no code implementations • COLING (CRAC) 2022 • Hideo Kobayashi, Christopher Malon
Product reviews may have complex discourse including coreference and bridging relations to a main product, competing products, and interacting products.
no code implementations • EMNLP (FEVER) 2021 • Christopher Malon
We develop a system for the FEVEROUS fact extraction and verification task that ranks an initial set of potential evidence and then pursues missing evidence in subsequent hops by trying to generate it, with a “next hop prediction module” whose output is matched against page elements in a predicted article.
no code implementations • Findings (EMNLP) 2021 • Zhan Shi, Hui Liu, Martin Renqiang Min, Christopher Malon, Li Erran Li, Xiaodan Zhu
Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training.
no code implementations • 23 Aug 2022 • Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim, PengFei Liu, Christopher Malon, Daniel Ruffinelli, Carolin Lawrence, Graham Neubig
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples.
1 code implementation • DeeLIO (ACL) 2022 • 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.
no code implementations • Joint Conference on Lexical and Computational Semantics 2021 • 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 pretraining.
Natural Language Inference Natural Language Understanding +1
no code implementations • 28 Jan 2021 • Bingyuan Liu, Christopher Malon, Lingzhou Xue, Erik Kruus
Finally, we empirically show that our designed network architecture is more robust against state-of-art gradient descent based attacks, such as a PGD attack on the benchmark datasets MNIST and CIFAR10.
no code implementations • ACL 2020 • Pengyu Cheng, Martin Renqiang Min, Dinghan Shen, Christopher Malon, Yizhe Zhang, Yitong Li, Lawrence Carin
Learning disentangled representations of natural language is essential for many NLP tasks, e. g., conditional text generation, style transfer, personalized dialogue systems, etc.
no code implementations • 27 Feb 2020 • Christopher Malon, Bing Bai
As a first instantiation of this framework, we train a pointer-generator network to predict followup questions based on the question and partial information.
1 code implementation • WS 2018 • 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 modeling, to classify a broad set of potential evidence.
no code implementations • WS 2018 • Ju-ho Kim, Christopher Malon, Asim Kadav
Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order.