Search Results for author: Christopher Malon

Found 13 papers, 2 papers with code

Analyzing Coreference and Bridging in Product Reviews

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.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)

Team Papelo at FEVEROUS: Multi-hop Evidence Pursuit

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.

Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning

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.

Image Captioning Retrieval

Automatically Evaluating Opinion Prevalence in Opinion Summarization

no code implementations26 Jul 2023 Christopher Malon

Finally, we show that preprocessing source reviews by simplification can raise the opinion prevalence achieved by existing abstractive opinion summarization systems to the level of human performance.

Unsupervised Opinion Summarization

Fast Few-shot Debugging for NLU Test Suites

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.

Natural Language Understanding Test

Overcoming Poor Word Embeddings with Word Definitions

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

Improving Neural Network Robustness through Neighborhood Preserving Layers

no code implementations28 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.

Adversarial Attack

Improving Disentangled Text Representation Learning with Information-Theoretic Guidance

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.

Conditional Text Generation Representation Learning +2

Generating Followup Questions for Interpretable Multi-hop Question Answering

no code implementations27 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.

Multi-hop Question Answering Question Answering +4

Team Papelo: Transformer Networks at FEVER

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.

Language Modelling Test

Teaching Syntax by Adversarial Distraction

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.

General Classification

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