge on Natural Questions
We create a subset of the NQ data, Factual Questions (FQ), where the questions have evidence in the KB in the form of paths that link question entities to answer entities but still must be answered using text, to facilitate further research into KB integration methods.
“A Little Birdie Told Me ... ” - Inductive Biases for Rumour Stance Detection on Social Media
The rise in the usage of social media has placed it in a central position for news dissemination and consumption.
Unsupervised Keyphrase Extraction via Interpretable Neural Networks
Keyphrase extraction aims at automatically extracting a list of "important" phrases which represent the key concepts in a document.
DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues
To successfully negotiate a deal, it is not enough to communicate fluently: pragmatic planning of persuasive negotiation strategies is essential.
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics
Modern summarization models generate highly fluent but often factually unreliable outputs.
Simple and Efficient ways to Improve REALM
Dense retrieval has been shown to be effective for retrieving relevant documents for Open Domain QA, surpassing popular sparse retrieval methods like BM25.
SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers
We introduce SelfExplain, a novel self-explaining model that explains a text classifier's predictions using phrase-based concepts.
StructSum: Summarization via Structured Representations
To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models.
Differentiable Reasoning over a Virtual Knowledge Base
In particular, we describe a neural module, DrKIT, that traverses textual data like a KB, softly following paths of relations between mentions of entities in the corpus.
