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        <title>Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs</title>
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        <namePart type="given">Jad</namePart>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs
%A Kabbara, Jad
%A Feng, Yulan
%A Cheung, Jackie Chi Kit
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F kabbara-etal-2016-capturing
%X 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.
%U https://aclanthology.org/C16-1247
%P 2625-2634

Markdown (Informal)

[Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs](https://aclanthology.org/C16-1247) (Kabbara et al., COLING 2016)

• Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs (Kabbara et al., COLING 2016)

ACL

• Jad Kabbara, Yulan Feng, and Jackie Chi Kit Cheung. 2016. Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2625–2634, Osaka, Japan. The COLING 2016 Organizing Committee.