Benjamin Van Durme
14 papers in the PaperMetrix corpus
Papers by this author
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A Unified Bayesian Model of Scripts, Frames and Language
2016 · Proceedings of the AAAI Conference on Artificial Intelligence
We present the first probabilistic model to capture all levels of the Minsky Frame structure, with the goal of corpus-based induction of scenario definitions. Our model unifies prior efforts in discourse-level modeling with that of …
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Predicting the Argumenthood of English Prepositional Phrases
2018 · arXiv (Cornell University)
Distinguishing between arguments and adjuncts of a verb is a longstanding, nontrivial problem. In natural language processing, argumenthood information is important in tasks such as semantic role labeling (SRL) and prepositional phrase (PP) attachment disambiguation. …
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On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
2019
Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be …
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BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation
2021 · arXiv (Cornell University)
The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate these pre-trained models into neural machine translation (NMT) systems. However, …
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Adapting Coreference Resolution Models through Active Learning
2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Neural coreference resolution models trained on one dataset may not transfer to new, lowresource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is …
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Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation
2022 · arXiv (Cornell University)
We introduce a novel setup for low-resource task-oriented semantic parsing which incorporates several constraints that may arise in real-world scenarios: (1) lack of similar datasets/models from a related domain, (2) inability to sample useful logical …
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MegaWika: Millions of reports and their sources across 50 diverse languages
2023 · arXiv (Cornell University)
To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials. We process …
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The Effect of Alignment Correction on Cross-Lingual Annotation Projection
2023
Cross-lingual annotation projection is a practical method for improving performance on low resource structured prediction tasks. An important step in annotation projection is obtaining alignments between the source and target texts, which enables the mapping …
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Dated Data: Tracing Knowledge Cutoffs in Large Language Models
2024 · arXiv (Cornell University)
Released Large Language Models (LLMs) are often paired with a claimed knowledge cutoff date, or the dates at which training data was gathered. Such information is crucial for applications where the LLM must provide up …
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Social Bias in Elicited Natural Language Inferences
2017
We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data. The human-elicitation protocol employed in the construction of the SNLI makes it prone to amplifying bias …
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ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension
2018 · arXiv (Cornell University)
We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for …
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Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
2019
Najoung Kim, Roma Patel, Adam Poliak, Patrick Xia, Alex Wang, Tom McCoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel R. Bowman, Ellie Pavlick. Proceedings of the Eighth Joint Conference on Lexical and …
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Can You Tell Me How to Get Past Sesame Street? Sentence-Level Pretraining Beyond Language Modeling
2019
Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman. …
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What do you learn from context? Probing for sentence structure in\n contextualized word representations
2019 · arXiv (Cornell University)
Contextualized representation models such as ELMo (Peters et al., 2018a) and\nBERT (Devlin et al., 2018) have recently achieved state-of-the-art results on a\ndiverse array of downstream NLP tasks. Building on recent token-level probing\nwork, we introduce a …