Frederick Liu


2019

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Incorporating Priors with Feature Attribution on Text Classification
Frederick Liu | Besim Avci
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Feature attribution methods, proposed recently, help users interpret the predictions of complex models. Our approach integrates feature attributions into the objective function to allow machine learning practitioners to incorporate priors in model building. To demonstrate the effectiveness our technique, we apply it to two tasks: (1) mitigating unintended bias in text classifiers by neutralizing identity terms; (2) improving classifier performance in scarce data setting by forcing model to focus on toxic terms. Our approach adds an L2 distance loss between feature attributions and task-specific prior values to the objective. Our experiments show that i) a classifier trained with our technique reduces undesired model biases without a tradeoff on the original task; ii) incorporating prior helps model performance in scarce data settings.

2018

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Handling Homographs in Neural Machine Translation
Frederick Liu | Han Lu | Graham Neubig
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context. However, with the advent of neural machine translation (NMT) systems, which can theoretically take into account global sentential context, one may hypothesize that this problem has been alleviated. In this paper, we first provide empirical evidence that existing NMT systems in fact still have significant problems in properly translating ambiguous words. We then proceed to describe methods, inspired by the word sense disambiguation literature, that model the context of the input word with context-aware word embeddings that help to differentiate the word sense before feeding it into the encoder. Experiments on three language pairs demonstrate that such models improve the performance of NMT systems both in terms of BLEU score and in the accuracy of translating homographs.

2017

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Learning Character-level Compositionality with Visual Features
Frederick Liu | Han Lu | Chieh Lo | Graham Neubig
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Previous work has modeled the compositionality of words by creating character-level models of meaning, reducing problems of sparsity for rare words. However, in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts. In this paper, we model this effect by creating embeddings for characters based on their visual characteristics, creating an image for the character and running it through a convolutional neural network to produce a visual character embedding. Experiments on a text classification task demonstrate that such model allows for better processing of instances with rare characters in languages such as Chinese, Japanese, and Korean. Additionally, qualitative analyses demonstrate that our proposed model learns to focus on the parts of characters that carry topical content which resulting in embeddings that are coherent in visual space.

2016

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The Creation and Analysis of a Website Privacy Policy Corpus
Shomir Wilson | Florian Schaub | Aswarth Abhilash Dara | Frederick Liu | Sushain Cherivirala | Pedro Giovanni Leon | Mads Schaarup Andersen | Sebastian Zimmeck | Kanthashree Mysore Sathyendra | N. Cameron Russell | Thomas B. Norton | Eduard Hovy | Joel Reidenberg | Norman Sadeh
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Attention-based Multimodal Neural Machine Translation
Po-Yao Huang | Frederick Liu | Sz-Rung Shiang | Jean Oh | Chris Dyer
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers