Jad Kabbara


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Computational Investigations of Pragmatic Effects in Natural Language
Jad Kabbara
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

Semantics and pragmatics are two complimentary and intertwined aspects of meaning in language. The former is concerned with the literal (context-free) meaning of words and sentences, the latter focuses on the intended meaning, one that is context-dependent. While NLP research has focused in the past mostly on semantics, the goal of this thesis is to develop computational models that leverage this pragmatic knowledge in language that is crucial to performing many NLP tasks correctly. In this proposal, we begin by reviewing the current progress in this thesis, namely, on the tasks of definiteness prediction and adverbial presupposition triggering. Then we discuss the proposed research for the remainder of the thesis which builds on this progress towards the goal of building better and more pragmatically-aware natural language generation and understanding systems.


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Let’s do it “again”: A First Computational Approach to Detecting Adverbial Presupposition Triggers
Andre Cianflone | Yulan Feng | Jad Kabbara | Jackie Chi Kit Cheung
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization 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.


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Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs
Jad Kabbara | Yulan Feng | Jackie Chi Kit Cheung
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We examine the potential of recurrent neural networks for handling pragmatic inferences involving complex contextual cues for the task of article usage prediction. We train and compare several variants of Long Short-Term Memory (LSTM) networks with an attention mechanism. Our model outperforms a previous state-of-the-art system, achieving up to 96.63% accuracy on the WSJ/PTB corpus. In addition, we perform a series of analyses to understand the impact of various model choices. We find that the gain in performance can be attributed to the ability of LSTMs to pick up on contextual cues, both local and further away in distance, and that the model is able to solve cases involving reasoning about coreference and synonymy. We also show how the attention mechanism contributes to the interpretability of the model’s effectiveness.

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Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks
Jad Kabbara | Jackie Chi Kit Cheung
Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods