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Jad Kabbara

Alumni

Publications

Investigating the Effect of Pre-finetuning BERT Models on NLI Involving Presuppositions
Jad Kabbara
Jackie Cheung
2023-10-07
EMNLP/2023/Conference (published)
openreview.net
openreview.net
Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences
Jad Kabbara
Jackie Cheung
Presuppositions are assumptions that are taken for granted by an utterance, and identifying them is key to a pragmatic interpretation of lan… (see more)guage. In this paper, we investigate the capabilities of transformer models to perform NLI on cases involving presupposition. First, we present simple heuristics to create alternative “contrastive” test cases based on the ImpPres dataset and investigate the model performance on those test cases. Second, to better understand how the model is making its predictions, we analyze samples from sub-datasets of ImpPres and examine model performance on them. Overall, our findings suggest that NLI-trained transformer models seem to be exploiting specific structural and lexical cues as opposed to performing some kind of pragmatic reasoning.
2022-01-01
COLING (published)
dblp.uni-trier.de
Post-Editing Extractive Summaries by Definiteness Prediction
Jad Kabbara
Jackie Cheung
Extractive summarization has been the main-stay of automatic summarization for decades. Despite all the progress, extractive summarizers sti… (see more)ll suffer from shortcomings including coreference issues arising from extracting sentences away from their original context in the source document. This affects the coherence and readability of extractive summaries. In this work, we propose a lightweight postediting step for extractive summaries that centers around a single linguistic decision: the definiteness of noun phrases. We conduct human evaluation studies that show that human expert judges substantially prefer the output of our proposed system over the original summaries. Moreover, based on an automatic evaluation study, we provide evidence for our system’s ability to generate linguistic decisions that lead to improved extractive summaries. We also draw insights about how the automatic system is exploiting some local cues related to the writing style of the main article texts or summary texts to make the decisions, rather than reasoning about the contexts pragmatically.
2021-01-01
Conference on Empirical Methods in Natural Language Processing (published)
doi.org
Let’s do it “again”: A First Computational Approach to Detecting Adverbial Presupposition Triggers
Andre Cianflone
Yulan Feng
Jad Kabbara
Jackie Cheung
We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as s… (see more)ummarization and dialogue systems. We introduce two new corpora, derived from the Penn Treebank and the Annotated English Gigaword dataset and investigate the use of a novel attention mechanism tailored to this task. Our attention mechanism augments a baseline recurrent neural network without the need for additional trainable parameters, minimizing the added computational cost of our mechanism. We demonstrate that this model statistically outperforms our baselines.
2018-07-01
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (published)
doi.org
arxiv.org