4 citations · 10 across the 7 of their papers we have counts for
9 papers
Automatically Evaluating Opinion Prevalence in Opinion Summarization
Christopher Malon
When faced with a large number of product reviews, it is not clear that a human can remember all of them and weight opinions representatively to write a good reference summary. We…
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models
Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim +5
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) task…
Fast Few-shot Debugging for NLU Test Suites
Christopher Malon, Kai Li, Erik Kruus
We study few-shot debugging of transformer based natural language understanding models, using recently popularized test suites to not just diagnose but correct a problem. Given a f…
Overcoming Poor Word Embeddings with Word Definitions
Christopher Malon
Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretrain…
Improving Neural Network Robustness through Neighborhood Preserving Layers
Bingyuan Liu, Christopher Malon, Lingzhou Xue +1
Robustness against adversarial attack in neural networks is an important research topic in the machine learning community. We observe one major source of vulnerability of neural ne…
Improving Disentangled Text Representation Learning with Information-Theoretic Guidance
Pengyu Cheng, Martin Renqiang Min, Dinghan Shen +4
Learning disentangled representations of natural language is essential for many NLP tasks, e.g., conditional text generation, style transfer, personalized dialogue systems, etc. Si…
Generating Followup Questions for Interpretable Multi-hop Question Answering
Christopher Malon, Bing Bai
We propose a framework for answering open domain multi-hop questions in which partial information is read and used to generate followup questions, to finally be answered by a pretr…
Team Papelo: Transformer Networks at FEVER
Christopher Malon
We develop a system for the FEVER fact extraction and verification challenge that uses a high precision entailment classifier based on transformer networks pretrained with language…
Teaching Syntax by Adversarial Distraction
Juho Kim, Christopher Malon, Asim Kadav
Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order. Learning syntax requires comparing examples where different gram…