Alicia Tsai


2021

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Style Control for Schema-Guided Natural Language Generation
Alicia Tsai | Shereen Oraby | Vittorio Perera | Jiun-Yu Kao | Yuheng Du | Anjali Narayan-Chen | Tagyoung Chung | Dilek Hakkani-Tur
Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI

Natural Language Generation (NLG) for task-oriented dialogue systems focuses on communicating specific content accurately, fluently, and coherently. While these attributes are crucial for a successful dialogue, it is also desirable to simultaneously accomplish specific stylistic goals, such as response length, point-of-view, descriptiveness, sentiment, formality, and empathy. In this work, we focus on stylistic control and evaluation for schema-guided NLG, with joint goals of achieving both semantic and stylistic control. We experiment in detail with various controlled generation methods for large pretrained language models: specifically, conditional training, guided fine-tuning, and guided decoding. We discuss their advantages and limitations, and evaluate them with a broad range of automatic and human evaluation metrics. Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods. The results also suggest that methods that are more scalable (with less hyper-parameters tuning) and that disentangle context generation and stylistic variations are more effective at achieving semantic correctness and style accuracy.

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Proceedings of the Fifth Workshop on Widening Natural Language Processing
Erika Varis | Ryan Georgi | Alicia Tsai | Antonios Anastasopoulos | Kyathi Chandu | Xanda Schofield | Surangika Ranathunga | Haley Lepp | Tirthankar Ghosal
Proceedings of the Fifth Workshop on Widening Natural Language Processing

2020

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Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm
Alicia Tsai | Laurent El Ghaoui
Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing

We address the problem of unsupervised extractive document summarization, especially for long documents. We model the unsupervised problem as a sparse auto-regression one and approximate the resulting combinatorial problem via a convex, norm-constrained problem. We solve it using a dedicated Frank-Wolfe algorithm. To generate a summary with k sentences, the algorithm only needs to execute approximately k iterations, making it very efficient for a long document. We evaluate our approach against two other unsupervised methods using both lexical (standard) ROUGE scores, as well as semantic (embedding-based) ones. Our method achieves better results with both datasets and works especially well when combined with embeddings for highly paraphrased summaries.

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Proceedings of the The Fourth Widening Natural Language Processing Workshop
Rossana Cunha | Samira Shaikh | Erika Varis | Ryan Georgi | Alicia Tsai | Antonios Anastasopoulos | Khyathi Raghavi Chandu
Proceedings of the The Fourth Widening Natural Language Processing Workshop