Felix Yu


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InFillmore: Frame-Guided Language Generation with Bidirectional Context
Jiefu Ou | Nathaniel Weir | Anton Belyy | Felix Yu | Benjamin Van Durme
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

We propose a structured extension to bidirectional-context conditional language generation, or “infilling,” inspired by Frame Semantic theory. Guidance is provided through one of two approaches: (1) model fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive lexically constrained decoding that leverages frame semantic lexical units. Automatic and human evaluations confirm that frame-guided generation allows for explicit manipulation of intended infill semantics, with minimal loss in distinguishability from human-generated text. Our methods flexibly apply to a variety of use scenarios, and we provide an interactive web demo.


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Semantic Label Smoothing for Sequence to Sequence Problems
Michal Lukasik | Himanshu Jain | Aditya Menon | Seungyeon Kim | Srinadh Bhojanapalli | Felix Yu | Sanjiv Kumar
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Label smoothing has been shown to be an effective regularization strategy in classification, that prevents overfitting and helps in label de-noising. However, extending such methods directly to seq2seq settings, such as Machine Translation, is challenging: the large target output space of such problems makes it intractable to apply label smoothing over all possible outputs. Most existing approaches for seq2seq settings either do token level smoothing, or smooth over sequences generated by randomly substituting tokens in the target sequence. Unlike these works, in this paper, we propose a technique that smooths over well formed relevant sequences that not only have sufficient n-gram overlap with the target sequence, but are also semantically similar. Our method shows a consistent and significant improvement over the state-of-the-art techniques on different datasets.