Andrew Wang


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Can Authorship Representation Learning Capture Stylistic Features?
Andrew Wang | Cristina Aggazzotti | Rebecca Kotula | Rafael Rivera Soto | Marcus Bishop | Nicholas Andrews
Transactions of the Association for Computational Linguistics, Volume 11

Automatically disentangling an author’s style from the content of their writing is a longstanding and possibly insurmountable problem in computational linguistics. At the same time, the availability of large text corpora furnished with author labels has recently enabled learning authorship representations in a purely data-driven manner for authorship attribution, a task that ostensibly depends to a greater extent on encoding writing style than encoding content. However, success on this surrogate task does not ensure that such representations capture writing style since authorship could also be correlated with other latent variables, such as topic. In an effort to better understand the nature of the information these representations convey, and specifically to validate the hypothesis that they chiefly encode writing style, we systematically probe these representations through a series of targeted experiments. The results of these experiments suggest that representations learned for the surrogate authorship prediction task are indeed sensitive to writing style. As a consequence, authorship representations may be expected to be robust to certain kinds of data shift, such as topic drift over time. Additionally, our findings may open the door to downstream applications that require stylistic representations, such as style transfer.


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ConvoKit: A Toolkit for the Analysis of Conversations
Jonathan P. Chang | Caleb Chiam | Liye Fu | Andrew Wang | Justine Zhang | Cristian Danescu-Niculescu-Mizil
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

This paper describes the design and functionality of ConvoKit, an open-source toolkit for analyzing conversations and the social interactions embedded within. ConvoKit provides an unified framework for representing and manipulating conversational data, as well as a large and diverse collection of conversational datasets. By providing an intuitive interface for exploring and interacting with conversational data, this toolkit lowers the technical barriers for the broad adoption of computational methods for conversational analysis.


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The MSR system for IWSLT 2011 evaluation
Xiaodong He | Amittai Axelrod | Li Deng | Alex Acero | Mei-Yuh Hwang | Alisa Nguyen | Andrew Wang | Xiahui Huang
Proceedings of the 8th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the Microsoft Research (MSR) system for the evaluation campaign of the 2011 international workshop on spoken language translation. The evaluation task is to translate TED talks ( This task presents two unique challenges: First, the underlying topic switches sharply from talk to talk. Therefore, the translation system needs to adapt to the current topic quickly and dynamically. Second, only a very small amount of relevant parallel data (transcripts of TED talks) is available. Therefore, it is necessary to perform accurate translation model estimation with limited data. In the preparation for the evaluation, we developed two new methods to attack these problems. Specifically, we developed an unsupervised topic modeling based adaption method for machine translation models. We also developed a discriminative training method to estimate parameters in the generative components of the translation models with limited data. Experimental results show that both methods improve the translation quality. Among all the submissions, ours achieves the best BLEU score in the machine translation Chinese-to-English track (MT_CE) of the IWSLT 2011 evaluation that we participated.